Sitemap
A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
Posts
notes
Computer Vision
This course introduces Python and OpenCV for computer vision tasks. Learn image and video manipulation, object detection, and deep learning integration. Gain practical skills for real-world applications in robotics, automation, and more.
Differential Equations
Classification of ordinary differential equations, first and second order equations with applications, series solutions about regular points and singular points, special functions, Laplace transform.
Introduction to Digital Engineering
This course provides an introduction to data-driven engineering, covering essential programming elements for system implementation, programming methods aiding data analysis, data acquisition, verification, and visualization. Students will also be introduced to fundamental machine learning techniques and data-driven modeling, with practical applications across various engineering fields.
Introduction to Digital Engineering
This course provides an introduction to geospatial data analytics using Geographic Information Systems (GIS) and Remote Sensing technologies as foundational tools. This course is designed for individuals who are new to geospatial data processing and have a growing interest in data science.
Introduction to Probability and Statistics
This course covers key concepts in descriptive statistics and graphical representation. Topics include measures of central tendency and dispersion, elementary probability, and both discrete and continuous random variables. Students will learn about expectation, as well as the binomial, normal, and Student’s t-distributions. The course also addresses large and small sample inference and estimation, culminating in an understanding of the Central Limit Theorem.
Introduction to Python
This course covers fundamental programming concepts using Python, encompassing statements, conditionals, loops, functions, file I/O, debugging, data parsing, display techniques, and library usage.
Linear Algebra
This short course provides an essential foundation in linear algebra, covering key topics such as systems of equations and matrices, vectors, matrix representations, and determinants. Additionally, students will explore complex numbers and their polar form, gaining insights into eigenvalues and eigenvectors. Practical applications of these concepts in various fields will be highlighted, allowing students to grasp their relevance and importance in real-world scenarios.
Numerical Analysis
This course covers root-finding algorithms, interpolation, integration, differentiation, linear systems, and numerical ODE solutions for practical problem-solving. Gain vital skills for diverse applications in science and engineering.
publications
A Stable Numerical Solution of an Inverse Moving Boundary Problem of Heat Conduction Using Discrete Mollification Approach
Published in Advances in Mathematical Modeling, 2012
Abstract:
In this paper the application of marching scheme and mollification approach to solve a one dimensional inverse moving boundary problem for the heat equation is investigated. The problem is considered with noisy data. A regularization method based on marching scheme and discrete mollification approach is developed to solve the proposed problem and the stability and convergence of numerical solution is proved. To show the ability and efficiency of the proposed method, some numerical experiments are investigated.
Recommended citation: M. Garshasbi, H. Dastour, and M. Jalalvand. A Stable Numerical Solution of an Inverse Moving Boundary Problem of Heat Conduction Using Discrete Mollification Approach. Advances in Mathematical Modeling, 2(1):47-60, 2012. https://jamm.scu.ac.ir/?_action=articleInfo&article=10027&lang=fa&lang=en&lang=fa&lang=en
A stable numerical solution of a class of semi-linear Cauchy problem
Published in Journal of Advanced Research in Dynamical and Control Systems, 2012
Abstract:
In this paper, we are mainly concerned with the numerical solution of a class of non-well posed semi-linear Cauchy problems for the heat equation. The noisy data are given at the boundary. A stable numerical method based on mollification scheme and marching method is developed to solve the proposed problem. The error of this method is analyzed and some numerical examples are investigated.
Recommended citation: M. Garshasbi, P. Reihani, and H. Dastour. A stable numerical solution of a class of semi-linear cauchy problems. Journal of Advanced Research in Dynamical and Control Systems, 4:56-67, 2012. http://connection.ebscohost.com/c/articles/77840821/stable-numerical-solution-class-semi-linear-cauchy-problems
Proportional Factors Estimation in an IHCP
Published in Journal of Hyperstructures, 2014
Abstract:
In this paper, a numerical scheme was developed based on mollification method and space marching scheme for solving an inverse heat conduction problem. The proposed inverse problem contains the estimation of two unknown functions at the boundaries named proportional factors. The temperature and heat flux measurements in an interior point are considered as overspecified data with the presence of noise. Convergence and stability of the solution for the proposed method are analyzed. To support the numerical achievements, some numerical examples are considered and discussed.
Recommended citation: M. Garshasbi and H. Dastour. Proportional factors estimation in an IHCP. Journal of Hyperstructures, 3(1):53-67, 2014 http://www.jhs-uma.com/index.php/JHSMS/article/view/128
Estimation of unknown boundary functionsin an inverse heat conduction problem using a mollified marching scheme
Published in Numerical Algorithms, 2015
Abstract:
In this article, a one-dimensional inverse heat conduction problem with unknown nonlinear boundary conditions is studied. In many practical heat transfer situations, the heat transfer coefficient depends on the boundary temperature and the dependence has a complicated or unknown structure. For this reason highly nonlinear boundary conditions are imposed involving both the flux and the temperature. A numerical procedure based on the mollification method and the space marching scheme is developed to solve numerically the proposed inverse problem. The stability and convergence of numerical solutions are investigated and the numerical results are presented and discussed for some test problems.
Recommended citation: M. Garshasbi and H. Dastour. "Estimation of unknown boundary functions in an inverse heat conduction problem using a mollified marching scheme". Numerical Algorithms, 68(4):769-790, 2015. [IF: 2.1; IF Quartile: Q1] https://link.springer.com/article/10.1007/s11075-014-9871-7
A mollified marching solution of an inverse ablation-type moving boundary problem
Published in Computational and Applied Mathematics, 2016
Abstract:
This study investigates the application of marching scheme and mollification method to solve a one-dimensional inverse ablation-type moving boundary problem. The problem is considered with noisy data. A regularization method based on a marching scheme and discrete mollification approach is developed to solve the proposed problem and the stability and convergence of the numerical solution are proved. Some numerical experiments are presented to demonstrate the attractiveness and feasibility of the proposed approach. It is shown that the results are in good agreement with exact solutions.
