This page has only limited features, please log in for full access.
Accurate prediction of stable alluvial hydraulic geometry, in which erosion and sedimentation are in equilibrium, is one of the most difficult but critical topics in the field of river engineering. Data mining algorithms have been gaining more attention in this field due to their high performance and flexibility. However, an understanding of the potential for these algorithms to provide fast, cheap, and accurate predictions of hydraulic geometry is lacking. This study provides the first quantification of this potential. Using at-a-station field data, predictions of flow depth, water-surface width and longitudinal water surface slope are made using three standalone data mining techniques -, Instance-based Learning (IBK), KStar, Locally Weighted Learning (LWL) - along with four types of novel hybrid algorithms in which the standalone models are trained with Vote, Attribute Selected Classifier (ASC), Regression by Discretization (RBD), and Cross-validation Parameter Selection (CVPS) algorithms (Vote-IBK, Vote-Kstar, Vote-LWL, ASC-IBK, ASC-Kstar, ASC-LWL, RBD-IBK, RBD-Kstar, RBD-LWL, CVPS-IBK, CVPS-Kstar, CVPS-LWL). Through a comparison of their predictive performance and a sensitivity analysis of the driving variables, the results reveal: (1) Shield stress was the most effective parameter in the prediction of all geometry dimensions; (2) hybrid models had a higher prediction power than standalone data mining models, empirical equations and traditional machine learning algorithms; (3) Vote-Kstar model had the highest performance in predicting depth and width, and ASC-Kstar in estimating slope, each providing very good prediction performance. Through these algorithms, the hydraulic geometry of any river can potentially be predicted accurately and with ease using just a few, readily available flow and channel parameters. Thus, the results reveal that these models have great potential for use in stable channel design in data poor catchments, especially in developing nations where technical modelling skills and understanding of the hydraulic and sediment processes occurring in the river system may be lacking.
Khabat Khosravi; Zohreh Sheikh Khozani; James R. Cooper. Predicting stable gravel-bed river hydraulic geometry: A test of novel, advanced, hybrid data mining algorithms. Environmental Modelling & Software 2021, 144, 105165 .
AMA StyleKhabat Khosravi, Zohreh Sheikh Khozani, James R. Cooper. Predicting stable gravel-bed river hydraulic geometry: A test of novel, advanced, hybrid data mining algorithms. Environmental Modelling & Software. 2021; 144 ():105165.
Chicago/Turabian StyleKhabat Khosravi; Zohreh Sheikh Khozani; James R. Cooper. 2021. "Predicting stable gravel-bed river hydraulic geometry: A test of novel, advanced, hybrid data mining algorithms." Environmental Modelling & Software 144, no. : 105165.
Accurate streamflow (Qt) prediction can provide critical information for urban hydrological management strategies such as flood mitigation, long-term water resources management, land use planning and agricultural and irrigation operations. Since the mid-20th century, Artificial Intelligence (AI) models have been used in a wide range of engineering and scientific fields, and their application has increased in the last few years. In this study, the predictive capabilities of the reduced error pruning tree (REPT) model, used both as a standalone model and within five ensemble-approaches, were evaluated to predict streamflow in the Kurkursar basin in Iran. The ensemble-approaches combined the REPT model with the bootstrap aggregation (BA), random committee (RC), random subspace (RS), additive regression (AR) and disjoint aggregating (DA) (i.e. BA-REPT, RC-REPT, RS-REPT, AR-REPT and DA-REPT). The models were developed using 15 years of daily rainfall and streamflow data for the period 23 September 1997 to 22 September 2012. A set of eight different input scenarios was constructed using different combinations of the input variables to find the most effective scenario based on the linear correlation coefficient. A comprehensive suite of graphical (time-variation graph, scatter-plot, violin plot and Taylor diagram) and quantitative metrics (root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliff efficiency (NSE), Percent of BIAS (PBIAS) and the ratio of RMSE to the standard deviation of observation (RSR)) was applied to evaluate the prediction accuracy of the six models developed. The outcomes indicated that all models performed well but the AR-REPT outperformed all the other models by rendering lower errors and higher precision across a number of statistical measures. The use of the BA, RC, RS, AR and DA models enhanced the performance of the standalone REPT model by about 26.82%, 18.91%, 7.69%, 28.99% and 28.05% respectively.
Khabat Khosravi; Shaghayegh Miraki; Patricia M. Saco; Raziyeh Farmani. Short-term River streamflow modeling using Ensemble-based additive learner approach. Journal of Hydro-environment Research 2021, 1 .
AMA StyleKhabat Khosravi, Shaghayegh Miraki, Patricia M. Saco, Raziyeh Farmani. Short-term River streamflow modeling using Ensemble-based additive learner approach. Journal of Hydro-environment Research. 2021; ():1.
Chicago/Turabian StyleKhabat Khosravi; Shaghayegh Miraki; Patricia M. Saco; Raziyeh Farmani. 2021. "Short-term River streamflow modeling using Ensemble-based additive learner approach." Journal of Hydro-environment Research , no. : 1.
