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Boo KBW, El-Shafie A, Othman F, Khan MMH, Birima AH, Ahmed AN. Groundwater level forecasting with machine learning models: A review. WATER RESEARCH 2024; 252:121249. [PMID: 38330715 DOI: 10.1016/j.watres.2024.121249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 01/05/2024] [Accepted: 01/31/2024] [Indexed: 02/10/2024]
Abstract
Groundwater, the world's most abundant source of freshwater, is rapidly depleting in many regions due to a variety of factors. Accurate forecasting of groundwater level (GWL) is essential for effective management of this vital resource, but it remains a complex and challenging task. In recent years, there has been a notable increase in the use of machine learning (ML) techniques to model GWL, with many studies reporting exceptional results. In this paper, we present a comprehensive review of 142 relevant articles indexed by the Web of Science from 2017 to 2023, focusing on key ML models, including artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), support vector regression (SVR), evolutionary computing (EC), deep learning (DL), ensemble learning (EN), and hybrid-modeling (HM). We also discussed key modeling concepts such as dataset size, data splitting, input variable selection, forecasting time-step, performance metrics (PM), study zones, and aquifers, highlighting best practices for optimal GWL forecasting with ML. This review provides valuable insights and recommendations for researchers and water management agencies working in the field of groundwater management and hydrology.
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Affiliation(s)
- Kenneth Beng Wee Boo
- Department of Civil Engineering, Faculty of Engineering, Universiti Malaya (UM), 50603 Kuala Lumpur, Malaysia.
| | - Ahmed El-Shafie
- Department of Civil Engineering, Faculty of Engineering, Universiti Malaya (UM), 50603 Kuala Lumpur, Malaysia; National Water and Energy Center, United Arab Emirates University, P.O. Box 15551, Al Ain, United Arab Emirates.
| | - Faridah Othman
- Department of Civil Engineering, Faculty of Engineering, Universiti Malaya (UM), 50603 Kuala Lumpur, Malaysia.
| | - Md Munir Hayet Khan
- Faculty of Engineering & Quantity Surveying, INTI International University (INTI-IU), Persiaran Perdana BBN, Putra Nilai, 71800 Nilai, Negeri Sembilan, Malaysia.
| | - Ahmed H Birima
- Department of Civil Engineering, College of Engineering, Qassim University, Unaizah, Saudi Arabia.
| | - Ali Najah Ahmed
- School of Engineering and Technology, Sunway University, Bandar Sunway, Petaling Jaya, 47500, Malaysia; Institute of Energy Infrastructure (IEI) , Universiti Tenaga Nasional (UNITEN), 43000, Selangor, Malaysia.
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2
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Puri D, Kumar R, Kumar S, Thakur MS, Fekete G, Lee D, Singh T. Performance analysis and modelling of circular jets aeration in an open channel using soft computing techniques. Sci Rep 2024; 14:3140. [PMID: 38326386 PMCID: PMC10850504 DOI: 10.1038/s41598-024-53407-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 01/31/2024] [Indexed: 02/09/2024] Open
Abstract
Dissolved oxygen (DO) is an important parameter in assessing water quality. The reduction in DO concentration is the result of eutrophication, which degrades the quality of water. Aeration is the best way to enhance the DO concentration. In the current study, the aeration efficiency (E20) of various numbers of circular jets in an open channel was experimentally investigated for different channel angle of inclination (θ), discharge (Q), number of jets (Jn), Froude number (Fr), and hydraulic radius of each jet (HRJn). The statistical results show that jets from 8 to 64 significantly provide aeration in the open channel. The aeration efficiency and input parameters are modelled into a linear relationship. Additionally, utilizing WEKA software, three soft computing models for predicting aeration efficiency were created with Artificial Neural Network (ANN), M5P, and Random Forest (RF). Performance evaluation results and box plot have shown that ANN is the outperforming model with correlation coefficient (CC) = 0.9823, mean absolute error (MAE) = 0.0098, and root mean square error (RMSE) = 0.0123 during the testing stage. In order to assess the influence of different input factors on the E20 of jets, a sensitivity analysis was conducted using the most effective model, i.e., ANN. The sensitivity analysis results indicate that the angle of inclination is the most influential input variable in predicting E20, followed by discharge and the number of jets.