Recommended citation: M. Garshasbi and H. Dastour. "A mollified marching solution of an inverse ablation-type moving boundary problem". Computational and Applied Mathematics, 35(1):61-73, 2016. [IF: 1.5; IF Quartile: Q1] https://link.springer.com/article/10.1007/s40314-014-0180-5
Efficient and accurate numerical simulation of acoustic wave propagation in a 2D heterogeneous media
Published in Applied Mathematics and Computation, 2018
Abstract:
In this paper, a compact fourth-order finite difference scheme is derived to solve the 2D acoustic wave equation in heterogenous media. The Padé approximation is used to obtain fourth-order accuracy in both temporal and spatial dimensions, and the alternating direction implicit (ADI) technique is used to reduce the computational cost. Due to the non-constant wave velocity, the conventional ADI method is hard to implement as the algebraic manipulation cannot be used here. A novel numerical strategy is proposed in this work so that the compact scheme still maintains fourth-order accuracy in time and space. The fourth-order convergence order was firstly proved by theoretical error analysis, then was confirmed by numerical examples. It was shown that the proposed method is conditionally stable with a Courant–Friedrichs–Lewy (CFL) condition that is comparable to other existing finite difference schemes. Several numerical examples were solved to demonstrate the efficiency and accuracy of the new algorithm.
Recommended citation: W. Liao, P. Yong, H. Dastour, and J. Huang. "Efficient and accurate numerical simulation of acoustic wave propagation in a 2D heterogeneous media". Applied Mathematics and Computation 321:385-400, 2018. [IF: 2.1; IF Quartile: Q1] https://www.sciencedirect.com/science/article/abs/pii/S0096300317307610
A fourth-order optimal finite difference scheme for the Helmholtz equation with PML
Published in Computers & Mathematics with Applications, 2019
Abstract:
In this paper, 17-point and 25-point finite difference (FD) schemes for the Helmholtz equation with perfectly matched layer (PML) in the two-dimensional domain are presented. It is shown that the 17-point FD scheme is inconsistent in the presence of PML; however, the 25-point FD scheme is pointwise consistent. An error analysis for the numerical approximation of the exact wavenumber is also presented. We present the global and refined 25-point finite difference schemes based on minimizing the numerical dispersion. Numerical experiments are given to illustrate the improvement of the accuracy and the reduction of the numerical dispersion.
Recommended citation: Dastour H. and Liao, W. (2019). A fourth-order optimal finite difference scheme for the Helmholtz equation with PML. Computers & Mathematics with Applications, 78(6):2147--2165. [IF: 2.9; IF Quartile: Q1] https://www.sciencedirect.com/science/article/pii/S0898122119302676
Optimal Finite Difference Schemes for the Helmholtz Equation with PML
Published in University of Calgary, 2019
Abstract:
An efficient and accurate numerical scheme for solving the seismic wave equations is a key part in seismic wave propagation modeling. The pollution effect of high wavenumbers (the accuracy of the numerical results often deteriorates as the wavenumber increases) plays a critical role in the accuracy of these numerical schemes and it is inevitable in two and three dimensional Helmholtz equations. Optimal finite difference methods can offer a remedy to this problem; however, the numerical solution to a multi-dimensional Helmholtz equation can be troublesome when the perfectly matched layer (PML) boundary condition is implemented. This study develops a number of optimal finite difference schemes for solving the Helmholtz equation in the presence of PML. In doing so, we implement two common strategies, derivative-weighting and point-weighting strategies, for constructing these schemes. Furthermore, a challenge for developing such methods is being consistent with the Helmholtz equation with PML. Thus, analytical and numerical proofs are provided to show the consistency of the schemes. Moreover, for each developed optimal finite difference method, error analysis for the numerical approximation of the exact wavenumber is provided. Based on minimizing the numerical dispersion, some optimal parameters strategies for each optimal finite difference schemes are recommended. Furthermore, several examples are provided to illustrate the accuracy and effectiveness of the new methods in reducing numerical dispersion.
Recommended citation: H. Dastour. (2019). Optimal Finite Difference Schemes for the Helmholtz Equation with PML (Unpublished doctoral thesis). University of Calgary, Calgary, AB. [PhD, Thesis] https://prism.ucalgary.ca/handle/1880/111362
An optimal 13-point finite difference scheme for a 2D Helmholtz equation with a perfectly matched layer boundary condition
Published in Numerical Algorithms, 2020
Abstract:
Efficient and accurate numerical schemes for solving the Helmholtz equation are critical to the success of various wave propagation–related inverse problems, for instance, the full-waveform inversion problem. However, the numerical solution to a multi-dimensional Helmholtz equation is notoriously difficult, especially when a perfectly matched layer (PML) boundary condition is incorporated. In this paper, an optimal 13-point finite difference scheme for the Helmholtz equation with a PML in the two-dimensional domain is presented. An error analysis for the numerical approximation of the exact wavenumber is provided. Based on error analysis, the optimal 13-point finite difference scheme is developed so that the numerical dispersion is minimized. Two practical strategies for selecting optimal parameters are presented. Several numerical examples are solved by the new method to illustrate its accuracy and effectiveness in reducing numerical dispersion.
