Predicting groundwater depth fluctuations using deep learning, extreme learning machine and Gaussian process: a comparat
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RESEARCH ARTICLE
Predicting groundwater depth fluctuations using deep learning, extreme learning machine and Gaussian process: a comparative study Deepak Kumar 1 & Thendiyath Roshni 1
&
Anshuman Singh 1 & Madan Kumar Jha 2 & Pijush Samui 1
Received: 7 April 2020 / Accepted: 19 August 2020 # Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Groundwater depth has complex non-linear relationships with climate, groundwater extraction, and surface water flows. To understand the importance of each predictor and predictand (groundwater depth), different artificial intelligence (AI) techniques have been used. In this research, we have proposed a Deep Learning (DL) model to predict groundwater depths. The DL model is an extension of the conventional neural network with multiple layers having non-linear activation function. The feasibility of the DL model is assessed with well-established framework models [Extreme Learning Machine (ELM) and Gaussian Process Regression (GPR)]. The area selected for this study is Konan basin located in the Kochi Prefecture of Japan. The hydrometeorological and groundwater data used are precipitation, river stage, temperature, recharge and groundwater depth. Identical set of inputs and outputs of all the selected stations were used to train and validate the models. The predictive accuracy of the DL, ELM and GPR models has been assessed considering suitable goodness-of-fit criteria. During training period, the DL model has a very good agreement with the observed data (RMSE = 0.04, r = 0.99 and NSE = 0.98) and during validation period, its performance is satisfactory (RMSE = 0.08, r = 0.95 and NSE = 0.87). To check practicality and generalization ability of the DL model, it was re-validated at three different stations (E2, E3 and E6) of the same unconfined aquifer. The significant prediction capability and generalization ability makes the proposed DL model more reliable and robust. Based on the finding of this research, the DL model is an intelligent tool for predicting groundwater depths. Such advanced AI technique can save resources and labor conventionally employed to estimate various features of complex groundwater systems. Keywords Groundwater-fluctuation modeling . Extreme learning machine . Deep learning . Gaussian process regression . Model evaluation
Communicated by: H. Babaie * Thendiyath Roshni [email protected] Deepak Kumar [email protected] Anshuman Singh [email protected] Madan Kumar Jha [email protected] Pijush Samui [email protected] 1
Department of Civil Engineering, National Institute of Technology Patna, Ashok Rajpath, Bihar 800005, India
2
Agricultural and Food Engineering Department, Indian Institute of Technology Kharagpur, West Bengal 721302, India
Introduction From an ecological viewpoint, groundwater plays a crucial role in maintaining the health of the ecosystem (Fan et al. 2008). In arid and semi-arid region, groundwater depth influences the pattern of vegetation and soil health and result in soil salinization and land desertification
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