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Affiliation(s)
- Diksha Puri
- School of Environmental Science, Shoolini University, Solan, Himachal Pradesh, 173229, India
| | - Raj Kumar
- Department of Mechanical Engineering, Gachon University, Seongnam, 13120, South Korea
| | - Sushil Kumar
- Department of Physics, Hansraj College, University of Delhi, Delhi, 110007, India
| | - M S Thakur
- Department of Civil Engineering, Shoolini University, Solan, Himachal Pradesh, 173229, India
| | - Gusztáv Fekete
- Department of Material Science and Technology, Széchenyi István University, 9026, Győr, Hungary
| | - Daeho Lee
- Department of Mechanical Engineering, Gachon University, Seongnam, 13120, South Korea.
| | - Tej Singh
- Savaria Institute of Technology, Faculty of Informatics, ELTE Eötvös Loránd University, Budapest, 1117, Hungary.
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Saroughi M, Mirzania E, Achite M, Katipoğlu OM, Ehteram M. Shannon entropy of performance metrics to choose the best novel hybrid algorithm to predict groundwater level (case study: Tabriz plain, Iran). ENVIRONMENTAL MONITORING AND ASSESSMENT 2024; 196:227. [PMID: 38305997 DOI: 10.1007/s10661-024-12357-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 01/11/2024] [Indexed: 02/03/2024]
Abstract
Predicting groundwater level (GWL) fluctuations, which act as a reserve water reservoir, particularly in arid and semi-arid climates, is vital in water resources management and planning. Within the scope of current research, a novel hybrid algorithm is proposed for estimating GWL values in the Tabriz plain of Iran by combining the artificial neural network (ANN) algorithm with newly developed nature-inspired Coot and Honey Badger metaheuristic optimization algorithms. Various combinations of meteorological data such as temperature, evaporation, and precipitation, previous GWL values, and the month and year values of the data were used to evaluate the algorithm's success. Furthermore, the Shannon entropy of model performance was assessed according to 44 different statistical indicators, classified into two classes: accuracy and error. Hence, based on the high value of Shannon entropy, the best statistical indicator was selected. The results of the best model and the best scenario were analyzed. Results indicated that value of Shannon entropy is higher for the accuracy class than error class. Also, for accuracy and error class, respectively, Akaike information criterion (AIC) and residual sum of squares (RSS) indexes with the highest entropy value which is equal to 12.72 and 7.3 are the best indicators of both classes, and Legate-McCabe efficiency (LME) and normalized root mean square error-mean (NRMSE-Mean) indexes with the lowest entropy value which is equal to 3.7 and - 8.3 are the worst indicators of both classes. According to the evaluation best indicator results in the testing phase, the AIC indicator value for HBA-ANN, COOT-ANN, and the standalone ANN models is equal to - 344, - 332.8, and - 175.8, respectively. Furthermore, it was revealed that the proposed metaheuristic algorithms significantly improve the performance of the standalone ANN model and offer satisfactory GWL prediction results. Finally, it was concluded that the Honey Badger optimization algorithm showed superior results than the Coot optimization algorithm in GWL prediction.
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Affiliation(s)
- Mohsen Saroughi
- Department of Irrigation and Reclamation Engineering, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
| | - Ehsan Mirzania
- Department of Water Engineering, University of Tabriz, Tabriz, Iran
| | - Mohammed Achite
- Faculty of Nature and Life Sciences, Laboratory of Water and Environment, Hassiba Benbouali University of Chlef, 02180, Chlef, Algeria.