Recommended citation: H. Dastour and W. Liao. "An optimal 13-point finite difference scheme for a 2D Helmholtz equation with a perfectly matched layer boundary condition". Numerical Algorithms, 86(3):1109–1141, 2021. [IF: 2.1; IF Quartile: Q1] https://link.springer.com/article/10.1007/s11075-020-00926-5
A generalized optimal fourth-order finite difference scheme for a 2D Helmholtz equation with the perfectly matched layer boundary condition
Published in Journal of Computational and Applied Mathematics, 2021
Abstract:
A crucial part of successful wave propagation related inverse problems is an efficient and accurate numerical scheme for solving the seismic wave equations. In particular, the numerical solution to a multi-dimensional Helmholtz equation can be troublesome when the perfectly matched layer (PML) boundary condition is implemented. In this paper, we present a general approach for constructing fourth-order finite difference schemes for the Helmholtz equation with PML in the two-dimensional domain based on point-weighting strategy. Particularly, we develop two optimal fourth-order finite difference schemes, optimal point-weighting 25p and optimal point-weighting 17p. It is shown that the two schemes are consistent with the Helmholtz equation with PML. Moreover, an error analysis for the numerical approximation of the exact wavenumber is provided. Based on minimizing the numerical dispersion, we implement the refined choice strategy for selecting optimal parameters and present refined point-weighting 25p and refined point-weighting 17p finite difference schemes. Furthermore, three numerical examples are provided to illustrate the accuracy and effectiveness of the new methods in reducing numerical dispersion.
Recommended citation: H. Dastour and W. Liao. "A generalized optimal fourth-order finite difference scheme for a 2D Helmholtz equation with the perfectly matched layer boundary condition". Journal of Computational and Applied Mathematics, 113544, 2021. [IF: 2.4; IF Quartile: Q1] https://www.sciencedirect.com/science/article/abs/pii/S0377042721001631
A Combined Approach for Monitoring Monthly Surface Water/Ice Dynamics of Lesser Slave Lake Via Earth Observation Data
Published in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022
Abstract:
Surface water/ice dynamic monitoring is crucial for many purposes, such as water resource management, agriculture, climate change, drought, and flood forecasting. New advances in remote sensing satellite data have made it possible to monitor the surface water/ice dynamics both spatially and temporally. However, there are many challenges when using these data, such as the availability of valid imagery, cloud contamination issues for Landsat-8, and sensitivity of Sentinel-1 C-band to wind speed, topography, and others. A combined methodology using Landsat-8 and Sentinel-1 Synthetic Aperture Radar (SAR) data was proposed to create monthly change maps at 30 m spatial resolution for the Lesser Slave Lake in Alberta, Canada, for the period 2017-2020. The potentials of multi-spectral indices for Landsat-8, such as the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Modified NDWI (MNDWI) as well as the Sentinel-1 SAR backscattering coefficients (VV-VH) and Normalized Difference Polarized Index (NDPI) for separating water/ice from the land were investigated. The results obtained from satellite data with historical discharge and water level measurements for the lake were compared. Furthermore, the results show that the MNDWI and VH are the most effective indices for creating the change maps. The overall accuracies achieved for MNDWI and VH are 92.10% and 68.86% for cold months and 99.88% and 98.49% for warm months, respectively.
Recommended citation: H. Dastour, E. Ghaderpour and Q. K. Hassan, "A Combined Approach for Monitoring Monthly Surface Water/Ice Dynamics of Lesser Slave Lake Via Earth Observation Data," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15:6402--6417, 2022. [IF: 5.5; IF Quartile: Q1] https://ieeexplore.ieee.org/document/9850369
Wavelet-based spatiotemporal analyses of climate and vegetation for the Athabasca river basin in Canada
Published in International Journal of Applied Earth Observation and Geoinformation, 2022
Abstract:
Monitoring spatiotemporal changes in climate and vegetation coverage are crucial for various purposes, including water, hazard, and agricultural management. Climate has an impact on vegetation, however, studying their relationship is challenging. We implemented the Least-Squares Wavelet (LSWAVE) software for investigating trend, coherency, and time lag estimation between climate and vegetation time series. We utilized Normalized Difference Vegetation Index (NDVI) time series provided by the Terra satellite and hybrid climate time series. We found that the seasonal cycles of climate and NDVI are coherent with time delay. For the entire Athabasca River Basin (ARB), the most coherent component was the annual cycle with 84% annual coherency between vegetation and temperature and 46% between vegetation and precipitation. The annual cycles of temperature and precipitation led the ones in vegetation by about two and three weeks, respectively. Relatively lower coherency was observed in the mountainous region (upper ARB) and higher coherency in the middle ARB. From the cross-spectrograms, a clear time delay pattern was observed between the annual cycles of climate and vegetation since 2000 but not for other high-frequency seasonal cycles. The results also highlighted the advantages of LSWAVE algorithms over traditional algorithms, such as linear regression and correlation. Furthermore, we analyzed the annual land use and land cover data provided by the Terra and Aqua satellites and discussed their linkage with the climate and NDVI results.