Trace element (TE) pollution in groundwater resources is one of the major concerns in both developing and developed countries as it can directly affect human health. Arsenic (As), Barium (Ba), and Rubidium (Rb) can be considered as TEs naturally present in groundwater due to water-rock interactions in Campania Plain (CP) aquifers, in South Italy. Their concentration could be predicted via some readily available input variables using an algorithm like the iterative classifier optimizer (ICO) for regression, and novel hybrid algorithms with additive regression (AR-ICO), attribute selected classifier (ASC-ICO) and bagging (BA-ICO). In this regard, 244 groundwater samples were collected from water wells within the CP and analyzed with respect to the electrical conductivity, pH, major ions and selected TEs. To develop the models, the available dataset was divided randomly into two subsets for model training (70% of the dataset) and evaluation (30% of the dataset), respectively. Based on the correlation coefficient (r), different input variables combinations were constructed to find the most effective one. Each model's performance was evaluated using common statistical and visual metrics. Results indicated that the prediction of As and Ba concentrations strongly depends on HCO3−, while Na+ is the most effective variable on Rb prediction. Also, the findings showed that the most powerful predictive models were those that used all the available input variables. According to models' performance evaluation metrics, the hybrid ASC-ICO outperformed other hybrid (BA- and AR-ICO) and standalone (ICO) algorithms to predict As and Ba concentrations, while both hybrid ASC- and BA-ICO models had higher accuracy and lower error than other algorithms for Rb prediction.
Khabat Khosravi; Rahim Barzegar; Ali Golkarian; Gianluigi Busico; Emilio Cuoco; Micòl Mastrocicco; Nicolò Colombani; Dario Tedesco; Maria Margarita Ntona; Nerantzis Kazakis. Predictive modeling of selected trace elements in groundwater using hybrid algorithms of iterative classifier optimizer. Journal of Contaminant Hydrology 2021, 242, 103849 .
AMA StyleKhabat Khosravi, Rahim Barzegar, Ali Golkarian, Gianluigi Busico, Emilio Cuoco, Micòl Mastrocicco, Nicolò Colombani, Dario Tedesco, Maria Margarita Ntona, Nerantzis Kazakis. Predictive modeling of selected trace elements in groundwater using hybrid algorithms of iterative classifier optimizer. Journal of Contaminant Hydrology. 2021; 242 ():103849.
Chicago/Turabian StyleKhabat Khosravi; Rahim Barzegar; Ali Golkarian; Gianluigi Busico; Emilio Cuoco; Micòl Mastrocicco; Nicolò Colombani; Dario Tedesco; Maria Margarita Ntona; Nerantzis Kazakis. 2021. "Predictive modeling of selected trace elements in groundwater using hybrid algorithms of iterative classifier optimizer." Journal of Contaminant Hydrology 242, no. : 103849.
Sediment transport modeling has been known as an essential issue and challenging task in water resources and environmental engineering. In order to minimize the adverse impacts of the continues sediment deposition that is known as a main source of pollution in the urban area, the self-cleansing method is widely utilized for designing the sewer pipes to create a condition to keep the bottom of channel clean from sedimentation. In the present study, an extensive data range is utilized for modeling the sediment transport in non-deposition with clean bed condition. Regarding the effective parameters involved, four different scenarios are considered for the modeling. To this end, four standalone methods including the M5P, reduced error pruning tree (REPT), random forest (RF) and random tree (RT) and two hybrid models based on rotation forest (ROF) and weighted instances handler wrapper (WIHW) techniques are developed and result compared with three empirical equations. Based on the results, the hybrid WIHW-RT and WIHW-RF models provide better performance in particle Froude number estimation in comparison to other standalone and hybrid models. Performances of the most of the models are found accurate except RT and REPT standalone models. The outcomes revealed that the empirical models have considerable overestimation. Generally, hybrid data mining methods yield more precise estimations of sediment transport in contrast to the regression equations and standalone models. Particularly, both WIHW-RT and WIHW-RF models provide almost the same performances however, as WIHW-RT can better capture the extreme particle Froude number values, it slightly outperforms WIHW-RF. Promising findings of the current study may encourage the implementation of the recommended approaches in alternative hydrological problems.
Katayoun Kargar; Mir Jafar Sadegh Safari; Khabat Khosravi. Weighted instances handler wrapper and rotation forest-based hybrid algorithms for sediment transport modeling. Journal of Hydrology 2021, 598, 126452 .
AMA StyleKatayoun Kargar, Mir Jafar Sadegh Safari, Khabat Khosravi. Weighted instances handler wrapper and rotation forest-based hybrid algorithms for sediment transport modeling. Journal of Hydrology. 2021; 598 ():126452.
Chicago/Turabian StyleKatayoun Kargar; Mir Jafar Sadegh Safari; Khabat Khosravi. 2021. "Weighted instances handler wrapper and rotation forest-based hybrid algorithms for sediment transport modeling." Journal of Hydrology 598, no. : 126452.
In the current paper, the efficiency of three new standalone data mining algorithms [e.g., M5P, Random Forest (RF), M5Rule (M5R)] and six novel hybrid algorithms of Bagging, BA (BA-M5P, BA-RF and BA-M5R) and Attribute Selected Classifier, ASC (ASC-M5P, ASC-RF and ASC-M5R) for streamflow prediction were assessed and compared with autoregressive integrated moving average (ARIMA) model as a benchmark. The models used precipitation (P) and streamflow (Q) data from 1979-2012 for training and validation (70% and 30% of data, respectively). Different input combinations were prepared using both P and Q with different lag times. The best input combination proved to be that in which all the data were used (i.e., R and Q –with lag times). Overall, employing Q with different lag times proved to be more effective than using only P as input for streamflow prediction. Although all models showed very good predictive power, the BA-M5P outperformed the other models.
Khabat Khosravi; Ali Golkarian; Martijn J. Booij; Rahim Barzegar; Wei Sun; Zaher M. Yaseen; Amir Mosavi. Improving daily stochastic streamflow prediction: Comparison of novel hybrid data mining algorithms. Hydrological Sciences Journal 2021, 1 .
AMA StyleKhabat Khosravi, Ali Golkarian, Martijn J. Booij, Rahim Barzegar, Wei Sun, Zaher M. Yaseen, Amir Mosavi. Improving daily stochastic streamflow prediction: Comparison of novel hybrid data mining algorithms. Hydrological Sciences Journal. 2021; ():1.