| | - Okan Mert Katipoğlu
- Department of Civil Engineering, Erzincan Binali Yıldırım University, Erzincan, Turkey
| | - Mohammad Ehteram
- Department of Water Engineering, Semnan University, Semnan, Iran
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Samani S, Vadiati M, Nejatijahromi Z, Etebari B, Kisi O. Groundwater level response identification by hybrid wavelet-machine learning conjunction models using meteorological data. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:22863-22884. [PMID: 36308648 DOI: 10.1007/s11356-022-23686-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 10/13/2022] [Indexed: 06/16/2023]
Abstract
Due to its heterogeneous and complex nature, groundwater modeling needs great effort to quantify the aquifer, a crucial tool for policymakers and hydrogeologists to understand the variations in groundwater levels (GWL). This study proposed a set of supervised machine learning (ML) models to delineate the GWL changes in the Zarand-Saveh complex aquifer in Iran using 15-year (2005-2020) monthly dataset. The wavelet transform (WT) procedure was also used to improve the GWL prediction ability of ML models for 3-month horizons using input datasets of precipitation, evapotranspiration, temperature, and GWL. The four well-accepted standalone ML methods, i.e., artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), group method of data handling (GMDH), and least square support vector machine (LSSVM), were implemented and compared with the hybrid wavelet conjunction models. The methods were compared based on root mean square error (RMSE), mean absolute error (MAE), correlation coefficient (R), and Nash-Sutcliffe efficiency (NSE). Comparison outcomes showed that the hybrid wavelet-ML considerably improved the standalone model results. The wavelet transform-least square support vector machine (WT-LSSVM) model was superior to other standalone and hybrid wavelet-ML methods to predict GWL. The best GWL predictions were acquired from the WT-LSSVM model with input scenario 5 involving all influential variables, and this model produced RMSE, MAE, R, and NSE as 0.05, 0.04, 0.99, and 0.99 for 1 month ahead of GWL prediction, while the corresponding values were obtained as 0.18, 0.14, 0.95, and 0.90 for 3 months ahead of GWL prediction, respectively.
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Affiliation(s)
- Saeideh Samani
- Department of Water Resources Study and Research, Water Research Institute (WRI), Tehran Province, District 4, Bahar Blvd, Tehran, Iran
| | - Meysam Vadiati
- Global Affairs, Hubert H. Humphrey Fellowship Program, University of California, 10 College Park, Davis, CA, 95616, USA.
| | - Zohre Nejatijahromi
- Department of Minerals and Hydrogeology, Faculty of Earth Sciences, Shahid Beheshti University, Evin Ave, Tehran, Iran
| | - Behrooz Etebari
- CalNRA/Dept. of Water Resources/ Sustainable Groundwater Management Office, 715 P Street, Sacramento, CA, USA
| | - Ozgur Kisi
- Department of Civil Engineering, Technical University of Lübeck, 23562, Lübeck, Germany
- Department of Civil Engineering, Ilia State University, 0162, Tbilisi, Georgia
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Gupta D, Natarajan N, Berlin M. Short-term wind speed prediction using hybrid machine learning techniques. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:50909-50927. [PMID: 34251573 DOI: 10.1007/s11356-021-15221-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 06/27/2021] [Indexed: 06/13/2023]
Abstract
Wind energy is one of the potential renewable energy sources being exploited around the globe today. Accurate prediction of wind speed is mandatory for precise estimation of wind power at a site. In this study, hybrid machine learning models have been deployed for short-term wind speed prediction. The twin support vector regression (TSVR), primal least squares twin support vector regression (PLSTSVR), iterative Lagrangian twin parametric insensitive support vector regression (ILTPISVR), extreme learning machine (ELM), random vector functional link (RVFL), and large-margin distribution machine-based regression (LDMR) models have been adopted in predicting the short-term wind speed collected from five stations named as Chennai, Coimbatore, Madurai, Salem, and Tirunelveli in Tamil Nadu, India. Further to check the applicability of the models, the performance of the models was compared based on various performance measures like RMSE, MAPE, SMAPE, MASE, SSE/SST, SSR/SST, and R2. The results suggest that LDMR outperforms other models in terms of its prediction accuracy and ELM is computationally faster compared to other models.