Recommended citation: H. Dastour, E. Ghaderpour, M. S. Zaghloul, B. Farjad, A. Gupta, H. Eum, G. Achari, and Q. K. Hassan, "Wavelet-based spatiotemporal analyses of climate and vegetation for the Athabasca river basin in Canada" International Journal of Applied Earth Observation and Geoinformation, 114:103044, 2022. [IF: 7.5; IF Quartile: Q1] https://www.sciencedirect.com/science/article/pii/S1569843222002321
Long Term Trend Analysis of River Flow and Climate in Northern Canada
Published in Hydrology, 2022
Abstract:
Changes in water resources within basins can significantly impact ecosystems, agriculture, and biodiversity, among others. Basins in northern Canada have a cold climate, and the recent changes in climate can have a profound impact on water resources in these basins. Therefore, it is crucial to study long term trends in water flow as well as their influential factors, such as temperature and precipitation. This study focused on analyzing long term trends in water flow across the Athabasca River Basin (ARB) and Peace River Basin (PRB). Long term trends in temperature and precipitation within these basins were also studied. Water flow data from 18 hydrometric stations provided by Water Survey of Canada were analyzed using the Mann-Kendall test and Sen’s slope. In addition, hybrid climate data provided by Alberta Environment and Parks at approximately 10 km spatial resolution were analyzed for the ARB and its surrounding regions during 1950–2019. Trend analysis was performed on the water flow data on monthly, seasonal, and annual scales, and the results were cross-checked with trends in temperature and precipitation and land use and land cover data. The overall temperature across the basins has been increasing since 1950, while precipitation showed an insignificant decrease during this period. Winter water flow in the upper ARB has been slowly and steadily increasing since 1956 because of the rising temperatures and the subsequent slow melting of snowpacks/glaciers. The warm season flows in the middle and lower subregions declined up to 1981, then started to show an increasing trend. The middle and lower ARB exhibited a rapid increase in warm-season water flow since 2015. A similar trend change was also observed in the PRB. The gradual increase in water flow observed in the recent decades may continue by the mid-century, which is beneficial for agriculture, forestry, fishery, and industry. However, climate and land cover changes may alter the trend of water flow in the future; therefore, it is important to have a proper management plan for water usage in the next decades.
Recommended citation: M. S. Zaghloul, E. Ghaderpour, H. Dastour, B. Farjad, A. Gupta, H. Eum, G. Achari, and Q. K. Hassan, "Long Term Trend Analysis of River Flow and Climate in Northern Canada", Hydrology, 9(11):197, 2022. [IF: 3.2; IF Quartile: Q2] https://www.mdpi.com/2306-5338/9/11/197
A Machine-Learning Framework for Modeling and Predicting Monthly Streamflow Time Series
Published in Hydrology, 2023
Abstract:
Having a complete hydrological time series is crucial for water-resources management and modeling. However, this can pose a challenge in data-scarce environments where data gaps are widespread. In such situations, recurring data gaps can lead to unfavorable outcomes such as loss of critical information, ineffective model calibration, inaccurate timing of peak flows, and biased statistical analysis in various applications. Despite its importance, predicting monthly streamflow can be a complex task due to its connection to random dynamics and uncertain phenomena, posing significant challenges. This study introduces an ensemble machine-learning regression framework for modeling and predicting monthly streamflow time series with a high degree of accuracy. The framework utilizes historical data from multiple monthly streamflow datasets in the same region to predict missing monthly streamflow data. The framework selects the best features from all available gap-free monthly streamflow time-series combinations and identifies the optimal model from a pool of 12 machine-learning models, including random forest regression, gradient boosting regression, and extra trees regressor, among others. The model selection is based on cross-validation train-and-test set scores, as well as the coefficient of determination. We conducted modeling on 26 monthly streamflow time series and found that the gradient boosting regressor with bagging regressor produced the highest accuracy in 7 of the 26 instances. Across all instances, the models using this method exhibited an overall accuracy range of 0.9737 to 0.9968. Additionally, the use of either a bagging regressor or an AdaBoost regressor improved both the tree-based and gradient-based models, resulting in these methods accounting for nearly 80% of the best models. Between January 1960 and December 2021, an average of 40% of the monthly streamflow data was missing for each of the 26 stations. Notably, two crucial stations located in the economically significant lower Athabasca Basin River in Alberta province, Canada, had approximately 70% of their monthly streamflow data missing. To address this issue, we employed our framework to accurately extend the missing data for all 26 stations. These accurate extensions also allow for further analysis, including grouping stations with similar monthly streamflow behavior using Pearson correlation.
Recommended citation: H. Dastour and Q. K. Hassan, "A Machine-Learning Framework for Modeling and Predicting Monthly Streamflow Time Series", Hydrology, 10(4):95, 2023. [IF: 3.2; IF Quartile: Q2] https://www.mdpi.com/2306-5338/10/4/95
A Robust Regime Shift Change Detection Algorithm for Water-Flow Dynamics
Published in Water, 2023
Abstract:
Stream and river monitoring have an influential role in agriculture, the fishing industry, land surveillance, the oil and gas industry, etc. Recognizing sudden changes in the behavior of streamflow could also provide tremendous insight for decision-making and administration purposes. The primary purpose of this study is to offer a new robust Regime Shift Change Detection (RSCD) algorithm which can identify periods and regime changes without any assumptions regarding the length of these periods. A regime shift algorithm using two different refined method approaches is proposed in this article. The RSCD with Relative Difference (RSCD-RD) and RSCD with Growth Rate (RSCD-GR) are the two main specializations of this regime shift algorithm. We compared these two specializations on train and test datasets and commented on the advantages and each specialization. RSCD-GR and RSCD-RD were equally effective in detecting regime changes when thresholds were pinpointed for each station and season. However, RSCD-RD outperformed RSCD-GR when general thresholds were used for cold and warm months. A strength of RSCD-GR is the ability to investigate newly observed data separately, while RSCD-RD may require re-investigation of historical data in some cases. A regime change was detected in the monthly streamflow data of the Athabasca River at Athabasca (07BE001) in May 2007, while no such change was observed in the monthly streamflow data of the Athabasca River below Fort McMurray (07DA001). The discrepancy could be attributed to factors such as the clarity of the river water from Saskatchewan or the utilization of industrial water. Additional investigation might be required to determine the underlying causes.
