Prediction of groundwater table and drought analysis; a new hybridization strategy based on bi-directional long short-term model and the Harris hawk optimization algorithm

• Published in: Journal of water and climate change Vol. 13; no. 5; pp. 2233 - 2254
• Main Authors: Farzin, Saeed, Anaraki, Mahdi Valikhan, Naeimi, Maryam, Zandifar, Samira
• Format: Journal Article
• Language: English
• Published: London IWA Publishing 01.05.2022
• Subjects: Accuracy; Algorithms; Analysis; Aquifers; Artificial intelligence; Artificial neural networks; Autoregressive models; bi-directional long short-term model (blstm); Case studies; Computer simulation; Confidence intervals; Drought; Drought analysis; Freshwater resources; ground resource index (gri); Groundwater; Groundwater levels; Groundwater table; groundwater table time series; harris hawk optimization (hho) algorithm; Hybridization; Hydrology; Mathematical models; Methods; Neural networks; Optimization; Optimization algorithms; Rain; Simulation; Support vector machines; Time series; Water table; Iran
• ISSN: 2040-2244; 2408-9354; 2408-9354
• DOI: 10.2166/wcc.2022.066

In the present study, a new hybridization strategy for predicting the groundwater table (GWT) and drought analysis is presented. Therefore, a hybrid of the bi-directional long short-term model (BLSTM) and the Harris hawk optimization (HHO) algorithm, namely the BLSTM–HHO algorithm, is applied. In this algorithm, the lagged data of the GWT are used as the input, whereas the current GWT data are used as the output. Additionally, the standalone BLSTM, the long short-term model (LSTM), artificial neural networks (ANN), Seasonal Autoregressive Integrated Moving Average (SARIMA), and the Autoregressive Integrated Moving Average (ARIMA) are employed as benchmark simulating algorithms. The results show that the BLSTM–HHO algorithm has more accuracy than the other investigated simulating algorithms based on the different evaluation criteria such as relative root mean squared error (RRMSE), Nash–Sutcliffe coefficient (NSE), and refined Willmott index (dr). The prediction results (from 2018 to 2022) in all three investigated aquifers show the decline of the GWT (−5.40 m for Brojen aquifer, −7.23 m for Javanmardi aquifer, and −5.81 m for Shahrekord aquifer). Accordingly, the drought analysis by the ground resource index (GRI) in the investigated areas shows that drought is expected to be continued for the next 5 years with an increasing magnitude of severity.