Chicago/Turabian StyleKhabat Khosravi; Ali Golkarian; Martijn J. Booij; Rahim Barzegar; Wei Sun; Zaher M. Yaseen; Amir Mosavi. 2021. "Improving daily stochastic streamflow prediction: Comparison of novel hybrid data mining algorithms." Hydrological Sciences Journal , no. : 1.
Sixteen different sites from two provinces (Lorestan and Illam) in the western part of Iran were considered for the field data measurement of cumulative infiltration, infiltration rate, and other effective variables that affect infiltration process. Soil infiltration is recognized as a fundamental process of the hydrologic cycle affecting surface runoff, soil erosion, and groundwater recharge. Hence, accurate prediction of the infiltration process is one of the most important tasks in hydrological science. As direct measurement is difficult and costly, and empirical models are inaccurate, the current study proposed a standalone, and optimized deep learning algorithm of a convolutional neural network (CNN) using gray wolf optimization (GWO), a genetic algorithm (GA), and an independent component analysis (ICA) for cumulative infiltration and infiltration rate prediction. First, 154 raw datasets were collected including the time of measuring; sand, clay, and silt percent; bulk density; soil moisture percent; infiltration rate; and cumulative infiltration using field survey. Next, 70 % of the dataset were used for model building and the remaining 30 % was used for model validation. Then, based on the correlation coefficient between input variables and outputs, different input combinations were constructed. Finally, the prediction power of each developed algorithm was evaluated using different visually-based (scatter plot, box plot and Taylor diagram) and quantitatively-based [root mean square error (RMSE), mean absolute error (MAE), the Nash-Sutcliffe efficiency (NSE), and percentage of bias (PBIAS)] metrics. Finding revealed that the time of measurement is more important for cumulative infiltration, while soil characteristics (i.e. silt content) are more significant in infiltration rate prediction. This shows that in the study area, silt parameter, which is the dominant constituent parameter, can control infiltration process more effectively. Effectiveness of the variables in the present study, in the order of importance are time, silt, clay, moisture content, sand, and bulk density. This can be related to the fact that most of study area is rangeland and thus, overgrazing leads to compaction of the silt soil that can lead to a slow infiltration process. Soil moisture content and bulk density are not highly effective in our study because these two factors do not significantly change across the study area. Findings demonstrated that the optimum input variable combination, is the one in which all input variables are considered. The results illustrated that CNN algorithms have a very high performance, while a metaheuristic algorithm enhanced the performance of a standalone CNN algorithm (from 7% to 28 %). The results also showed that a CNN-GWO algorithm outperformed the other algorithms, followed by CNN-ICA, CNN-GA, and CNN for both cumulative infiltration and infiltration rate prediction. All developed algorithms underestimated cumulative infiltration, while overestimating infiltration rates.
Mahdi Panahi; Khabat Khosravi; Sajjad Ahmad; Somayeh Panahi; Salim Heddam; Assefa M Melesse; Ebrahim Omidvar; Chang-Wook Lee. Cumulative infiltration and infiltration rate prediction using optimized deep learning algorithms: A study in Western Iran. Journal of Hydrology: Regional Studies 2021, 35, 100825 .
AMA StyleMahdi Panahi, Khabat Khosravi, Sajjad Ahmad, Somayeh Panahi, Salim Heddam, Assefa M Melesse, Ebrahim Omidvar, Chang-Wook Lee. Cumulative infiltration and infiltration rate prediction using optimized deep learning algorithms: A study in Western Iran. Journal of Hydrology: Regional Studies. 2021; 35 ():100825.
Chicago/Turabian StyleMahdi Panahi; Khabat Khosravi; Sajjad Ahmad; Somayeh Panahi; Salim Heddam; Assefa M Melesse; Ebrahim Omidvar; Chang-Wook Lee. 2021. "Cumulative infiltration and infiltration rate prediction using optimized deep learning algorithms: A study in Western Iran." Journal of Hydrology: Regional Studies 35, no. : 100825.
The objective of the current study is groundwater vulnerability assessment using DRASTIC, modified DRASTIC, and three statistical bivariate models (frequency ratio (FR), evidential belief function (EBF), and weights-of-evidence (WOE)) for Sari-Behshahr plain, Iran. A total of 218 wells were sampled for nitrate concentration measurement in 2015. Datasets were generated using results from 109 wells having nitrate concentrations greater than 50 mg/L. The nitrate data were divided into two groups of 70% (76 locations as training dataset) for modeling and 30% (33 locations as a testing dataset) for model validation. Finally, five groundwater potential pollution (GPP) maps were produced by the training dataset and then evaluated using the testing dataset and receiver operating characteristic (ROC) method. Results of the ROC method showed that the WOE model had the highest predictive power, followed by EBF, FR, modified DRASTIC, and DRASTIC models. Results of the maps obtained revealed that high and very high pollution potential covered the southern part of the study areas, where big cities are located. Results of the present study can be replicated in other locations for identifying groundwater contaminant prone areas.
Khabat Khosravi; Majid Sartaj; Mahshid Karimi; Jana Levison; Aghdas Lotfi. A GIS-based groundwater pollution potential using DRASTIC, modified DRASTIC, and bivariate statistical models. Environmental Science and Pollution Research 2021, 1 -17.
AMA StyleKhabat Khosravi, Majid Sartaj, Mahshid Karimi, Jana Levison, Aghdas Lotfi. A GIS-based groundwater pollution potential using DRASTIC, modified DRASTIC, and bivariate statistical models. Environmental Science and Pollution Research. 2021; ():1-17.