Recommended citation: H. Dastour, A. Gupta, G. Achari, and Q. K. Hassan, "A Robust Regime Shift Change Detection Algorithm for Water-Flow Dynamics," Water, 15(8):1571, 2023. [IF: 3.4; IF Quartile: Q2] https://www.mdpi.com/2073-4441/15/8/1571
A Comparison of Deep Transfer Learning Methods for Land Use and Land Cover Classification
Published in Sustainability, 2023
Abstract:
The pace of Land Use/Land Cover (LULC) change has accelerated due to population growth, industrialization, and economic development. To understand and analyze this transformation, it is essential to examine changes in LULC meticulously. LULC classification is a fundamental and complex task that plays a significant role in farming decision making and urban planning for long-term development in the earth observation system. Recent advances in deep learning, transfer learning, and remote sensing technology have simplified the LULC classification problem. Deep transfer learning is particularly useful for addressing the issue of insufficient training data because it reduces the need for equally distributed data. In this study, thirty-nine deep transfer learning models were systematically evaluated alongside multiple deep transfer learning models for LULC classification using a consistent set of criteria. Our experiments will be conducted under controlled conditions to provide valuable insights for future research on LULC classification using deep transfer learning models. Among our models, ResNet50, EfficientNetV2B0, and ResNet152 were the top performers in terms of kappa and accuracy scores. ResNet152 required three times longer training time than EfficientNetV2B0 on our test computer, while ResNet50 took roughly twice as long. ResNet50 achieved an overall f1-score of 0.967 on the test set, with the Highway class having the lowest score and the Sea Lake class having the highest.
Recommended citation: H. Dastour and Q. K. Hassan, "Comparison of Deep Transfer Learning Methods for Land Use and Land Cover Classification", Sustainability, 15(10):7854, 2023. [IF: 3.9; IF Quartile: Q2] https://www.mdpi.com/2071-1050/15/10/7854
Least-Squares Triple Cross-Wavelet and Multivariate Regression Analyses of Climate and River Flow in Athabasca River Basin
Published in Journal of Hydrometeorology, 2023
Abstract:
River flow monitoring is a critical task for land management, agriculture, fishery, industry, and others. Herein, a robust least-squares triple cross-wavelet analysis is proposed to investigate possible relationships between river flow, temperature, and precipitation in the time-frequency domain. The Athabasca River Basin (ARB) in Canada is selected as a case study to investigate such relationships. The historical climate and river flow datasets since 1950 for three homogeneous subregions of ARB were analyzed using a traditional multivariate regression model and the proposed wavelet analysis. The highest Pearson correlation (0.87) was estimated between all the monthly averaged river flow, temperature, and accumulated precipitation for the subregion between Hinton and Athabasca. The highest and lowest correlations between climate and river flow were found to be during the open warm season and cold season, respectively. Particularly, the highest correlations between temperature, precipitation, and river flow were in May (0.78) for Hinton, July (0.54) for Athabasca, and September (0.44) for Fort McMurray. The new wavelet analysis revealed significant coherency between annual cycles of climate and river flow for the three subregions, with the highest of 33.7% for Fort McMurray and the lowest of 4.7% for Hinton with more coherency since 1991. The phase delay analysis showed that annual and semiannual cycles of precipitation generally led the ones in river flow by a few weeks mainly for upper and middle ARB since 1991. The climate and river flow anomalies were also demonstrated using the baseline period 1961-1990, showing a significant increase in temperature and decrease in precipitation since 1991 for all the three subregions. Unlike the multivariate regression, the proposed wavelet method can analyze any hydrometeorological time series in the time-frequency domain without any need for resampling, interpolation, or gap filling.
Recommended citation: E. Ghaderpour, M. S. Zaghloul, H. Dastour, A. Gupta, G. Achari, and Q. K. Hassan, "Least-Squares Triple Cross-Wavelet and Multivariate Regression Analyses of Climate and River Flow in Athabasca River Basin", Journal of Hydrometeorology, https://doi.org/10.1175/JHM-D-23-0013.1. [IF: 3.8; IF Quartile: Q2] https://doi.org/10.1175/JHM-D-23-0013.1
Utilizing MODIS remote sensing and integrated data for forest fire spread modeling in the Southwest region of Canada
Published in Environmental Research Communications, 2024
Abstract:
Accurate prediction of fire spread is considered crucial for facilitating effective fire management, enabling proactive planning, and efficient allocation of resources. This study places its focus on wildfires in two regions of Alberta, Fort McMurray and Slave Lake, in Southwest Canada. For the simulation of wildfire spread, an adapted fire propagation model was employed, incorporating MODIS datasets such as land surface temperature, land cover, land use, and integrated climate data. The pixels were classified as burned or unburned in relation to the 2011 Slave Lake wildfire and the initial 16 days of the 2016 Fort McMurray wildfire, utilizing defined starting points and the aforementioned specified datasets. The simulation for the 2011 Slave Lake wildfire achieved an weighted average precision, recall, and f1-scores of 0.989, 0.986, and 0.987, respectively. Additionally, macro-averaged scores across these three phases were 0.735, 0.829, and 0.774 for precision, recall, and F1-scores, respectively. The simulation of the 2016 Fort McMurray wildfire introduced a phased analysis, dividing the initial 16 days into three distinct periods. This approach led to average precision, recall, and f1-scores of 0.958, 0.933, and 0.942 across these phases. Additionally, macro-averaged scores across these three phases were 0.681, 0.772, and 0.710 for precision, recall, and F1-scores, respectively. The strategy of segmenting simulations into phases may enhance adaptability to dynamic factors like weather conditions and firefighting strategies.
