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Management of available water resources needs good planning and to do this, prognostication of hydrological parameters (parameters of the hydrological cycle such as rainfall, runoff, solar radiation, groundwater, evaporation/evapotranspiration)
Ozgur Kisi. Machine Learning with Metaheuristic Algorithms for Sustainable Water Resources Management. Sustainability 2021, 13, 8596 .
AMA StyleOzgur Kisi. Machine Learning with Metaheuristic Algorithms for Sustainable Water Resources Management. Sustainability. 2021; 13 (15):8596.
Chicago/Turabian StyleOzgur Kisi. 2021. "Machine Learning with Metaheuristic Algorithms for Sustainable Water Resources Management." Sustainability 13, no. 15: 8596.
Determination of wetting patterns’ dimensions is essential in designing and managing surface/subsurface drip irrigation systems. The laboratory experiments were conducted using physical model with dimensions of 3 × 1 × 0.5 m3 to evaluate the moisture redistribution process under continuous and pulse surface/subsurface irrigation systems. In the present study, the efficiency of a new machine learning method, named fuzzy c-means clustering- based adaptive neural-fuzzy inference system combined with a new meta-heuristic algorithm, hybrid particle swarm optimization – gravity search algorithm (ANFIS-FCM-PSOGSA), is investigated in order to model wetting front redistribution of drip irrigation systems (IS) using soil and system parameters as inputs under continuous and pulse surface/subsurface IS. The outcomes of the assessed method are compared with those of the ANFIS-FCM-PSO, generalized regression neural networks and multivariate adaptive regression splines. In assessing the implemented methods, four commonly used indices, root mean square errors (RMSE), mean absolute error (MAE), coefficient of determination (R2), Nash-Sutcliffe model efficiency (NSE) and graphical methods (e.g., scatter, box plot and Taylor diagrams) are utilized. The benchmark outcomes demonstrate the superiority of new method in estimating wetting front dimensions by improving the accuracy of the ANFIS-FCM-PSO by 29.6%, 18.5%, 6.1%, and 9.0% in estimating the diameter of horizontal redistribution with respect to RMSE, MAE, R2 and NSE, respectively. Furthermore, the ANFIS-FCM-PSOGSA respectively improves the RMSE, MAE, R2 and NSE accuracy of the ANFIS-FCM-PSO by 20.1%, 19.2%, 35.7% and 35.6% in estimating the diameter of downward vertical redistribution. The general outcomes recommend the use of new method in estimating wetting front dimensions of drip irrigation systems.
Ozgur Kisi; Payam Khosravinia; Salim Heddam; Bakhtiar Karimi; Nazir Karimi. Modeling wetting front redistribution of drip irrigation systems using a new machine learning method: Adaptive neuro- fuzzy system improved by hybrid particle swarm optimization – Gravity search algorithm. Agricultural Water Management 2021, 256, 107067 .
AMA StyleOzgur Kisi, Payam Khosravinia, Salim Heddam, Bakhtiar Karimi, Nazir Karimi. Modeling wetting front redistribution of drip irrigation systems using a new machine learning method: Adaptive neuro- fuzzy system improved by hybrid particle swarm optimization – Gravity search algorithm. Agricultural Water Management. 2021; 256 ():107067.
Chicago/Turabian StyleOzgur Kisi; Payam Khosravinia; Salim Heddam; Bakhtiar Karimi; Nazir Karimi. 2021. "Modeling wetting front redistribution of drip irrigation systems using a new machine learning method: Adaptive neuro- fuzzy system improved by hybrid particle swarm optimization – Gravity search algorithm." Agricultural Water Management 256, no. : 107067.
Total organic carbon (TOC) has vital significance for measuring water quality in river streamflow. The detection of TOC can be considered as an important evaluation because of issues on human health and environmental indicators. This research utilized the novel hybrid models to improve the predictive accuracy of TOC at Andong and Changnyeong stations in the Nakdong River, South Korea. A data pre-processing approach (i.e., complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)) and evolutionary optimization algorithm (i.e., crow search algorithm (CSA)) were implemented for enhancing the accuracy and robustness of standalone models (i.e., multivariate adaptive regression spline (MARS) and M5Tree). Various water quality indicators (i.e., TOC, potential of Hydrogen (pH), electrical conductivity (EC), dissolved oxygen (DO), water temperature (WT), chemical oxygen demand (COD), and suspended solids (SS)) were utilized for developing the standalone and hybrid models based on three input combinations (i.e., categories 1~3). The developed models were evaluated utilizing the correlation coefficient (CC), root-mean-square error (RMSE), and Nash-Sutcliffe efficiency (NSE). The CEEMDAN-MARS-CSA based on category 2 (C-M-CSA2) model (CC = 0.762, RMSE = 0.570 mg/L, and NSE = 0.520) was the most accurate for predicting TOC at Andong station, whereas the CEEMDAN-MARS-CSA based on category 3 (C-M-CSA3) model (CC = 0.900, RMSE = 0.675 mg/L, and NSE = 0.680) was the best at Changnyeong station.
Sungwon Kim; Niloofar Maleki; Mohammad Rezaie-Balf; Vijay P. Singh; Meysam Alizamir; Nam Won Kim; Jong-Tak Lee; Ozgur Kisi. Assessment of the total organic carbon employing the different nature-inspired approaches in the Nakdong River, South Korea. Environmental Monitoring and Assessment 2021, 193, 1 -22.
AMA StyleSungwon Kim, Niloofar Maleki, Mohammad Rezaie-Balf, Vijay P. Singh, Meysam Alizamir, Nam Won Kim, Jong-Tak Lee, Ozgur Kisi. Assessment of the total organic carbon employing the different nature-inspired approaches in the Nakdong River, South Korea. Environmental Monitoring and Assessment. 2021; 193 (7):1-22.