Chicago/Turabian StyleKhabat Khosravi; Majid Sartaj; Mahshid Karimi; Jana Levison; Aghdas Lotfi. 2021. "A GIS-based groundwater pollution potential using DRASTIC, modified DRASTIC, and bivariate statistical models." Environmental Science and Pollution Research , no. : 1-17.
Complex vortex flow patterns around bridge piers, especially during floods, cause scour process that can result in the failure of foundations. Abutment scour is a complex three-dimensional phenomenon that is difficult to predict especially with traditional formulas obtained using empirical approaches such as regressions. This paper presents a test of a standalone Kstar model with five novel hybrid algorithm of bagging (BA-Kstar), dagging (DA-Kstar), random committee (RC-Kstar), random subspace (RS-Kstar), and weighted instance handler wrapper (WIHW-Kstar) to predict scour depth (ds) for clear water condition. The dataset consists of 99 scour depth data from flume experiments (Dey and Barbhuiya, 2005) using abutment shapes such as vertical, semicircular and 45° wing. Four dimensionless parameter of relative flow depth (h/l), excess abutment Froude number (Fe), relative sediment size (d50/l) and relative submergence (d50/h) were considered for the prediction of relative scour depth (ds/l). A portion of the dataset was used for the calibration (70%), and the remaining used for model validation. Pearson correlation coefficients helped deciding relevance of the input parameters combination and finally four different combinations of input parameters were used. The performance of the models was assessed visually and with quantitative metrics. Overall, the best input combination for vertical abutment shape is the combination of Fe, d50/l and h/l, while for semicircular and 45° wing the combination of the Fe and d50/l is the most effective input parameter combination. Our results show that incorporating Fe, d50/l and h/l lead to higher performance while involving d50/h reduced the models prediction power for vertical abutment shape and for semicircular and 45° wing involving h/l and d50/h lead to more error. The WIHW-Kstar provided the highest performance in scour depth prediction around vertical abutment shape while RC-Kstar model outperform of other models for scour depth prediction around semicircular and 45° wing.
Khabat Khosravi; Zohreh Sheikh Khozani; Luca Mao. A comparison between advanced hybrid machine learning algorithms and empirical equations applied to abutment scour depth prediction. Journal of Hydrology 2021, 596, 126100 .
AMA StyleKhabat Khosravi, Zohreh Sheikh Khozani, Luca Mao. A comparison between advanced hybrid machine learning algorithms and empirical equations applied to abutment scour depth prediction. Journal of Hydrology. 2021; 596 ():126100.
Chicago/Turabian StyleKhabat Khosravi; Zohreh Sheikh Khozani; Luca Mao. 2021. "A comparison between advanced hybrid machine learning algorithms and empirical equations applied to abutment scour depth prediction." Journal of Hydrology 596, no. : 126100.
Due to excessive exploitation, groundwater resources of coastal regions are exposed to seawater intrusion. Therefore, vulnerability assessments are essential for the quantitative and qualitative management of these resources. The GALDIT model is the most widely used approach for coastal aquifer vulnerability assessment, but suffers from subjectivity of the identification of rates and weights. This study aimes at developing a new hybrid framework for improving the accuracy of coastal aquifer vulnerability assessment using various statistical, metaheuristic, and Multi-Attribute Decision Making (MADM) methods to improve the GALDIT model. The Gharesoo-Gorgan Rood coastal aquifer in northern Iran is used as study site. In order to meet this aim, the Differential Evolution (DE) and Biogeography-Based Optimization (BBO) metaheuristic algorithms were employed to optimize the GALDIT weights. In addition, a novel MADM method, named Step-wise Weight Assessment Ratio Analysis (SWARA), and the bivariate statistical method called statistical index (SI) were used to modify the GALDIT ratings. Finally, correlation coefficients between the maps obtained from each method and Total Dissolved Solid (TDS) as an indicator of seawater intrusion were computed to evaluate the models' prediction power. Correlation coefficients of 0.72, 0.75, 0.76 and 0.78 were obtained for the GALDITSWARA-BBO, GALDITSI-BBO, GALDITSWARA-DE and GALDITSI-DE models, respectively. The results from the GALDITSI-DE model outperformed all other models at improving the accuracy of the vulnerability assessment. Moreover, the statistical-metaheuristic method yielded more accurate results than SWARA-metaheuristic hybrid models. The vulnerability map of the studied region indicates that the northwestern and western areas are very highly vulnerable. According to GALDITSI-DE model, 42%, 17%, 18% and 22% of the aquifer areas respectively have a low, medium, high and very high vulnerability to seawater intrusion. The research findings could be applied by regional authorities to manage and protect groundwater resources.
Khabat Khosravi; Mojgan Bordbar; Sina Paryani; Patricia M. Saco; Nerantzis Kazakis. New hybrid-based approach for improving the accuracy of coastal aquifer vulnerability assessment maps. Science of The Total Environment 2021, 767, 145416 .
AMA StyleKhabat Khosravi, Mojgan Bordbar, Sina Paryani, Patricia M. Saco, Nerantzis Kazakis. New hybrid-based approach for improving the accuracy of coastal aquifer vulnerability assessment maps. Science of The Total Environment. 2021; 767 ():145416.
Chicago/Turabian StyleKhabat Khosravi; Mojgan Bordbar; Sina Paryani; Patricia M. Saco; Nerantzis Kazakis. 2021. "New hybrid-based approach for improving the accuracy of coastal aquifer vulnerability assessment maps." Science of The Total Environment 767, no. : 145416.