Recommended citation: Dastour, H. and Hassan, Q. K. (2024). Utilizing MODIS remote sensing and integrated data for forest fire spread modeling in the Southwest region of Canada. Environmental Research Communications, 6(2):025007. [IF: 2.9; IF Quartile: Q3] https://doi.org/10.1088/2515-7620/ad248f
Analysis of forest fire patterns and their relationship with climate variables in Albertas natural subregions
Published in Ecological Informatics, 2024
Abstract:
Forest fires are significant ecological and environmental phenomena that can be influenced by various climatic factors. This study used fire point records from the Canadian National Fire Database (CNFDB) and interpolated climate data, which include the minimum and maximum air temperature, the average relative humidity, and the precipitation for each subregion of Alberta, Canada, to analyze the patterns and relationships of forest fires and climate variables using trend analysis and anomaly detection methods. The trend analysis was based on the Mann-Kendall test and Sen's slope, which were used to detect the presence and magnitude of monotonic trends in the monthly aggregated data from 1955 to 2022. The anomaly detection is based on the RobustSTL method, which was used to decompose the monthly aggregated data into seasonal, trend, and remainder components, and to identify the periods of significantly high or low values for each component. Most subregions showed a significant increase in temperature and a decrease in humidity, indicating a warming and drying trend due to climate change. Precipitation change was variable across subregions. Human-caused or prescribed forest fires increased significantly in Central Mixedwood, Dry Mixedwood, Lower Foothills, Montane, and Upper Foothills, while lightning-caused forest fires had mixed trends in Dry Mixedwood, Upper Foothills, Central Mixedwood, and Lower Boreal Highlands. The fire occurrence and source were affected by the climate variables in different ways across subregions. The fire occurrence in the Athabasca Plain subregion changed with the air temperature. It was low when the temperature was significantly low, and it was high due to lightning when the temperature was significantly high. The Central Mixedwood subregion had three peaks of lightning-induced fires when the relative humidity was significantly low, and several peaks of fires from human activities and lightning when the air temperature was significantly high. The study also revealed some other interesting patterns and relationships between the climate and fire variables and the forest fire distribution in different subregions, which may help to understand and manage the climate and fire interactions and their implications for forest fire understanding and management in the context of climate change.
Recommended citation: Dastour, H., Ahmed M. Razu and Hassan, Q. K. (2024). Analysis of forest fire patterns and their relationship with climate variables in Albertas natural subregions. Ecological Informatics. [IF: 5.1; IF Quartile: Q1] https://doi.org/10.1007/s41748-024-00384-2
Quantifying the Influence of Climate Variables on Vegetation Through Remote Sensing and Multi-dimensional Data Analysis
Published in Earth Systems and Environment, 2024
Abstract:
The exchange of energy, water, and carbon between the land surface and the atmosphere is critically influenced by vegetation. However, vegetation cover has been changing due to climate variability and human activities, which can affect ecosystems, biodiversity, land management, and human well-being. This study aimed to examine the impact of climate factors on different vegetation types in Alberta, Canada. Remote sensing and climate data from various sites were collected and analyzed using spatial and temporal correlation analysis methods. During the study period of 2001–2022, this study revealed that temporal dynamics between NDVI and climate variables were assessed using NCC analyses on various land cover types. The results revealed a lead of 3.5–4 months of NDVI over the Relative Humidity Average. Additionally, LST-Day demonstrated a lead of two weeks over NDVI, while LST-Night exhibited minimal lag with NDVI, except in regions characterized by sparse vegetation. Furthermore, NDVI displayed a lag of 2–4 weeks behind Precipitation. Land cover dynamics in Alberta from 2001 to 2022 reveal significant trends. Cropland areas, covering nearly 20%, consistently increased with increasing relative humidity, except for deviations in 2001 and 2002. Evergreen Needleleaf forests, constituting around 14.5%, exhibited an upward trend correlated with increased precipitation. Grasslands, comprising 13.8%, showed diminishing coverage despite rising humidity and precipitation. Woody Savannas, accounting for approximately 29%, displayed increased coverage in 2006 but exhibited a declining trend over the study period. These trends highlight the complex interplay between land cover changes and climatic factors in Alberta. The quantification of the influence of climate variables on NDVI revealed the pivotal roles of LST-Day and LST-Night, with average feature importance values of 37.42% and 40.35%, respectively, across all land cover types.
Recommended citation: Dastour, H. and Hassan, Q. K. (2024). Quantifying the Influence of Climate Variables on Vegetation Through Remote Sensing and Multi-dimensional Data Analysis. Earth Systems and Environment. [IF: 7.2; IF Quartile: Q1] https://doi.org/10.1007/s41748-024-00384-2
research
Climate-Vegetation Dynamics
Studies focusing on the interactions between climate patterns and vegetation, examining how climate change affects plant growth, distribution, and biodiversity. These studies often use remote sensing and field data to understand the impact of climate on vegetation dynamics. 
Climate Change and Forest Fire Dynamics
Research in this area explores the relationship between climate change and forest fires, investigating how changes in climate variables such as temperature and precipitation influence the frequency, intensity, and distribution of wildfires. 
Forest Fire Management Research
Exploring innovative fire management techniques and remote sensing to enhance wildfire response and management.
Hydrological Climate Effects
This field examines the effects of climate change on the hydrological cycle, including precipitation patterns, river flows, and groundwater levels. It aims to understand how changing climates affect water resources and flood risks. 
Land Use and Cover Studies
LULC classification involves categorizing the Earth’s surface into various types based on observed land cover, such as forests, urban areas, or water bodies. This research is crucial for environmental monitoring and urban planning. 