Chicago/Turabian StyleSungwon Kim; Niloofar Maleki; Mohammad Rezaie-Balf; Vijay P. Singh; Meysam Alizamir; Nam Won Kim; Jong-Tak Lee; Ozgur Kisi. 2021. "Assessment of the total organic carbon employing the different nature-inspired approaches in the Nakdong River, South Korea." Environmental Monitoring and Assessment 193, no. 7: 1-22.
Accurate estimation of suspended sediment (SS) is very essential for planning and management of hydraulic structures. The study investigates the accuracy of four machine learning methods, dynamic evolving neural-fuzzy inference systems (DENFIS), fuzzy c-means based adaptive neuro fuzzy system (ANFIS-FCM), multivariate adaptive regression spline (MARS) and M5 model tree (M5Tree), in estimating suspended sediments. Several input scenarios including streamflow (Q) and sediment (S) data obtained from Ain Hamara Station in Wadi Abd basin, Algeria were constructed to find the most effective one. The research results indicate that the DENFIS model with current streamflow (Qt) and 1 previous sediment (St-1) values performs superior to the other alternatives in SS estimation; it increases the efficiency of the best ANFIS-FCM, MARS and M5Tree by 1.6%, 15.7% and 9.6% with respect to RMSE (root mean square error), respectively. Variation of Q and S data on models’ estimation ability was also investigated and it was found that the variation input considerably increase the prediction ability of MARS method; increments in RMSE and MAE (mean absolute error) are by 10.8 and 4.9% and decrement in NSE (Nash-Sutcliffe efficiency) is by 12.9%.
Achite Mohammed; Zaher Mundher Yaseen; Salim Heddam; Anurag Malik; Ozgur Kisi. Advanced machine learning models development for suspended sediment prediction: Comparative analysis study. Geocarto International 2021, 1 -25.
AMA StyleAchite Mohammed, Zaher Mundher Yaseen, Salim Heddam, Anurag Malik, Ozgur Kisi. Advanced machine learning models development for suspended sediment prediction: Comparative analysis study. Geocarto International. 2021; ():1-25.
Chicago/Turabian StyleAchite Mohammed; Zaher Mundher Yaseen; Salim Heddam; Anurag Malik; Ozgur Kisi. 2021. "Advanced machine learning models development for suspended sediment prediction: Comparative analysis study." Geocarto International , no. : 1-25.
The development of advanced computational models for improving the accuracy of streamflow forecasting could save time and cost for sustainable water resource management. In this study, a locally weighted learning (LWL) algorithm is combined with the Additive Regression (AR), Bagging (BG), Dagging (DG), Random Subspace (RS), and Rotation Forest (RF) ensemble techniques for the streamflow forecasting in the Jhelum Catchment, Pakistan. To build the models, we grouped the initial parameters into four different scenarios (M1–M4) of input data with a five-fold cross-validation (I–V) approach. To evaluate the accuracy of the developed ensemble models, previous lagged values of streamflow were used as inputs whereas the cross-validation technique and periodicity input were used to examine prediction accuracy on the basis of root correlation coefficient (R), root mean squared error (RMSE), mean absolute error (MAE), relative absolute error (RAE), and root relative squared error (RRSE). The results showed that the incorporation of periodicity (i.e., MN) as an additional input variable considerably improved both the training performance and predictive performance of the models. A comparison between the results obtained from the input combinations III and IV revealed a significant performance improvement. The cross-validation revealed that the dataset M3 provided more accurate results compared to the other datasets. While all the ensemble models successfully outperformed the standalone LWL model, the ensemble LWL-AR model was identified as the best model. Our study demonstrated that the ensemble modeling approach is a robust and promising alternative to the single forecasting of streamflow that should be further investigated with different datasets from other regions around the world.
Rana Adnan; Abolfazl Jaafari; Aadhityaa Mohanavelu; Ozgur Kisi; Ahmed Elbeltagi. Novel Ensemble Forecasting of Streamflow Using Locally Weighted Learning Algorithm. Sustainability 2021, 13, 5877 .
AMA StyleRana Adnan, Abolfazl Jaafari, Aadhityaa Mohanavelu, Ozgur Kisi, Ahmed Elbeltagi. Novel Ensemble Forecasting of Streamflow Using Locally Weighted Learning Algorithm. Sustainability. 2021; 13 (11):5877.
Chicago/Turabian StyleRana Adnan; Abolfazl Jaafari; Aadhityaa Mohanavelu; Ozgur Kisi; Ahmed Elbeltagi. 2021. "Novel Ensemble Forecasting of Streamflow Using Locally Weighted Learning Algorithm." Sustainability 13, no. 11: 5877.
Recent evidence of regional climate change impacts on hydrological cycle directed us to study the floods in a high elevated and rapidly urbanized river basin, the Kabul River basin (KRB), Pakistan, which is susceptible to frequent flooding. Therefore, we analyzed the changes in flood regime at various spatial and temporal scales and their possible causes, which is accomplished by using flood indicators, trend analysis, change point analysis, and hydrological modeling. The results showed that the northern and northwestern parts of the KRB were more exposed to flood hazard than the southern parts under long-term scenario (1961/64-2015). However, after the change points, the flood risk decreased in the northern and increased in the southern regions. This spatial shift increased the vulnerability of population to the flood hazard, because the majority of population resides in the southern region. The extreme precipitation has also increased, especially the maximum one-day rainfall and maximum five-day rainfall throughout the basin. Particularly, the major cause of the decrease in different flood indicators in the northern parts of the KRB is the corresponding decrease in the annual and monsoonal rainfall and corresponding positive mass balance of glaciers in the northern region after the occurrence of change point in flood regime. However, the major cause of the increase in flood hazard on the southern part of the KRB is associated with maximum five-day rainfall. A 68% variability of annual maximum flood for the Kabul River at Nowshera and an 84% variability of annual maximum flood for Bara River at Jhansi post are explained by maximum five-day rainfall. In addition, a considerable decrease in forests (–5.21%) and increase in the urban area (88.26%) from 1992–2015 also amplifies the risk of higher flood peaks. The results of hydrological modeling suggest that the six-hourly flood peak increased by 6.85% (1992–2010) and 4.81% (2010–2015) for the extreme flood of 2010 for the Kabul River at Nowshera. The flood peak per decade will increase by 8.6%, as compared to the flood peak under the land use scenario of 2010. Therefore, consideration of proper land use planning is crucial for sustainable flood management in the KRB.
