Groundwater level modeling using Augmented Artificial Ecosystem Optimization

• Published in: Journal of hydrology (Amsterdam) Vol. 617; p. 129034
• Main Authors: Van Thieu, Nguyen, Deb Barma, Surajit, Van Lam, To, Kisi, Ozgur, Mahesha, Amai
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.02.2023
• Subjects: artificial intelligence; Augmented Artificial Ecosystem Optimization; Coastal aquifers; data collection; ecosystems; Groundwater level modeling; India; Meta-heuristic algorithms; Nature-inspired computing; Neural network; prediction; rain; stream flow; temperature; time series analysis; water table; India; Groundwater level modeling; Neural network; Meta-heuristic algorithms; Coastal aquifers; Augmented Artificial Ecosystem Optimization; Nature-inspired computing
• ISSN: 0022-1694; 1879-2707
• DOI: 10.1016/j.jhydrol.2022.129034

Nature-inspired optimization is an active area of research in the artificial intelligence (AI) field and has recently been adopted in hydrology for the calibration (training) of both process-based and statistical models. This study proposes an improved AI model, Augmented Artificial Ecosystem Optimization-based Multi-Layer Perceptron (AAEO-MLP), to build a monthly groundwater level (GWL) forecasting model. AAEO-MLP model is built on the novel Augmented version of Artificial Ecosystem Optimization and traditional MLP network. In AAEO, Levy-flight trajectory and Gaussian random are utilized in exploration and exploitation to improve the optimizing ability. The AAEO-MLP model is tested on two time-series (1989–2012) datasets collected at two wells in India. Various explanatory variables such as monthly cumulative precipitation, mean temperature, tidal height, and previous measurements of GWL were considered for predicting 1-month ahead of GWL. The performance of AAEO-MLP was benchmarked against 17 different models (original AEO, 3 different variants of AEO, and 13 well-known models) in terms of forecasting accuracy based on six metrics (e.g., mean absolute error, root mean square error, Kling–Gupta efficiency, normalized Nash–Sutcliffe efficiency, Pearson’s correlation index, a20 index). Furthermore, convergence analysis and model stability are employed to indicate the effectiveness of AAEO-MLP. The compared results express that the AAEO-MLP is superior to other models in terms of prediction accuracy, convergence, and stability. Overall, the results depict that the AAEO is a promising approach for optimizing machine learning models (e.g., MLP) and should be explored for other hydrological forecasting applications (e.g., streamflow, rainfall) to further assess its strengths over existing methods. •A novel Augmented Artificial Ecosystem Optimization (AAEO) algorithm is proposed.•Groundwater level is modeled using AAEO combined with multilayer perceptron (MLP).•Eighteen different models are compared for groundwater level forecasting.•AAEO-MLP shows the highest performance and stability with reasonable convergence.•AAEO-MLP can be used for similar hydrologic prediction problems like streamflow, drought, lake level.