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.
Highlights
One of the most important sources of water supply for industrial, drinking, and irrigation purposes is groundwater (GW)
For the testing phase assessed with support vector machine (SVM)-grasshopper optimization algorithm (GOA), the results indicated a value of root mean square error (RMSE): 1.31 m, MAE: 0.903 m, NSE: 0.89, and Percent bias (PBIAS): 0.20 which reflected better performance than the SVM model and indicates the improvements when SVM is hybridized with the GOA
The R2 values were found to vary in the range of 0.84–0.94 and 0.79–0.91 for the Artificial neural network (ANN) and SVM models, respectively
Summary
One of the most important sources of water supply for industrial, drinking, and irrigation purposes is groundwater (GW). It is necessary to predict the Ardebil groundwater level for water resources management. Mathematical models incorporating GW dynamics are applied to predict GWL for optimizing groundwater use, optimal management, and development of conservation plans [5,7]. Since such models are costly, time-consuming, and data-intensive, their use in practice is limited because of data-scarcity [8,9]. In such cases, when geological and hydro-geological data are insufficient, soft computing models become an attractive option [10]. Artificial neural network (ANN), adaptive neuro-fuzzy interface (ANFIS), genetic programming (GP), support vector machine (SVM), and decision tree models are among the important soft computing models that are suited for modeling dynamic and uncertain nonlinear systems [7]
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