Asif Mehmood; Shaofeng Jia; Aifeng Lv; Wenbin Zhu; Rashid Mahmood; Muhammad Saifullah; Rana Adnan. Detection of Spatial Shift in Flood Regime of the Kabul River Basin in Pakistan, Causes, Challenges, and Opportunities. Water 2021, 13, 1276 .
AMA StyleAsif Mehmood, Shaofeng Jia, Aifeng Lv, Wenbin Zhu, Rashid Mahmood, Muhammad Saifullah, Rana Adnan. Detection of Spatial Shift in Flood Regime of the Kabul River Basin in Pakistan, Causes, Challenges, and Opportunities. Water. 2021; 13 (9):1276.
Chicago/Turabian StyleAsif Mehmood; Shaofeng Jia; Aifeng Lv; Wenbin Zhu; Rashid Mahmood; Muhammad Saifullah; Rana Adnan. 2021. "Detection of Spatial Shift in Flood Regime of the Kabul River Basin in Pakistan, Causes, Challenges, and Opportunities." Water 13, no. 9: 1276.
The accurate estimation of suspended sediments (SSs) carries significance in determining the volume of dam storage, river carrying capacity, pollution susceptibility, soil erosion potential, aquatic ecological impacts, and the design and operation of hydraulic structures. The presented study proposes a new method for accurately estimating daily SSs using antecedent discharge and sediment information. The novel method is developed by hybridizing the multivariate adaptive regression spline (MARS) and the Kmeans clustering algorithm (MARS–KM). The proposed method’s efficacy is established by comparing its performance with the adaptive neuro-fuzzy system (ANFIS), MARS, and M5 tree (M5Tree) models in predicting SSs at two stations situated on the Yangtze River of China, according to the three assessment measurements, RMSE, MAE, and NSE. Two modeling scenarios are employed; data are divided into 50–50% for model training and testing in the first scenario, and the training and test data sets are swapped in the second scenario. In Guangyuan Station, the MARS–KM showed a performance improvement compared to ANFIS, MARS, and M5Tree methods in term of RMSE by 39%, 30%, and 18% in the first scenario and by 24%, 22%, and 8% in the second scenario, respectively, while the improvement in RMSE of ANFIS, MARS, and M5Tree was 34%, 26%, and 27% in the first scenario and 7%, 16%, and 6% in the second scenario, respectively, at Beibei Station. Additionally, the MARS–KM models provided much more satisfactory estimates using only discharge values as inputs.
Rana Adnan; Kulwinder Parmar; Salim Heddam; Shamsuddin Shahid; Ozgur Kisi. Suspended Sediment Modeling Using a Heuristic Regression Method Hybridized with Kmeans Clustering. Sustainability 2021, 13, 4648 .
AMA StyleRana Adnan, Kulwinder Parmar, Salim Heddam, Shamsuddin Shahid, Ozgur Kisi. Suspended Sediment Modeling Using a Heuristic Regression Method Hybridized with Kmeans Clustering. Sustainability. 2021; 13 (9):4648.
Chicago/Turabian StyleRana Adnan; Kulwinder Parmar; Salim Heddam; Shamsuddin Shahid; Ozgur Kisi. 2021. "Suspended Sediment Modeling Using a Heuristic Regression Method Hybridized with Kmeans Clustering." Sustainability 13, no. 9: 4648.
Reference evapotranspiration (ETo) is one of the foremost elements of the hydrology cycle which is essential for water resources management and irrigation applications. The current study is emphasized on the implementation of evolutionary computing models (i.e., gene expression programming (GEP)) for the simulation daily ETo in different locations of Peninsular Malaysia. The ETo models are developed using various input combinations of meteorological variables including air temperature (mean, maximum, and minimum), relative humidity, solar radiation, and mean wind speed. The in situ measurements of the ET are used to validate the model’s performance. The performance of the proposed GEP model is also compared with five well-established empirical formulations (EFs) developed based on the related climatological variability. The attained results evidenced the potential of GEP-derived ETo models in terms of all the statistical measures used. The best GEP model attained when all the meteorological variables are incorporated. However, the study revealed that the use of only temperature information can provide substantial predictability compared to EFs at all the studied stations across Peninsular Malaysia. This confirms the applicability of GEP in simulating ETo with fewer meteorological variables. The major advantage of GEP compared to other black box artificial intelligence algorithms is that GEP provides a set of equations which can be used by practitioners for reliable estimation of ETo at field with a fewer meteorological variable and, thus, can have wide applicability in water resources management.
Mohd Khairul Idlan Muhammad; Shamsuddin Shahid; Tarmizi Ismail; Sobri Harun; Ozgur Kisi; Zaher Mundher Yaseen. The development of evolutionary computing model for simulating reference evapotranspiration over Peninsular Malaysia. Theoretical and Applied Climatology 2021, 144, 1419 -1434.
AMA StyleMohd Khairul Idlan Muhammad, Shamsuddin Shahid, Tarmizi Ismail, Sobri Harun, Ozgur Kisi, Zaher Mundher Yaseen. The development of evolutionary computing model for simulating reference evapotranspiration over Peninsular Malaysia. Theoretical and Applied Climatology. 2021; 144 (3):1419-1434.