Flood spatial susceptibility prediction is the first essential step in developing flood mitigation strategies and reducing flood damage. Flood occurrence is a complex process that is not easily predicted through simple methods. This study describes optimization of support vector regression (SVR) using meta-optimization algorithms including the grasshopper optimization algorithm (GOA) and particle swarm optimization (PSO) for flood modeling at Qazvin Plain, Iran. Geospatial data including nine readily available geo-environmental flood conditioning factors (i.e., ground slope, aspect, elevation, planform curvature, profile curvature, proximity to a river, land use, lithology and rainfall) were derived. The information gain ratio (IGR) method was used to determine the relative importance of input variables. A historical flood inventory map for 43 locations was created from existing reports. The geospatial data and historical flood levels were used to construct the training and testing datasets. Then, the training dataset was used to generate flood-susceptibility maps using the optimized SVR model with the GOA and PSO algorithms. Finally, the predictive accuracy of the models was quantified using the statistical measures of root mean square error (RMSE), mean absolute error (MAE), and area under the receiver operating characteristic (ROC) curve (AUC). Although both the GOA and PSO algorithms improved SVR performance, the SVR-GOA model performed best (AUC = 0.959, RMSE = 0.31 and MSE = 0.098), followed by the SVR-PSO model (AUC = 0.959, RMSE = 0.33 and MSE = 0.11) and standalone SVR model (AUC = 0.87, RMSE = 0.35 and MSE = 0.12). Elevation, lithology and aspect had the highest IGR values and were identified as the most effective predictors of flood susceptibility.
Mahdi Panahi; Esmaeel Dodangeh; Fatemeh Rezaie; Khabat Khosravi; Hiep Van Le; Moung-Jin Lee; Saro Lee; Binh Thai Pham. Flood spatial prediction modeling using a hybrid of meta-optimization and support vector regression modeling. CATENA 2021, 199, 105114 .
AMA StyleMahdi Panahi, Esmaeel Dodangeh, Fatemeh Rezaie, Khabat Khosravi, Hiep Van Le, Moung-Jin Lee, Saro Lee, Binh Thai Pham. Flood spatial prediction modeling using a hybrid of meta-optimization and support vector regression modeling. CATENA. 2021; 199 ():105114.
Chicago/Turabian StyleMahdi Panahi; Esmaeel Dodangeh; Fatemeh Rezaie; Khabat Khosravi; Hiep Van Le; Moung-Jin Lee; Saro Lee; Binh Thai Pham. 2021. "Flood spatial prediction modeling using a hybrid of meta-optimization and support vector regression modeling." CATENA 199, no. : 105114.
Seyed Vahid Razavi-Termeh; Khabat Khosravi; Abolghasem Sadeghi-Niaraki; Soo-Mi Choi; Vijay P. Singh. Improving groundwater potential mapping using metaheuristic approaches. Hydrological Sciences Journal 2020, 65, 2729 -2749.
AMA StyleSeyed Vahid Razavi-Termeh, Khabat Khosravi, Abolghasem Sadeghi-Niaraki, Soo-Mi Choi, Vijay P. Singh. Improving groundwater potential mapping using metaheuristic approaches. Hydrological Sciences Journal. 2020; 65 (16):2729-2749.
Chicago/Turabian StyleSeyed Vahid Razavi-Termeh; Khabat Khosravi; Abolghasem Sadeghi-Niaraki; Soo-Mi Choi; Vijay P. Singh. 2020. "Improving groundwater potential mapping using metaheuristic approaches." Hydrological Sciences Journal 65, no. 16: 2729-2749.
Suspended sediment load is a substantial portion of the total sediment load in rivers and plays a vital role in determination of the service life of the downstream dam. To this end, estimation models are needed to compute suspended sediment load in rivers. The application of artificial intelligence (AI) techniques has become popular in water resources engineering for solving complex problems such as sediment transport modeling. In this study, novel integrative intelligence models coupled with iterative classifier optimizer (ICO) are proposed to compute suspended sediment load in Simga station in Seonath river basin, Chhattisgarh State, India. The proposed models are hybridization of the random forest (RF) and pace regression (PR) models with the iterative classifier optimizer (ICO) algorithm to develop ICO-RF and ICO-PR hybrid models. The recommended models are established using the discharge and sediment daily data spanning a 35-year period (1980–2015). The accuracy of the developed models is examined in terms of error; by root mean square error (RMSE) and mean absolute error (MAE); and based on a correlation index of determination coefficient (R2). The proposed novel hybrid models of ICO-RF and ICO-PR have been found to be more precise than their stand-alone counterparts of RF and PR. Overall, ICO-RF models delivered better accuracy than their alternatives. The results of this analysis tend to claim the appropriateness of the implemented methodology for precise modeling of the suspended sediment load in rivers.
Sarita Gajbhiye Meshram; Mir Jafar Sadegh Safari; Khabat Khosravi; Chandrashekhar Meshram. Iterative classifier optimizer-based pace regression and random forest hybrid models for suspended sediment load prediction. Environmental Science and Pollution Research 2020, 28, 11637 -11649.
AMA StyleSarita Gajbhiye Meshram, Mir Jafar Sadegh Safari, Khabat Khosravi, Chandrashekhar Meshram. Iterative classifier optimizer-based pace regression and random forest hybrid models for suspended sediment load prediction. Environmental Science and Pollution Research. 2020; 28 (9):11637-11649.
Chicago/Turabian StyleSarita Gajbhiye Meshram; Mir Jafar Sadegh Safari; Khabat Khosravi; Chandrashekhar Meshram. 2020. "Iterative classifier optimizer-based pace regression and random forest hybrid models for suspended sediment load prediction." Environmental Science and Pollution Research 28, no. 9: 11637-11649.
