Majid Niazkar's research interests are Hydrology (specifically: Flood modeling and routing), Open channel hydraulics, Water resources management, Soft-computing techniques (ANN and genetic programming), Parameter estimation, Bed roughness prediction, Water Distribution Networks, Optimization, Numerical modeling, River engineering and Sediment transport, Groundwater, Assessing the impacts of climate change, and Interdisciplinary topics like AI in medicine and the COVID-19 outbreak.
Environment, Development and Sustainability, Apr 21, 2023
Sediment ratings supply an important input to the design of water resources projects. Nevertheles... more Sediment ratings supply an important input to the design of water resources projects. Nevertheless, the accuracy of sediment ratings has remained a matter of concern for hydrologists. The present article investigates both the aspect of improving the accuracy, i.e., modifying the simple rating curve equation by introducing a four-parameter equation and application of ensemble machine learning (ML) and ensemble empirical models, to estimate sediment loads. The ML models include artificial neural networks, multi-gene genetic programming (MGGP), and a hybrid MGGP-based model. Published field data at two measuring stations were used to assess the performance of different models employed in this study. The comparative analysis conducted in this study provides a novel comparison of sediment load estimations for three time scales. For instance, the ML-based simple average ensemble model (i.e., 556.5, 255.0, and 0.759) and the empirical-based nonlinear ensemble model (i.e., 549.1, 378.6, and 0.589) achieved the lowest root-mean-square errors and mean absolute errors and highest determination coefficients for the train and test monthly sediment data of the first station, respectively. Finally, the findings demonstrate that ensemble-based models generally improve the estimates of sediment loads at daily, 10-daily, and monthly scales.
Bridges are essential structures that connect riverbanks and facilitate transportation. However, ... more Bridges are essential structures that connect riverbanks and facilitate transportation. However, bridge piers and abutments can disrupt the natural flow of rivers, causing a rise in water levels upstream of the bridge. The rise in water levels, known as bridge backwater or afflux, can threaten the stability or service of bridges and riverbanks. It is postulated that applications of estimation models with more precise afflux predictions can enhance the safety of bridges in flood-prone areas. In this study, eight machine learning (ML) models were developed to estimate bridge afflux utilizing 202 laboratory and 66 field data. The ML models consist of Support Vector Regression (SVR), Decision Tree Regressor (DTR), Random Forest Regressor (RFR), AdaBoost Regressor (ABR), Gradient Boost Regressor (GBR), eXtreme Gradient Boosting (XGBoost) for Regression (XGBR), Gaussian Process Regression (GPR), and K-Nearest Neighbors (KNN). To the best of the authors’ knowledge, this is the first time t...
Sediment ratings supply an important input to the design of water resources projects. Nevertheles... more Sediment ratings supply an important input to the design of water resources projects. Nevertheless, the accuracy of sediment ratings has remained a matter of concern for hydrologists. The present article investigates both the aspect of improving the accuracy, i.e., modifying the simple rating curve equation by introducing a four-parameter equation and application of ensemble machine learning (ML) and ensemble empirical models, to estimate sediment loads. The ML models include artificial neural networks, multi-gene genetic programming (MGGP), and a hybrid MGGP-based model. Published field data at two measuring stations were used to assess the performance of different models employed in this study. The comparative analysis conducted in this study provides a novel comparison of sediment load estimations for three time scales. For instance, the ML-based simple average ensemble model (i.e., 556.5, 255.0, and 0.759) and the empirical-based nonlinear ensemble model (i.e., 549.1, 378.6, and 0.589) achieved the lowest root-mean-square errors and mean absolute errors and highest determination coefficients for the train and test monthly sediment data of the first station, respectively. Finally, the findings demonstrate that ensemble-based models generally improve the estimates of sediment loads at daily, 10-daily, and monthly scales.
Excessive population growth and high water demands have significantly increased water extractions... more Excessive population growth and high water demands have significantly increased water extractions from deep and semi-deep wells in the arid regions of Iran. This has negatively affected water quality in different areas. The Water Quality Index (WQI) is a suitable tool to assess such impacts. This study used WQI and the fuzzy hierarchical analysis process of the water quality index (FAHP-WQI) to investigate the water quality status of 96 deep agricultural wells in the Yazd-Ardakan Plain, Iran. Calculating the WQI is time-consuming, but estimating WQI is inevitable for water resources management. For this purpose, three Machine Learning (ML) algorithms, namely, Gene Expression Programming (GEP), M5P Model tree, and Multivariate Adaptive Regression Splines (MARS), were employed to predict WQI. Using Wilcox and Schoeller charts, water quality was also investigated for agricultural and drinking purposes. The results demonstrated that 75% and 33% of the study area have good quality, based...
Snowmelt is an important source of stream flows in mountainous areas. This study investigated the... more Snowmelt is an important source of stream flows in mountainous areas. This study investigated the impact of snowmelt on flooding. First, the study area was divided into four zones based on elevation. Second, the Snow-Covered Area (SCA) from 2013 to 2018 was estimated from daily MODIS images with the help of Google Earth Engine. Runoff in the area was then simulated using the Snowmelt Runoff Model (SRM). As a result, short periods with high runoff and the possibility of floods were identified, while the contribution of snowmelt and rainfall in the total runoff was separated. The results showed that while the snowmelt on average accounted for only 23% of total runoff in the zone with elevation under 2000 m, the ratio increased with elevation, ultimately reaching as high as 87% in the zone with elevation above 3000 m. As the height increases, the effect of snow on runoff and flooding increases so much that it should not be ignored. However, in most hydrological studies, the effect of snow is ignored due to the lack of sufficient data about snow. This study showed that snow can be very effective, especially in high areas.