Chicago/Turabian StyleMohd Khairul Idlan Muhammad; Shamsuddin Shahid; Tarmizi Ismail; Sobri Harun; Ozgur Kisi; Zaher Mundher Yaseen. 2021. "The development of evolutionary computing model for simulating reference evapotranspiration over Peninsular Malaysia." Theoretical and Applied Climatology 144, no. 3: 1419-1434.
This study employs two heuristic algorithms, including the genetic algorithm (GA) and ant colony optimization for continuous domains (ACOR), for optimizing the parameters of two soft computing models, namely adaptive neuro-fuzzy inference system (ANFIS) and least-squares support vector machine (LSSVM), which were used for modeling monthly precipitation for all 12 months of the year. Data from 40 meteorological stations situated in different parts of Iran were used. The effectiveness of input data was determined by internal correlation-coefficient and nonlinear sensitivity analysis. Selected input data were further evaluated by another sensitivity analysis method, cosine amplitude (CA). Considering different evaluation months, LSSVM was more accurate and reliable than ANFIS. It was also found that both algorithms improved the performance of models for most months of the year. ACOR was better and more reliable than was GA in optimizing the models. ACOR produced the best results in autumn that led to the improvement of performance of ANFIS in terms of correlation coefficient (R) and root-mean square error (RMSE) by 35% and 0.40 mm for October; 42% and 0.99 mm for November; and 31% and 0.74 mm for December. The performance of LSSVM was enhanced by 6% and 0.28 mm for October; 22% and 0.20 mm for November; and 4% and 0.10 mm for December, respectively. For July and August, the suggested algorithms could not improve the performance of ANFIS. The algorithms did optimize LSSVM in all months, so the RMSE and mean absolute error were improved by 0.15 and 0.28 mm for July and 0.28 and 0.56 mm for August, respectively.
Armin Azad; Saeed Farzin; Hadi Sanikhani; Hojat Karami; Ozgur Kisi; Vijay P. Singh. Approaches for Optimizing the Performance of Adaptive Neuro-Fuzzy Inference System and Least-Squares Support Vector Machine in Precipitation Modeling. Journal of Hydrologic Engineering 2021, 26, 04021010 .
AMA StyleArmin Azad, Saeed Farzin, Hadi Sanikhani, Hojat Karami, Ozgur Kisi, Vijay P. Singh. Approaches for Optimizing the Performance of Adaptive Neuro-Fuzzy Inference System and Least-Squares Support Vector Machine in Precipitation Modeling. Journal of Hydrologic Engineering. 2021; 26 (4):04021010.
Chicago/Turabian StyleArmin Azad; Saeed Farzin; Hadi Sanikhani; Hojat Karami; Ozgur Kisi; Vijay P. Singh. 2021. "Approaches for Optimizing the Performance of Adaptive Neuro-Fuzzy Inference System and Least-Squares Support Vector Machine in Precipitation Modeling." Journal of Hydrologic Engineering 26, no. 4: 04021010.
The study investigates accuracy of two machine learning methods, neuro-fuzzy system with grid partition (ANFIS-GP) and multivariate adaptive regression spline (MARS) in prediction of 1-day- to 6-day-ahead groundwater levels (GWLs) using data from two wells, USA. The outcomes indicate that the ANFIS-GP provides inferior results compared to regression-based simple MARS method. The MARS method which is much simpler than the ANFIS-GP is recommended for short-term GWL prediction.
Ozgur Kisi; Hadi Sanikhani. Modeling Short-Term Groundwater-Level Fluctuations Using Multivariate Adaptive Regression Spline. Plant-Microbes-Engineered Nano-particles (PM-ENPs) Nexus in Agro-Ecosystems 2021, 195 -199.
AMA StyleOzgur Kisi, Hadi Sanikhani. Modeling Short-Term Groundwater-Level Fluctuations Using Multivariate Adaptive Regression Spline. Plant-Microbes-Engineered Nano-particles (PM-ENPs) Nexus in Agro-Ecosystems. 2021; ():195-199.
Chicago/Turabian StyleOzgur Kisi; Hadi Sanikhani. 2021. "Modeling Short-Term Groundwater-Level Fluctuations Using Multivariate Adaptive Regression Spline." Plant-Microbes-Engineered Nano-particles (PM-ENPs) Nexus in Agro-Ecosystems , no. : 195-199.
Hydrological models play a crucial role in water planning and decision making. Machine Learning-based models showed several drawbacks for frequent high and a wide range of streamflow records. These models also experience problems during the training process such as over-fitting or trapping in searching for global optima To overcome these limitations, the current study attempts to hybridize the recently developed physics-inspired metaheuristic algorithms (MHAs) such as Equilibrium Optimization (EO), Henry Gases Solubility Optimization (HGSO), and Nuclear Reaction Optimization(NRO) with Multi-layer Perceptron (MLP). These models’ accuracy will be inspected to solve the streamflow forecasting problem where the streamflow dataset was collected through 130 years from a station located on the High Aswan Dam (HAD). The performance of proposed models then will be compared with two traditional neural network models(MLP and RNN), and nine well-known hybrid MLP-based models belong to the different branches of the metaheuristic field (evolutionary group, swarm group, and physics group). The internal parameters of the proposed models will be initialized and optimized. Different performance metrics will be used to examine the performance of the proposed models. The stability of the proposed models and the convergence speed will be evaluated. Finally, ranking these models based on different performance evaluations will be carried out. The results show that the models in the group of Physics-MLP are more reliable in capturing the streamflow patterns, followed by the Swarm-MLP group and then by the Evolutionary-MLP group. Finally, among the all employed methods, the NRO has the best accuracy with the lowest RMSE(2.35), MAE(1.356), MAPE(16.747), and the highest WI(0.957), R(0.924), and confidence in forecasting the streamflow of Aswan High Dam. It can be concluded that augmenting the NRO algorithm with MLP can be a reliable tool in forecasting the monthly streamflow with a high level of precision, speed convergence, and high constancy level.