Electrical conductivity (EC), one of the most widely used indices for water quality assessment, has been applied to predict the salinity of the Babol-Rood River, the greatest source of irrigation water in northern Iran. This study uses two individual—M5 Prime (M5P) and random forest (RF)—and eight novel hybrid algorithms—bagging-M5P, bagging-RF, random subspace (RS)-M5P, RS-RF, random committee (RC)-M5P, RC-RF, additive regression (AR)-M5P, and AR-RF—to predict EC. Thirty-six years of observations collected by the Mazandaran Regional Water Authority were randomly divided into two sets: 70% from the period 1980 to 2008 was used as model-training data and 30% from 2009 to 2016 was used as testing data to validate the models. Several water quality variables—pH, HCO3−, Cl−, SO42−, Na+, Mg2+, Ca2+, river discharge (Q), and total dissolved solids (TDS)—were modeling inputs. Using EC and the correlation coefficients (CC) of the water quality variables, a set of nine input combinations were established. TDS, the most effective input variable, had the highest EC-CC (r = 0.91), and it was also determined to be the most important input variable among the input combinations. All models were trained and each model’s prediction power was evaluated with the testing data. Several quantitative criteria and visual comparisons were used to evaluate modeling capabilities. Results indicate that, in most cases, hybrid algorithms enhance individual algorithms’ predictive powers. The AR algorithm enhanced both M5P and RF predictions better than bagging, RS, and RC. M5P performed better than RF. Further, AR-M5P outperformed all other algorithms (R2 = 0.995, RMSE = 8.90 μs/cm, MAE = 6.20 μs/cm, NSE = 0.994 and PBIAS = −0.042). The hybridization of machine learning methods has significantly improved model performance to capture maximum salinity values, which is essential in water resource management.
Assefa M. Melesse; Khabat Khosravi; John P. Tiefenbacher; Salim Heddam; Sungwon Kim; Amir Mosavi; Binh Thai Pham. River Water Salinity Prediction Using Hybrid Machine Learning Models. Water 2020, 12, 2951 .
AMA StyleAssefa M. Melesse, Khabat Khosravi, John P. Tiefenbacher, Salim Heddam, Sungwon Kim, Amir Mosavi, Binh Thai Pham. River Water Salinity Prediction Using Hybrid Machine Learning Models. Water. 2020; 12 (10):2951.
Chicago/Turabian StyleAssefa M. Melesse; Khabat Khosravi; John P. Tiefenbacher; Salim Heddam; Sungwon Kim; Amir Mosavi; Binh Thai Pham. 2020. "River Water Salinity Prediction Using Hybrid Machine Learning Models." Water 12, no. 10: 2951.
Shear stress distribution prediction in open channels is of utmost importance in hydraulic structural engineering as it directly affects the design of stable channels. In this study, at first, a series of experimental tests were conducted to assess the shear stress distribution in prismatic compound channels. The shear stress values around the whole wetted perimeter were measured in the compound channel with different floodplain widths also in different flow depths in subcritical and supercritical conditions. A set of, data mining and machine learning algorithms including Random Forest (RF), M5P, Random Committee, KStar and Additive Regression implemented on attained data to predict the shear stress distribution in the compound channel. Results indicated among these five models; RF method indicated the most precise results with the highest R2 value of 0.9. Finally, the most powerful data mining method which studied in this research compared with two well-known analytical models of Shiono and Knight method (SKM) and Shannon method to acquire the proposed model functioning in predicting the shear stress distribution. The results showed that the RF model has the best prediction performance compared to SKM and Shannon models.
Zohreh Sheikh Khozani; Khabat Khosravi; MohammadAmin Torabi; Amir Mosavi; Bahram Rezaei; Timon Rabczuk. Shear stress distribution prediction in symmetric compound channels using data mining and machine learning models. Frontiers of Structural and Civil Engineering 2020, 14, 1097 -1109.
AMA StyleZohreh Sheikh Khozani, Khabat Khosravi, MohammadAmin Torabi, Amir Mosavi, Bahram Rezaei, Timon Rabczuk. Shear stress distribution prediction in symmetric compound channels using data mining and machine learning models. Frontiers of Structural and Civil Engineering. 2020; 14 (5):1097-1109.
Chicago/Turabian StyleZohreh Sheikh Khozani; Khabat Khosravi; MohammadAmin Torabi; Amir Mosavi; Bahram Rezaei; Timon Rabczuk. 2020. "Shear stress distribution prediction in symmetric compound channels using data mining and machine learning models." Frontiers of Structural and Civil Engineering 14, no. 5: 1097-1109.
Reliable flash flood susceptibility maps are a vital tool for land planners and emergency management officials for early flood warning and mitigation. We have developed a new ensemble learning model that predicts flash flood susceptibility at Haraz, Iran. The new model couples a Bayesian Belief Network (BBN) model with an extreme learning machine (ELM) and backpropagation (BP) structure optimized by a genetic algorithm (GA) named GA-BN-NN model. We applied the support vector machine (SVM) technique to a database of 194 flood locations with ten conditioning factors. An artificial neural network (ANN) algorithm with a multi-layer perceptron function, MLP-BP, optimized by a genetic algorithm, GA-MLP, and a shuffled frog-leaping algorithm, SFLA-MLP, were used as benchmark models for assessing the power prediction of the proposed model. Statistical measures, including sensitivity, specificity, accuracy, F1-measure and Jaccard coefficient, and root mean square error, were used to evaluate the goodness-of-fit and prediction accuracy, respectively, of the training and testing datasets. We found that all ten factors are positively correlated with flood occurrence, but slope angle has the highest average merit (AM=9.7) and thus contributes most to the occurrence of flooding. Results indicate that the GA-BN-NN model has the highest goodness-of-fit and prediction accuracy (AUC=0.966) and hence outperforms other ensemble learning models that we tested — the SFLA-MLP, MLP-BP, and GA-MLP models. We thus conclude that the proposed model is a promising technique for managing risk in flood-prone areas around the world.