Environment, Development and Sustainability, Apr 21, 2023
Sediment ratings supply an important input to the design of water resources projects. Nevertheles... more Sediment ratings supply an important input to the design of water resources projects. Nevertheless, the accuracy of sediment ratings has remained a matter of concern for hydrologists. The present article investigates both the aspect of improving the accuracy, i.e., modifying the simple rating curve equation by introducing a four-parameter equation and application of ensemble machine learning (ML) and ensemble empirical models, to estimate sediment loads. The ML models include artificial neural networks, multi-gene genetic programming (MGGP), and a hybrid MGGP-based model. Published field data at two measuring stations were used to assess the performance of different models employed in this study. The comparative analysis conducted in this study provides a novel comparison of sediment load estimations for three time scales. For instance, the ML-based simple average ensemble model (i.e., 556.5, 255.0, and 0.759) and the empirical-based nonlinear ensemble model (i.e., 549.1, 378.6, and 0.589) achieved the lowest root-mean-square errors and mean absolute errors and highest determination coefficients for the train and test monthly sediment data of the first station, respectively. Finally, the findings demonstrate that ensemble-based models generally improve the estimates of sediment loads at daily, 10-daily, and monthly scales.
Bridges are essential structures that connect riverbanks and facilitate transportation. However, ... more Bridges are essential structures that connect riverbanks and facilitate transportation. However, bridge piers and abutments can disrupt the natural flow of rivers, causing a rise in water levels upstream of the bridge. The rise in water levels, known as bridge backwater or afflux, can threaten the stability or service of bridges and riverbanks. It is postulated that applications of estimation models with more precise afflux predictions can enhance the safety of bridges in flood-prone areas. In this study, eight machine learning (ML) models were developed to estimate bridge afflux utilizing 202 laboratory and 66 field data. The ML models consist of Support Vector Regression (SVR), Decision Tree Regressor (DTR), Random Forest Regressor (RFR), AdaBoost Regressor (ABR), Gradient Boost Regressor (GBR), eXtreme Gradient Boosting (XGBoost) for Regression (XGBR), Gaussian Process Regression (GPR), and K-Nearest Neighbors (KNN). To the best of the authors’ knowledge, this is the first time t...
Sediment ratings supply an important input to the design of water resources projects. Nevertheles... more Sediment ratings supply an important input to the design of water resources projects. Nevertheless, the accuracy of sediment ratings has remained a matter of concern for hydrologists. The present article investigates both the aspect of improving the accuracy, i.e., modifying the simple rating curve equation by introducing a four-parameter equation and application of ensemble machine learning (ML) and ensemble empirical models, to estimate sediment loads. The ML models include artificial neural networks, multi-gene genetic programming (MGGP), and a hybrid MGGP-based model. Published field data at two measuring stations were used to assess the performance of different models employed in this study. The comparative analysis conducted in this study provides a novel comparison of sediment load estimations for three time scales. For instance, the ML-based simple average ensemble model (i.e., 556.5, 255.0, and 0.759) and the empirical-based nonlinear ensemble model (i.e., 549.1, 378.6, and 0.589) achieved the lowest root-mean-square errors and mean absolute errors and highest determination coefficients for the train and test monthly sediment data of the first station, respectively. Finally, the findings demonstrate that ensemble-based models generally improve the estimates of sediment loads at daily, 10-daily, and monthly scales.
Excessive population growth and high water demands have significantly increased water extractions... more Excessive population growth and high water demands have significantly increased water extractions from deep and semi-deep wells in the arid regions of Iran. This has negatively affected water quality in different areas. The Water Quality Index (WQI) is a suitable tool to assess such impacts. This study used WQI and the fuzzy hierarchical analysis process of the water quality index (FAHP-WQI) to investigate the water quality status of 96 deep agricultural wells in the Yazd-Ardakan Plain, Iran. Calculating the WQI is time-consuming, but estimating WQI is inevitable for water resources management. For this purpose, three Machine Learning (ML) algorithms, namely, Gene Expression Programming (GEP), M5P Model tree, and Multivariate Adaptive Regression Splines (MARS), were employed to predict WQI. Using Wilcox and Schoeller charts, water quality was also investigated for agricultural and drinking purposes. The results demonstrated that 75% and 33% of the study area have good quality, based...
Snowmelt is an important source of stream flows in mountainous areas. This study investigated the... more Snowmelt is an important source of stream flows in mountainous areas. This study investigated the impact of snowmelt on flooding. First, the study area was divided into four zones based on elevation. Second, the Snow-Covered Area (SCA) from 2013 to 2018 was estimated from daily MODIS images with the help of Google Earth Engine. Runoff in the area was then simulated using the Snowmelt Runoff Model (SRM). As a result, short periods with high runoff and the possibility of floods were identified, while the contribution of snowmelt and rainfall in the total runoff was separated. The results showed that while the snowmelt on average accounted for only 23% of total runoff in the zone with elevation under 2000 m, the ratio increased with elevation, ultimately reaching as high as 87% in the zone with elevation above 3000 m. As the height increases, the effect of snow on runoff and flooding increases so much that it should not be ignored. However, in most hydrological studies, the effect of snow is ignored due to the lack of sufficient data about snow. This study showed that snow can be very effective, especially in high areas.
It is of my great pleasure to announce that I have been selected as one of the World's Top 2% Sci... more It is of my great pleasure to announce that I have been selected as one of the World's Top 2% Scientists. The corresponding list was published by Stanford University and Elsevier on 19 October 2021.
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