Ali Najah Ahmed; To Van Lam; Nguyen Duy Hung; Nguyen Van Thieu; Ozgur Kisi; Ahmed El-Shafie. A comprehensive comparison of recent developed meta-heuristic algorithms for streamflow time series forecasting problem. Applied Soft Computing 2021, 105, 107282 .
AMA StyleAli Najah Ahmed, To Van Lam, Nguyen Duy Hung, Nguyen Van Thieu, Ozgur Kisi, Ahmed El-Shafie. A comprehensive comparison of recent developed meta-heuristic algorithms for streamflow time series forecasting problem. Applied Soft Computing. 2021; 105 ():107282.
Chicago/Turabian StyleAli Najah Ahmed; To Van Lam; Nguyen Duy Hung; Nguyen Van Thieu; Ozgur Kisi; Ahmed El-Shafie. 2021. "A comprehensive comparison of recent developed meta-heuristic algorithms for streamflow time series forecasting problem." Applied Soft Computing 105, no. : 107282.
Owing to the reduction of surface-water resources and frequent droughts, the exploitation of groundwater resources has faced critical challenges. For optimal management of these valuable resources, careful studies of groundwater potential status are essential. The main goal of this study was to determine the optimal network structure of a Bayesian network (BayesNet) machine-learning model using three metaheuristic optimization algorithms—a genetic algorithm (GA), a simulated annealing (SA) algorithm, and a Tabu search (TS) algorithm—to prepare groundwater-potential maps. The methodology was applied to the town of Baghmalek in the Khuzestan province of Iran. For modeling, the location of 187 springs in the study area and 13 parameters (altitude, slope angle, slope aspect, plan curvature, profile curvature, topography wetness index (TWI), distance to river, distance to fault, drainage density, rainfall, land use/cover, lithology, and soil) affecting the potential of groundwater were provided. In addition, the statistical method of certainty factor (CF) was utilized to determine the input weight of the hybrid models. The results of the OneR technique showed that the parameters of altitude, lithology, and drainage density were more important for the potential of groundwater compared to the other parameters. The results of groundwater-potential mapping (GPM) employing the receiver operating characteristic (ROC) area under the curve (AUC) showed an estimation accuracy of 0.830, 0.818, 0.810, and 0.792, for the BayesNet-GA, BayesNet-SA, BayesNet-TS, and BayesNet models, respectively. The BayesNet-GA model improved the GPM estimation accuracy of the BayesNet-SA (4.6% and 7.5%) and BayesNet-TS (21.8% and 17.5%) models with respect to the root mean square error (RMSE) and mean absolute error (MAE), respectively. Based on metric indices, the GA provides a higher capability than the SA and TS algorithms for optimizing the BayesNet model in determining the GPM.
Sadegh Karimi-Rizvandi; Hamid Goodarzi; Javad Afkoueieh; Il-Moon Chung; Ozgur Kisi; Sungwon Kim; Nguyen Linh. Groundwater-Potential Mapping Using a Self-Learning Bayesian Network Model: A Comparison among Metaheuristic Algorithms. Water 2021, 13, 658 .
AMA StyleSadegh Karimi-Rizvandi, Hamid Goodarzi, Javad Afkoueieh, Il-Moon Chung, Ozgur Kisi, Sungwon Kim, Nguyen Linh. Groundwater-Potential Mapping Using a Self-Learning Bayesian Network Model: A Comparison among Metaheuristic Algorithms. Water. 2021; 13 (5):658.
Chicago/Turabian StyleSadegh Karimi-Rizvandi; Hamid Goodarzi; Javad Afkoueieh; Il-Moon Chung; Ozgur Kisi; Sungwon Kim; Nguyen Linh. 2021. "Groundwater-Potential Mapping Using a Self-Learning Bayesian Network Model: A Comparison among Metaheuristic Algorithms." Water 13, no. 5: 658.
Accurate short-term rainfall–runoff prediction is essential for flood mitigation and safety of hydraulic structures and infrastructures. This study investigates the capability of four machine learning methods (MLM), optimal pruning extreme learning machine (OPELM), multivariate adaptive regression spline (MARS), M5 model tree (M5Tree, and hybridized MARS and Kmeans algorithm (MARS-Kmeans), in hourly rainfall–runoff modeling (considering 1-, 6- and 12-h horizons). Their results are compared with a conceptual method, Event-Based Approach for Small and Ungauged Basins (EBA4SUB) and multi-linear regression (MLR). Hourly rainfall and runoff data gathered from Ilme River watershed, Germany, were divided into two equal parts, and MLM were validated considering each part by swapping training and testing datasets. MLM were compared with EBA4SUB using four events and with respect to three statistics, root-mean-square errors (RMSE), mean absolute error (MAE) and Nash–Sutcliffe efficiency (NSE). Comparison results revealed that the newly developed hybridized MARS-Kmeans method performed superior to the OPELM, MARS, M5Tree and MLR methods in prediction of 1-, 6- and 12-h ahead runoff. Comparison with conceptual method showed that all the machine learning models outperformed the EBA4SUB and OPELM provided slightly better performance than the other three alternatives in event-based rainfall–runoff modeling.
Rana Muhammad Adnan; Andrea Petroselli; Salim Heddam; Celso Augusto Guimarães Santos; Ozgur Kisi. Comparison of different methodologies for rainfall–runoff modeling: machine learning vs conceptual approach. Natural Hazards 2021, 105, 2987 -3011.