Numerical Computational Analysis
This area involves the development and application of numerical methods and algorithms to solve mathematical problems that are typically too complex for analytical solutions. It’s essential in engineering, physics, and economics for modeling and simulations. 
Water Dynamics and Monitoring
Research here focuses on the observation and analysis of water bodies’ behavior and characteristics, including their distribution, flow, and quality. It often involves the use of satellite imagery and sensors for real-time monitoring. 
talks
Alberta Mathematics Dialogue (AMD) 2016: A mollified marching solution of an inverse degenerate diffusion problem in petroleum reservoir
Published:
Abstract: This study deals with the numerical solution of a nonlinear inverse degenerate diffusion problem in petroleum reservoirs. A computational procedure based on the discrete mollification method and the space marching scheme is developed to solve the proposed inverse problem. The stability and convergence of the numerical solution are proved. To demonstrate the effectiveness and accuracy of the new method, two numerical examples are solved. Numerical results show that the new method is effective in solving inverse problems when noisy data is used.
2016 Canadian Mathematical Society (CMS) Summer Meeting: A numerical estimation approach for an inverse heat conduction problem
Published:
Abstract: In this study, a one-dimensional inverse heat conduction problem with unknown nonlinear boundary conditions is investigated. Nonlinear boundary conditions are imposed involving both the flux and the temperature. The heat transfer coefficient depends on the boundary temperature, and the dependence has a complicated or unknown structure. A numerical algorithm is generated based on a space marching scheme and the mollification method, and its stability and convergence are analyzed. Two numerical examples are tested to illustrate the efficiency of the proposed algorithm.
2016 Canadian Mathematical Society (CMS) Summer Meeting: A Computational Method for Solving an Inverse Heat Conduction Problem
Published:
Conference page: http://www.caims2016.caims.ca/welcome2016.html
Canadian Society of Applied and Industrial Mathematics (CAIMS 2016): A stabilized marching scheme for solving the inverse problem of degenerate diffusion model
Published:
Abstract: In this study, a one-dimensional inverse heat conduction problem with unknown nonlinear boundary conditions is investigated. Nonlinear boundary conditions are imposed involving both the flux and the temperature. The heat transfer coefficient depends on the boundary temperature, and the dependence has a complicated or unknown structure. A numerical algorithm is generated based on a space marching scheme and the mollification method, and its stability and convergence are analyzed. Two numerical examples are tested to illustrate the efficiency of the proposed algorithm.
Eric Milner Colloquium: Computational Methods for Solving Wave Equation Inverse Problem
Published:
Every year, the Eric Milner Graduate Scholarship recipient is invited to present a colloquium lecture.
Canadian Water Resources Association (CWRA) 2023: A Machine-Learning Framework for Modeling and Reconstructing Historical Monthly Streamflow Time Series
Published:
Abstract: This study focuses on the challenge of predicting monthly streamflow in data-scarce environments, where recurring data gaps can lead to unfavorable outcomes such as loss of critical information, ineffective model calibration, inaccurate timing of peak flows, and biased statistical analysis in various applications. To overcome this challenge, an ensemble machine-learning regression framework is introduced, which utilizes historical data from multiple monthly streamflow datasets in the same region to predict missing monthly streamflow data. The framework selects the best features from all available gap-free monthly streamflow time-series combinations and identifies the optimal model from a pool of 12 machine-learning models. The study shows that the gradient boosting regressor with bagging regressor produced the highest accuracy in 7 of the 26 instances, and the models using this method exhibited an overall accuracy range of 0.9737 to 0.9968. The framework was employed to accurately extend the missing data for all 26 stations, including two crucial stations located in the economically significant lower Athabasca Basin River in Alberta province, Canada, which had approximately 70% of their monthly streamflow data missing. The accurate extensions allow for further analysis, including grouping stations with similar monthly streamflow behavior using Pearson correlation.
Modeling Forest Fire Spread in Southwestern Canada Using MODIS Remote Sensing and Integrated Data
Published:
Abstract: This research highlights the importance of accurately predicting forest fire spread to enhance fire management, proactive planning, and the judicious use of resources. The study is centered on wildfires in Alberta’s Fort McMurray and Slave Lake areas. It utilized a modified fire propagation model that incorporated MODIS data, including land surface temperature, land cover/use, and climate information. Pixels were categorized as burned or unburned based on the 2011 Slave Lake fire and the first 16 days of the 2016 Fort McMurray fire, using specific starting points and datasets. The 2011 Slave Lake fire simulation was accurate, with weighted average precision, recall, and F1-scores of 0.989, 0.986, and 0.987 respectively. Additionally, macro-averaged scores were 0.735 for precision, 0.829 for recall, and 0.774 for F1-score. For the 2016 Fort McMurray fire, the simulation employed a phased analysis, dividing the initial 16 days into three distinct periods. This method resulted in average precision, recall, and F1 scores of 0.958, 0.933, and 0.942. Macro-averaged scores for these periods were 0.681 for precision, 0.772 for recall, and 0.710 for F1-score. Segmenting the simulations into phases may improve the model’s adaptability to dynamic factors such as changing weather conditions and varying firefighting strategies. The study’s approach could significantly bolster wildfire management practices.
teaching
MATH 275 - Calculus for Engineers and Scientists (Fall 2017)
Undergraduate course, University of Calgary, Department of Mathematics and Statistics, 2017
This course offers an in-depth study of single-variable differential and integral calculus, emphasizing its practical uses. The differentiation portion addresses key concepts like the rules of derivatives, the mean value theorem, optimization methods, and curve sketching, along with their applications in real-life scenarios. The integral calculus part introduces the fundamental theorem of calculus, diverse integration strategies, improper integrals, and the computation of areas in plane figures. Additionally, the course investigates infinite series, encompassing power series, Taylor’s theorem, and the Taylor series.