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Affiliation(s)
- Deepak Gupta
- Department of Computer Science and Engineering, National Institute of Technology Arunachal Pradesh, Yupia, Papum Pare, Arunachal Pradesh, 791112, India
| | - Narayanan Natarajan
- Department of Civil Engineering, Dr. Mahalingam College of Engineering and Technology, Pollachi, Tamil Nadu, 642003, India.
| | - Mohanadhas Berlin
- Department of Civil Engineering, National Institute of Technology Arunachal Pradesh, Yupia, Papum Pare, Arunachal Pradesh, 791112, India
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Tao H, Hameed MM, Marhoon HA, Zounemat-Kermani M, Heddam S, Kim S, Sulaiman SO, Tan ML, Sa’adi Z, Mehr AD, Allawi MF, Abba S, Zain JM, Falah MW, Jamei M, Bokde ND, Bayatvarkeshi M, Al-Mukhtar M, Bhagat SK, Tiyasha T, Khedher KM, Al-Ansari N, Shahid S, Yaseen ZM. Groundwater level prediction using machine learning models: A comprehensive review. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.03.014] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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Hajirahimi Z, Khashei M. Hybridization of hybrid structures for time series forecasting: a review. Artif Intell Rev 2022. [DOI: 10.1007/s10462-022-10199-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
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Najafabadipour A, Kamali G, Nezamabadi-pour H. Application of Artificial Intelligence Techniques for the Determination of Groundwater Level Using Spatio-Temporal Parameters. ACS OMEGA 2022; 7:10751-10764. [PMID: 35382324 PMCID: PMC8973156 DOI: 10.1021/acsomega.2c00536] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Accepted: 03/07/2022] [Indexed: 06/14/2023]
Abstract
Increasing the depth of mining leads to the location of the mine pit below the groundwater level. The entry of groundwater into the mining pit increases costs as well as reduces efficiency and the level of work safety. Prediction of the groundwater level is a useful tool for managing groundwater resources in the mining area. In this study, to predict the groundwater level, multilayer perceptron, cascade forward, radial basis function, and generalized regression neural network models were developed. Moreover, four optimization algorithms, including Bayesian regularization, Levenberg-Marquardt, resilient backpropagation, and scaled conjugate gradient, are used to improve the performance and prediction ability of the multilayer perception and cascade forward neural networks. More than 1377 data points including 12 spatial parameters divided into two categories of sediments and bedrock (longitude, latitude, hydraulic conductivity of sediments and bedrock, effective porosity of sediments and bedrock, the electrical resistivity of sediments and bedrock, depth of sediments, surface level, bedrock level, and fault), and besides, 6 temporal parameters are used (day, month, year, drainage, evaporation, and rainfall). Also, to determine the best models and combine them, 165 extra validation data points are used. After identifying the best models from the three candidate models with a lower average absolute relative error (AARE) value, the committee machine intelligence system (CMIS) model has been developed. The proposed CMIS model predicts groundwater level data with high accuracy with an AARE value of less than 0.11%. Sensitivity analysis indicates that the electrical resistivity of sediments had the highest effect on the groundwater level. Outliers' estimation applying the Leverage approach suggested that only 2% of the data points could be doubtful. Eventually, the results of modeling and estimating groundwater level fluctuations with low error indicate the high accuracy of machine learning methods that can be a good alternative to numerical modeling methods such as MODFLOW.
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Affiliation(s)
| | - Gholamreza Kamali
- Department
of Mining Engineering, Shahid Bahonar University
of Kerman, Kerman 76169-14111, Iran
| | - Hossein Nezamabadi-pour
- Department
of Electrical Engineering, Shahid Bahonar
University of Kerman, Kerman 76169-14111, Iran
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Introducing the Visual Imaging Feature to the Text Analysis: High Efficient Soft Computing Models with Bayesian Network. Neural Process Lett 2021. [DOI: 10.1007/s11063-020-10402-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Artificial Neural Network Optimized with a Genetic Algorithm for Seasonal Groundwater Table Depth Prediction in Uttar Pradesh, India. SUSTAINABILITY 2020. [DOI: 10.3390/su12218932] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Accurate information about groundwater level prediction is crucial for effective planning and management of groundwater resources. In the present study, the Artificial Neural Network (ANN), optimized with a Genetic Algorithm (GA-ANN), was employed for seasonal groundwater table depth (GWTD) prediction in the area between the Ganga and Hindon rivers located in Uttar Pradesh State, India. A total of 18 models for both seasons (nine for the pre-monsoon and nine for the post-monsoon) have been formulated by using groundwater recharge (GWR), groundwater discharge (GWD), and previous groundwater level data from a 21-year period (1994–2014). The hybrid GA-ANN models’ predictive ability was evaluated against the traditional GA models based on statistical indicators and visual inspection. The results appraisal indicates that the hybrid GA-ANN models outperformed the GA models for predicting the seasonal GWTD in the study region. Overall, the hybrid GA-ANN-8 model with an 8-9-1 structure (i.e., 8: inputs, 9: neurons in the hidden layer, and 1: output) was nominated optimal for predicting the GWTD during pre- and post-monsoon seasons. Additionally, it was noted that the maximum number of input variables in the hybrid GA-ANN approach improved the prediction accuracy. In conclusion, the proposed hybrid GA-ANN model’s findings could be readily transferable or implemented in other parts of the world, specifically those with similar geology and hydrogeology conditions for sustainable planning and groundwater resources management.