AMA StyleRana Muhammad Adnan, Andrea Petroselli, Salim Heddam, Celso Augusto Guimarães Santos, Ozgur Kisi. Comparison of different methodologies for rainfall–runoff modeling: machine learning vs conceptual approach. Natural Hazards. 2021; 105 (3):2987-3011.
Chicago/Turabian StyleRana Muhammad Adnan; Andrea Petroselli; Salim Heddam; Celso Augusto Guimarães Santos; Ozgur Kisi. 2021. "Comparison of different methodologies for rainfall–runoff modeling: machine learning vs conceptual approach." Natural Hazards 105, no. 3: 2987-3011.
The potential or reference evapotranspiration (ET0) is considered as one of the fundamental variables for irrigation management, agricultural planning, and modeling different hydrological pr°Cesses, and therefore, its accurate prediction is highly essential. The study validates the feasibility of new temperature based heuristic models (i.e., group method of data handling neural network (GMDHNN), multivariate adaptive regression spline (MARS), and M5 model tree (M5Tree)) for estimating monthly ET0. The outcomes of the newly developed models are compared with empirical formulations including Hargreaves-Samani (HS), calibrated HS, and Stephens-Stewart (SS) models based on mean absolute error (MAE), root mean square error (RMSE), and Nash-Sutcliffe efficiency. Monthly maximum and minimum temperatures (Tmax and Tmin) observed at two stations in Turkey are utilized as inputs for model development. In the applications, three data division scenarios are utilized and the effect of periodicity component (PC) on models’ accuracies are also examined. By importing PC into the model inputs, the RMSE accuracy of GMDHNN, MARS, and M5Tree models increased by 1.4%, 8%, and 6% in one station, respectively. The GMDHNN model with periodic input provides a superior performance to the other alternatives in both stations. The recommended model reduced the average error of MARS, M5Tree, HS, CHS, and SS models with respect to RMSE by 3.7–6.4%, 10.7–3.9%, 76–75%, 10–35%, and 0.8–17% in estimating monthly ET0, respectively. The HS model provides the worst accuracy while the calibrated version significantly improves its accuracy. The GMDHNN, MARS, M5Tree, SS, and CHS models are also compared in estimating monthly mean ET0. The GMDHNN generally gave the best accuracy while the CHS provides considerably over/under-estimations. The study indicated that the only one data splitting scenario may mislead the modeler and for better validation of the heuristic methods, more data splitting scenarios should be applied.
Rana Adnan; Salim Heddam; Zaher Yaseen; Shamsuddin Shahid; Ozgur Kisi; Binquan Li. Prediction of Potential Evapotranspiration Using Temperature-Based Heuristic Approaches. Sustainability 2020, 13, 297 .
AMA StyleRana Adnan, Salim Heddam, Zaher Yaseen, Shamsuddin Shahid, Ozgur Kisi, Binquan Li. Prediction of Potential Evapotranspiration Using Temperature-Based Heuristic Approaches. Sustainability. 2020; 13 (1):297.
Chicago/Turabian StyleRana Adnan; Salim Heddam; Zaher Yaseen; Shamsuddin Shahid; Ozgur Kisi; Binquan Li. 2020. "Prediction of Potential Evapotranspiration Using Temperature-Based Heuristic Approaches." Sustainability 13, no. 1: 297.
Precipitation deficit can affect different natural resources such as water, soil, rivers and plants, and cause meteorological, hydrological and agricultural droughts. Multivariate drought indexes can theoretically show the severity and weakness of various drought types simultaneously. This study introduces an approach for forecasting joint deficit index (JDI) and multivariate standardized precipitation index (MSPI) by using machine–learning methods and entropy theory. JDI and MSPI were calculated for the 1–12 months’ time window (JDI1–12 and MSPI1–12), using monthly precipitation data. The methods implemented for forecasting are group method of data handling (GMDH), generalized regression neural network (GRNN), least squared support vector machine (LSSVM), adaptive neuro-fuzzy inference system (ANFIS) and ANFIS optimized with three heuristic optimization algorithms, differential evolution (DE), genetic algorithm (GA) and particle swarm optimization (PSO) as meta-innovative methods (ANFIS-DE, ANFIS-GA and ANFIS-PSO). Monthly precipitation, monthly temperature and previous amounts of the index’s values were used as inputs to the models. Data from 10 synoptic stations situated in the widest climatic zone of Iran (extra arid-cold climate) were employed. Optimal model inputs were selected by gamma test and entropy theory. The evaluation results, which were given using mean absolute error (MAE), root mean squared error (RMSE) and Willmott index (WI), show that the machine learning and meta-innovative models can present acceptable forecasts of general drought’s conditions. The algorithms DE, GA and PSO, could improve the ANFIS’s performance by 39.4%, 38.7% and 22.6%, respectively. Among all the applied models, the GMDH shows the best forecasting accuracy with MAE = 0.280, RMSE = 0.374 and WI = 0.955. In addition, the models could forecast MSPI better than JDI in the majority of cases (stations). Among the two methods used to select the optimal inputs, it is difficult to select one as a better input selector, but according to the results, more attention can be paid to entropy theory in drought studies.
Pouya Aghelpour; Babak Mohammadi; Seyed Mostafa Biazar; Ozgur Kisi; Zohreh Sourmirinezhad. A Theoretical Approach for Forecasting Different Types of Drought Simultaneously, Using Entropy Theory and Machine-Learning Methods. ISPRS International Journal of Geo-Information 2020, 9, 701 .
AMA StylePouya Aghelpour, Babak Mohammadi, Seyed Mostafa Biazar, Ozgur Kisi, Zohreh Sourmirinezhad. A Theoretical Approach for Forecasting Different Types of Drought Simultaneously, Using Entropy Theory and Machine-Learning Methods. ISPRS International Journal of Geo-Information. 2020; 9 (12):701.