MATH 211 - Linear Methods I (Summer 2018)
Undergraduate course, University of Calgary, Department of Mathematics and Statistics, 2018
An introduction to systems of linear equations, vectors in Euclidean space, and matrix algebra. Additional topics include linear transformations, determinants, complex numbers, eigenvalues, and applications.
MATH 211 - Linear Methods I (Spring 2019)
Undergraduate course, University of Calgary, Department of Mathematics and Statistics, 2019
An introduction to systems of linear equations, vectors in Euclidean space, and matrix algebra. Additional topics include linear transformations, determinants, complex numbers, eigenvalues, and applications.
MATH 1510Y - Calculus for Management and Social Sciences (Fall 2021)
Undergraduate course, University of Lethbridge (Calgary Campus), Calgary, AB, Canada, 2021
This course examines a range of subjects on elementary functions, with an emphasis on differentiation methods like the chain rule and product rule. It further investigates issues of extrema and integration. The course highlights the real-world utility of these mathematical principles in fields such as management, humanities, and social sciences. Students will be equipped to utilize these mathematical instruments to evaluate real-life situations and make well-informed choices across diverse disciplines.
MATH 1203 - Linear Algebra for Scientists and Engineers (Fall 2022)
Undergraduate course, Mount Royal University, Department of Mathematics and Computing, 2022
This course provides a foundational journey through linear algebra tailored for students in the sciences. It encompasses a variety of subjects such as vector and matrix algebra, systems of linear equations, determinants, linear transformations, polar coordinates, and complex numbers. The course delves deeply into the practical uses of linear algebra in the physical sciences, with a special focus on the roles of eigenvalues and eigenvectors.
MATH 3101 - Numerical Analysis (Fall 2022)
Undergraduate course, Mount Royal University, Department of Mathematics and Computing, 2022
This course is centered on the theoretical and practical aspects of numerical computation techniques aimed at addressing real-world issues. It includes a spectrum of methodologies such as resolving nonlinear and simultaneous linear equations, curve fitting, eigenvalue issues, interpolation and approximation, as well as numerical differentiation and integration. Additionally, it involves tackling ordinary and partial differential equations. The lab segment of the course allows students to implement these techniques on basic problems and solve more complex problems through computerized methods.
MATH 2233 - Statistics for Biological Sciences (Winter 2023)
Undergraduate course, Mount Royal University, Department of Mathematics and Computing, 2023
This course offers an extensive study of descriptive statistics, probability theory, and structured methods for inferential statistics. It covers essential subjects including inference on population means and proportions, understanding of regression and correlation, execution of chi-square tests, analysis of variance, and application of non-parametric statistics. Moreover, students will gain practical experience by applying these statistical techniques to actual problems in the biological and health sciences.
MATH 2234 - Concepts of Mathematical Statistics (Winter 2023)
Undergraduate course, Mount Royal University, Department of Mathematics and Computing, 2023
This course delves into descriptive statistics and provides a primer on probability theory. The program also offers an in-depth examination of inferential statistics. Essential subjects covered include drawing conclusions about the means and proportions of single or dual populations, grasping linear regression and correlation, executing chi-square tests, and carrying out variance analysis. Students will employ statistical software in lab sessions to apply these principles to actual data sets.
MATH 2333 - Statistics for Life Sciences (Spring 2023)
Undergraduate course, Mount Royal University, Department of Mathematics and Computing, 2023
This course introduces students to exploratory data analysis, offering a concise overview of probability theory and inferential statistics. Students will thoroughly investigate key topics such as inferring population means and proportions, performing chi-square tests, and studying regression and correlation. The course also emphasizes the practical use of these statistical techniques, especially their importance in medical and health sciences.
ENGG 680 - Introduction to Digital Engineering (Fall 2023)
Graduate course, University of Calgary, Department of Electrical and Computer Engineering, 2023
This course covers the fundamental elements of digital engineering systems and basic programming structures for implementation. It teaches programming strategies to aid data analysis, including data acquisition and cleansing, validation, and visualization. The course introduces fundamental machine learning methods and data-centric modeling, with practical examples from various engineering fields.
ENEN 693 - Life Cycle Assessment (Winter 2024)
Graduate course, University of Calgary, Department of Civil Engineering, 2024
This course delves into the fundamentals of life cycle assessment (LCA), a critical approach for evaluating the environmental and economic repercussions throughout the lifespan of a product—from the initial extraction of raw materials to the final disposal of residuals. Students will engage in a thorough review and assessment of various LCA tools and frameworks, including process-based, input-output, and hybrid models. The curriculum aims to equip learners with the ability to discern the relative strengths of different methodologies for interpreting and quantifying environmental impacts. Additionally, the course provides real-world case studies demonstrating the application of LCA in environmental engineering and the energy sector, highlighting its significance in sustainable development.
ENSF 444 - Machine Learning Systems (Winter 2024)
Undergraduate course, University of Calgary, Department of Electrical and Computer Engineering, 2024
This course offers a comprehensive exploration of data science techniques tailored for engineering contexts. It covers the essential processes of extracting, cleaning, and visualizing data to make informed decisions based on engineering datasets. The course also delves into the fundamental numerical computation techniques that form the foundation of machine learning algorithms. Students will gain a solid understanding of both supervised and unsupervised learning algorithms, crucial for predictive modeling and pattern discovery in data.