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Abstract
Reliable seasonal prediction of groundwater levels is not always possible when the quality and the amount of available on-site groundwater data are limited. In the present work, a hybrid K-Nearest Neighbor-Random Forest (KNN-RF) is used for the prediction of variations in groundwater levels (L) of an aquifer with the groundwater relatively close to the surface (<10 m) is proposed. First, the time-series smoothing methods are applied to improve the quality of groundwater data. Then, the ensemble K-Nearest Neighbor-Random Forest (KNN-RF) model is treated using hydro-climatic data for the prediction of variations in the levels of the groundwater tables up to three months ahead. Climatic and groundwater data collected from eastern Rwanda were used for validation of the model on a rolling window basis. Potential predictors were: the observed daily mean temperature (T), precipitation (P), and daily maximum solar radiation (S). Previous day’s precipitation P (t − 1), solar radiation S (t), temperature T (t), and groundwater level L (t) showed the highest variation in the fluctuations of the groundwater tables. The KNN-RF model presents its results in an intelligible manner. Experimental results have confirmed the high performance of the proposed model in terms of root mean square error (RMSE), mean absolute error (MAE), Nash–Sutcliffe (NSE), and coefficient of determination (R2).
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Modeling and Uncertainty Analysis of Groundwater Level Using Six Evolutionary Optimization Algorithms Hybridized with ANFIS, SVM, and ANN. SUSTAINABILITY 2020. [DOI: 10.3390/su12104023] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
In the present study, six meta-heuristic schemes are hybridized with artificial neural network (ANN), adaptive neuro-fuzzy interface system (ANFIS), and support vector machine (SVM), to predict monthly groundwater level (GWL), evaluate uncertainty analysis of predictions and spatial variation analysis. The six schemes, including grasshopper optimization algorithm (GOA), cat swarm optimization (CSO), weed algorithm (WA), genetic algorithm (GA), krill algorithm (KA), and particle swarm optimization (PSO), were used to hybridize for improving the performance of ANN, SVM, and ANFIS models. Groundwater level (GWL) data of Ardebil plain (Iran) for a period of 144 months were selected to evaluate the hybrid models. The pre-processing technique of principal component analysis (PCA) was applied to reduce input combinations from monthly time series up to 12-month prediction intervals. The results showed that the ANFIS-GOA was superior to the other hybrid models for predicting GWL in the first piezometer (RMSE:1.21, MAE:0.878, NSE:0.93, PBIAS:0.15, R2:0.93), second piezometer (RMSE:1.22, MAE:0.881, NSE:0.92, PBIAS:0.17, R2:0.94), and third piezometer (RMSE:1.23, MAE:0.911, NSE:0.91, PBIAS:0.19, R2:0.94) in the testing stage. The performance of hybrid models with optimization algorithms was far better than that of classical ANN, ANFIS, and SVM models without hybridization. The percent of improvements in the ANFIS-GOA versus standalone ANFIS in piezometer 10 were 14.4%, 3%, 17.8%, and 181% for RMSE, MAE, NSE, and PBIAS in training stage and 40.7%, 55%, 25%, and 132% in testing stage, respectively. The improvements for piezometer 6 in train step were 15%, 4%, 13%, and 208% and in test step were 33%, 44.6%, 16.3%, and 173%, respectively, that clearly confirm the superiority of developed hybridization schemes in GWL modelling. Uncertainty analysis showed that ANFIS-GOA and SVM had, respectively, the best and worst performances among other models. In general, GOA enhanced the accuracy of the ANFIS, ANN, and SVM models.
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