Chicago/Turabian StylePouya Aghelpour; Babak Mohammadi; Seyed Mostafa Biazar; Ozgur Kisi; Zohreh Sourmirinezhad. 2020. "A Theoretical Approach for Forecasting Different Types of Drought Simultaneously, Using Entropy Theory and Machine-Learning Methods." ISPRS International Journal of Geo-Information 9, no. 12: 701.
Streamflow forecasting is a vital task for hydrology and water resources engineering, and the different artificial intelligence (AI) approaches have been employed for this purposes until now. Additionally, the forecasting accuracy and uncertainty estimation are the meaningful assignments that need to be recognized. The addressed research investigates the potential of novel ensemble approach, Bayesian model averaging (BMA), in streamflow forecasting using daily time series data from two stations (i.e., Hongcheon and Jucheon), South Korea. Six categories (i.e., M1–M6) of input combination using different antecedent times were employed for streamflow forecasting. The outcomes of BMA model were compared with those of multivariate adaptive regression spline (MARS), M5 model tree (M5Tree), and Kernel extreme learning machines (KELM) models considering four assessment indexes, root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), correlation coefficient (R), and mean absolute error (MAE). The results revealed the superior accuracy of BMA model over three machine learning models in daily streamflow forecasting. Considering RMSE values among the best models during testing phase, the best BMA model (i.e., BMA2) enhanced the forecasting accuracy of MARS1, M5Tree4, and KELM3 models by 5.2%, 5.8%, and 3.4% in Hongcheon station. Additionally, the best BMA model (i.e., BMA1) improved the forecasting accuracy of MARS1, M5Tree1, and KELM1 models by 6.7%, 9.5%, and 3.7% in Jucheon station. In addition, the best BMA models in both stations allowed the uncertainty estimation, and produced higher uncertainty of peak flows compared to that of low flows. As one of the most robust and effective tools, therefore, the BMA model can be successfully employed for streamflow forecasting with different antecedent times.
Sungwon Kim; Meysam Alizamir; Nam Won Kim; Ozgur Kisi. Bayesian Model Averaging: A Unique Model Enhancing Forecasting Accuracy for Daily Streamflow Based on Different Antecedent Time Series. Sustainability 2020, 12, 9720 .
AMA StyleSungwon Kim, Meysam Alizamir, Nam Won Kim, Ozgur Kisi. Bayesian Model Averaging: A Unique Model Enhancing Forecasting Accuracy for Daily Streamflow Based on Different Antecedent Time Series. Sustainability. 2020; 12 (22):9720.
Chicago/Turabian StyleSungwon Kim; Meysam Alizamir; Nam Won Kim; Ozgur Kisi. 2020. "Bayesian Model Averaging: A Unique Model Enhancing Forecasting Accuracy for Daily Streamflow Based on Different Antecedent Time Series." Sustainability 12, no. 22: 9720.
The applicability of four machine learning (ML) methods, ANFIS-PSO, ANFIS-FCM, MARS and M5Tree, together with multi model simple averaging (MM-SA) ensemble method, is investigated in rainfall-runoff modeling at hourly timescale. The results are compared with the conceptual EBA4SUB model using rainfall and runoff data from Samoggia River basin, Italy. The capability of the methods is measured using five statistics, Nash–Sutcliffe efficiency, root mean squared error, mean absolute error, scatter index, and adjusted index of agreement. Comparison of single ML reveals that the ANFIS-PSO, ANFIS-FCM and MARS produce similar accuracy which is better than the M5Tree model. MM-SA ensemble model improves the accuracy of ANFIS-PSO, ANFIS-FCM, MARS and M5Tree models with respect to RMSE by 8.5%, 5%, 7.4% and 28.8%, respectively. Comparison with the conceptual event-based method indicates that the ML methods generally performs superior to the EBA4SUB; however, latter method provides better accuracy than the M5Tree and MARS in some cases.
Rana Muhammad Adnan; Andrea Petroselli; Salim Heddam; Celso Augusto Guimarães Santos; Ozgur Kisi. Short term rainfall-runoff modelling using several machine learning methods and a conceptual event-based model. Stochastic Environmental Research and Risk Assessment 2020, 35, 597 -616.
AMA StyleRana Muhammad Adnan, Andrea Petroselli, Salim Heddam, Celso Augusto Guimarães Santos, Ozgur Kisi. Short term rainfall-runoff modelling using several machine learning methods and a conceptual event-based model. Stochastic Environmental Research and Risk Assessment. 2020; 35 (3):597-616.
Chicago/Turabian StyleRana Muhammad Adnan; Andrea Petroselli; Salim Heddam; Celso Augusto Guimarães Santos; Ozgur Kisi. 2020. "Short term rainfall-runoff modelling using several machine learning methods and a conceptual event-based model." Stochastic Environmental Research and Risk Assessment 35, no. 3: 597-616.
Sediment yield is important for maintaining soil health, reservoir sustainability, environmental pollution, and conservation of natural resources. The main aim of the present work is to develop four machine learning models, artificial neural networks (ANNs), radial basis function (RBF), support vector machine (SVM) and multiple model (MM)-ANNs for forecasting daily sediment yield. These models were applied to the Shakkar and Manot watersheds covering 25 years (1990–2015) and 10 years (2000–2010) of rainfall and discharge data, respectively. Results showed that the MM-ANNs model satisfactorily predicted sediment yield and outperformed the other models providing the highest correlation coefficient (0.921, 0.883) and Nash-Sutcliffe efficiency (0.744, 0.763) and the lowest relative absolute error (0.360, 0.344) and root mean square error (23,609.5, 269,671.5) for the Shakkar and Manot during the test period, respectively. Hence, the MM-ANNs model can be successfully used for sediment prediction.