Ataollah Shirzadi; Shahrokh Asadi; Himan Shahabi; Somayeh Ronoud; John J. Clague; Khabat Khosravi; Binh Thai Pham; Baharin Bin Ahmad; Dieu Tien Bui. A novel ensemble learning based on Bayesian Belief Network coupled with an extreme learning machine for flash flood susceptibility mapping. Engineering Applications of Artificial Intelligence 2020, 96, 103971 .
AMA StyleAtaollah Shirzadi, Shahrokh Asadi, Himan Shahabi, Somayeh Ronoud, John J. Clague, Khabat Khosravi, Binh Thai Pham, Baharin Bin Ahmad, Dieu Tien Bui. A novel ensemble learning based on Bayesian Belief Network coupled with an extreme learning machine for flash flood susceptibility mapping. Engineering Applications of Artificial Intelligence. 2020; 96 ():103971.
Chicago/Turabian StyleAtaollah Shirzadi; Shahrokh Asadi; Himan Shahabi; Somayeh Ronoud; John J. Clague; Khabat Khosravi; Binh Thai Pham; Baharin Bin Ahmad; Dieu Tien Bui. 2020. "A novel ensemble learning based on Bayesian Belief Network coupled with an extreme learning machine for flash flood susceptibility mapping." Engineering Applications of Artificial Intelligence 96, no. : 103971.
Iran experiences frequent destructive floods with significant socioeconomic consequences. Quantifying the accurate impacts of such natural hazards, however, is a complicated task. The present study uses a deep learning convolutional neural networks (CNN) algorithm, which is among the newer and most powerful algorithms in big data sets, to develop a flood susceptibility map for Iran. A total of 2769 records were collected from flood locations across the entire country; we divided this data set into two groups using a cross-validation technique. The first group, used as a training data set, was constructed from 70% of the data set and was used for model building. The second group, used as a testing data set, was constructed from the remaining 30% of the records and used for validation. Ten flood conditioning factors, slope, altitude, aspect, plan curvature, profile curvature, rainfall, geology, land use, distance from roads, and distance from rivers, were identified and used in the modeling process. The area under the prediction-rate curve was used for model evaluation, with results showing that the flood susceptibility map has an acceptable accuracy of 75%. The results also indicated that approximately 12% and 3% of the country are highly and very highly susceptible to future flooding events, respectively. Moreover, 29% and 49% of Iran’s cities are located in areas with high and very high susceptibility to future flooding hazards. The most effective approaches to flood mitigation are preventing urban expansion and new construction in highly to very highly flood-prone areas as well as watershed management plans and constructing flood control structures according to the topographical characteristics of the catchment.
Khabat Khosravi; Mahdi Panahi; Ali Golkarian; Saskia D. Keesstra; Patricia M. Saco; Dieu Tien Bui; Saro Lee. Convolutional neural network approach for spatial prediction of flood hazard at national scale of Iran. Journal of Hydrology 2020, 591, 125552 .
AMA StyleKhabat Khosravi, Mahdi Panahi, Ali Golkarian, Saskia D. Keesstra, Patricia M. Saco, Dieu Tien Bui, Saro Lee. Convolutional neural network approach for spatial prediction of flood hazard at national scale of Iran. Journal of Hydrology. 2020; 591 ():125552.
Chicago/Turabian StyleKhabat Khosravi; Mahdi Panahi; Ali Golkarian; Saskia D. Keesstra; Patricia M. Saco; Dieu Tien Bui; Saro Lee. 2020. "Convolutional neural network approach for spatial prediction of flood hazard at national scale of Iran." Journal of Hydrology 591, no. : 125552.
Dam break flows and resulting river bed erosion can have disastrous impacts on human safety, infrastructure, and environmental quality. However, there is a lack of research on the mobility of non-uniform sediment mixtures resulting from dam break flows and how these differ from uniform sized sediment. In this paper, laboratory flume experiments revealed that coarse and fine fractions in non-uniform sediment had a higher and a lower bed-load parameter, respectively, than uniform sediments of the same size. Thus, the finer fractions were more stable and the coarser fractions were more erodible in a non-uniform bed compared to a uniform-grained bed. These differences can be explained by the hiding and protrusion of these fractions, respectively. By investigating changes in mobility of the mixed-size fractions with reservoir water levels, the results revealed that at low water levels, when the coarser fractions were only just mobile, the bed-load parameter of the finer fractions was higher than the coarser fractions. The opposite was observed at a higher water level, when a significant proportion of the coarsest fractions was mobilized. The higher protrusion of these grains had an important effect on their mobility relative to the finer grains. The transported sediment on these mixed-sized beds was coarser than the initial bed sediment, and became coarser with an increase in reservoir water level.
Khabat Khosravi; Amir Hooshang Nezamivand Chegini; James R. Cooper; Luca Mao; Mahmood Habibnejad; Kaka Shahedi; Andrew D. Binns. A laboratory investigation of bed-load transport of gravel sediments under dam break flow. International Journal of Sediment Research 2020, 36, 229 -234.
AMA StyleKhabat Khosravi, Amir Hooshang Nezamivand Chegini, James R. Cooper, Luca Mao, Mahmood Habibnejad, Kaka Shahedi, Andrew D. Binns. A laboratory investigation of bed-load transport of gravel sediments under dam break flow. International Journal of Sediment Research. 2020; 36 (2):229-234.
Chicago/Turabian StyleKhabat Khosravi; Amir Hooshang Nezamivand Chegini; James R. Cooper; Luca Mao; Mahmood Habibnejad; Kaka Shahedi; Andrew D. Binns. 2020. "A laboratory investigation of bed-load transport of gravel sediments under dam break flow." International Journal of Sediment Research 36, no. 2: 229-234.