Sarita Gajbhiye Meshram; Vijay P. Singh; Ozgur Kisi; Vahid Karimi; Chandrashekhar Meshram. Application of Artificial Neural Networks, Support Vector Machine and Multiple Model-ANN to Sediment Yield Prediction. Water Resources Management 2020, 34, 4561 -4575.
AMA StyleSarita Gajbhiye Meshram, Vijay P. Singh, Ozgur Kisi, Vahid Karimi, Chandrashekhar Meshram. Application of Artificial Neural Networks, Support Vector Machine and Multiple Model-ANN to Sediment Yield Prediction. Water Resources Management. 2020; 34 (15):4561-4575.
Chicago/Turabian StyleSarita Gajbhiye Meshram; Vijay P. Singh; Ozgur Kisi; Vahid Karimi; Chandrashekhar Meshram. 2020. "Application of Artificial Neural Networks, Support Vector Machine and Multiple Model-ANN to Sediment Yield Prediction." Water Resources Management 34, no. 15: 4561-4575.
Snow is one of the essential factors in hydrology, freshwater resources, irrigation, travel, pastimes, floods, avalanches, and vegetation. In this study, the snow cover of the northern and southern slopes of Alborz Mountains in Iran was investigated by considering two issues: (1) Estimating the snow cover area and the (2) effects of droughts on snow cover. The snow cover data were monitored by images obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. The meteorological data (including the precipitation, minimum and maximum temperature, global solar radiation, relative humidity, and wind velocity) were prepared by a combination of National Centers for Environmental Prediction-Climate Forecast System Reanalysis (NCEP-CFSR) points and meteorological stations. The data scale was monthly and belonged to the 2000–2014 period. In the first part of the study, snow cover estimation was conducted by Multiple Linear Regression (MLR), Least Square Support Vector Machine (LSSVM), Group Method of Data Handling (GMDH), Multilayer Perceptron (MLP), and MLP with Grey Wolf Optimization (MLP-GWO) models. The most accurate estimations were produced by the MLP-GWO and GMDH models. The models produced better snow cover estimations for the northern slope compared to the southern slope. The GWO improved the MLP’s accuracy by 10.7%. In the second part, seven drought indices, including the Palmer Drought Severity Index (PDSI), Bahlme–Mooley Drought Index (BMDI), Standardized Precipitation Index (SPI), Multivariate Standardized Precipitation Index (MSPI), Modified Standardized Precipitation Index (SPImod), Joint Deficit Index (JDI), and Standardized Precipitation-Evapotranspiration Index (SPEI) were calculated for both slopes. The results showed that the effects of a drought event on the snow cover area would remain up to 5 (or 6) months in the region. The highest impact of drought appears after two months in the snow cover area, and the drought index most related to snow cover variations is the 2–month time window of SPI (SPI2). The results of both subjects were promising and the methods can be examined in other snowy areas of the world.
Pouya Aghelpour; Yiqing Guan; Hadigheh Bahrami-Pichaghchi; Babak Mohammadi; Ozgur Kisi; Danrong Zhang. Using the MODIS Sensor for Snow Cover Modeling and the Assessment of Drought Effects on Snow Cover in a Mountainous Area. Remote Sensing 2020, 12, 3437 .
AMA StylePouya Aghelpour, Yiqing Guan, Hadigheh Bahrami-Pichaghchi, Babak Mohammadi, Ozgur Kisi, Danrong Zhang. Using the MODIS Sensor for Snow Cover Modeling and the Assessment of Drought Effects on Snow Cover in a Mountainous Area. Remote Sensing. 2020; 12 (20):3437.
Chicago/Turabian StylePouya Aghelpour; Yiqing Guan; Hadigheh Bahrami-Pichaghchi; Babak Mohammadi; Ozgur Kisi; Danrong Zhang. 2020. "Using the MODIS Sensor for Snow Cover Modeling and the Assessment of Drought Effects on Snow Cover in a Mountainous Area." Remote Sensing 12, no. 20: 3437.
Hydropower is essential for global electricity production, but it consumes water by evaporation from the reservoir surface. Here, a new approach is introduced in relation to modelling the water footprint of electricity (WFe) from hydropower. Two of the most important variables in calculating the WFe are volume of evaporation (EV) and electricity production (EP). In this study, the random forest (RF) model was used to predict both EV and EP. For analysing hybrid models, wavelet transform was used and wavelet RF (WRF) models were developed. After decomposing the input variables by wavelet transform, the relief algorithm (RA) was used to recognize important components and inserted into the RF model. The proposed approach was applied at Mahabad Hydropower in Iran. The results suggest that applying the wavelet transform on input data and using algorithms such as RA can be regarded as a good approach for modelling of EV and EP.
Mohammad Reza Golabi; Feridon Radmanesh; Ali Mohammad Akhoond-Ali; Mohammad Hossein Niksokhan; Ozgur Kisi. Development of an indirect method for modelling the water footprint of electricity using wavelet transform coupled with the random forest model. Hydrological Sciences Journal 2020, 65, 2521 -2534.
AMA StyleMohammad Reza Golabi, Feridon Radmanesh, Ali Mohammad Akhoond-Ali, Mohammad Hossein Niksokhan, Ozgur Kisi. Development of an indirect method for modelling the water footprint of electricity using wavelet transform coupled with the random forest model. Hydrological Sciences Journal. 2020; 65 (15):2521-2534.
Chicago/Turabian StyleMohammad Reza Golabi; Feridon Radmanesh; Ali Mohammad Akhoond-Ali; Mohammad Hossein Niksokhan; Ozgur Kisi. 2020. "Development of an indirect method for modelling the water footprint of electricity using wavelet transform coupled with the random forest model." Hydrological Sciences Journal 65, no. 15: 2521-2534.