The identification of landslide-prone areas is an essential step in landslide hazard assessment and mitigation of landslide-related losses. In this study, we applied two novel deep learning algorithms, the recurrent neural network (RNN) and convolutional neural network (CNN), for national-scale landslide susceptibility mapping of Iran. We prepared a dataset comprising 4069 historical landslide locations and 11 conditioning factors (altitude, slope degree, profile curvature, distance to river, aspect, plan curvature, distance to road, distance to fault, rainfall, geology and land-sue) to construct a geospatial database and divided the data into the training and the testing dataset. We then developed RNN and CNN algorithms to generate landslide susceptibility maps of Iran using the training dataset. We calculated the receiver operating characteristic (ROC) curve and used the area under the curve (AUC) for the quantitative evaluation of the landslide susceptibility maps using the testing dataset. Better performance in both the training and testing phases was provided by the RNN algorithm (AUC = 0.88) than by the CNN algorithm (AUC=0.85). Finally, we calculated areas of susceptibility for each province and found that 6% and 14% of the land area of Iran is very highly and highly susceptible to future landslide events, respectively, with the highest susceptibility in Chaharmahal and Bakhtiari Province (33.8%). About 31% of cities of Iran are located in areas with high and very high landslide susceptibility. The results of the present study will be useful for the development of landslide hazard mitigation strategies.
Phuong Thao Thi Ngo; Mahdi Panahi; Khabat Khosravi; Omid Ghorbanzadeh; Narges Kariminejad; Artemi Cerda; Saro Lee. Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran. Geoscience Frontiers 2020, 12, 505 -519.
AMA StylePhuong Thao Thi Ngo, Mahdi Panahi, Khabat Khosravi, Omid Ghorbanzadeh, Narges Kariminejad, Artemi Cerda, Saro Lee. Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran. Geoscience Frontiers. 2020; 12 (2):505-519.
Chicago/Turabian StylePhuong Thao Thi Ngo; Mahdi Panahi; Khabat Khosravi; Omid Ghorbanzadeh; Narges Kariminejad; Artemi Cerda; Saro Lee. 2020. "Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran." Geoscience Frontiers 12, no. 2: 505-519.
Khabat Khosravi; Amir H.N. Chegini; James R. Cooper; Prasad Daggupati; Andrew Binns; Luca Mao. ‘Corrigendum to “Uniform and graded bed-load sediment transport in a degrading channel with non-equilibrium conditions” [International Journal of Sediment Research 35 (2020) 115–124/04-258]’. International Journal of Sediment Research 2020, 36, 163 .
AMA StyleKhabat Khosravi, Amir H.N. Chegini, James R. Cooper, Prasad Daggupati, Andrew Binns, Luca Mao. ‘Corrigendum to “Uniform and graded bed-load sediment transport in a degrading channel with non-equilibrium conditions” [International Journal of Sediment Research 35 (2020) 115–124/04-258]’. International Journal of Sediment Research. 2020; 36 (1):163.
Chicago/Turabian StyleKhabat Khosravi; Amir H.N. Chegini; James R. Cooper; Prasad Daggupati; Andrew Binns; Luca Mao. 2020. "‘Corrigendum to “Uniform and graded bed-load sediment transport in a degrading channel with non-equilibrium conditions” [International Journal of Sediment Research 35 (2020) 115–124/04-258]’." International Journal of Sediment Research 36, no. 1: 163.
The predictive capability of a new artificial intelligence method, random subspace (RS), for the prediction of suspended sediment load in rivers was compared with commonly used methods: random forest (RF) and two support vector machine (SVM) models using a radial basis function kernel (SVM-RBF) and a normalized polynomial kernel (SVM-NPK). Using river discharge, rainfall and river stage data from the Haraz River, Iran, the results revealed: (a) the RS model provided a superior predictive accuracy (NSE = 0.83) to SVM-RBF (NSE = 0.80), SVM-NPK (NSE = 0.78) and RF (NSE = 0.68), corresponding to very good, good, satisfactory and unsatisfactory accuracies in load prediction; (b) the RBF kernel outperformed the NPK kernel; (c) the predictive capability was most sensitive to gamma and epsilon in SVM models, maximum depth of a tree and the number of features in RF models, classifier type, number of trees and subspace size in RS models; and (d) suspended sediment loads were most closely correlated with river discharge (PCC = 0.76). Overall, the results show that RS models have great potential in data poor watersheds, such as that studied here, to produce strong predictions of suspended load based on monthly records of river discharge, rainfall depth and river stage alone.
Viet-Ha Nhu; Khabat Khosravi; James R. Cooper; Mahshid Karimi; Ozgur Kisi; Binh Thai Pham; Zongjie Lyu. Monthly suspended sediment load prediction using artificial intelligence: testing of a new random subspace method. Hydrological Sciences Journal 2020, 65, 2116 -2127.
AMA StyleViet-Ha Nhu, Khabat Khosravi, James R. Cooper, Mahshid Karimi, Ozgur Kisi, Binh Thai Pham, Zongjie Lyu. Monthly suspended sediment load prediction using artificial intelligence: testing of a new random subspace method. Hydrological Sciences Journal. 2020; 65 (12):2116-2127.
Chicago/Turabian StyleViet-Ha Nhu; Khabat Khosravi; James R. Cooper; Mahshid Karimi; Ozgur Kisi; Binh Thai Pham; Zongjie Lyu. 2020. "Monthly suspended sediment load prediction using artificial intelligence: testing of a new random subspace method." Hydrological Sciences Journal 65, no. 12: 2116-2127.