Data-based modelling approach for variable density flow and solute transport simulation in a coastal aquifer |
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| Authors: | Basant Yadav Shashi Mathur Sudheer Ch Brijesh Kumar Yadav |
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| Institution: | 1. Department of Hydrology, Indian Institute of Technology, Roorkee, India;2. Department of Civil Engineering, Indian Institute of Technology, Delhi, India;3. Ministry of Environment Forest and Climate Change, Indira Paryavaran Bhawan Jor Bagh Road, New Delhi, India |
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| Abstract: | Data-based models, namely artificial neural network (ANN), support vector machine (SVM), genetic programming (GP) and extreme learning machine (ELM), were developed to approximate three-dimensional, density-dependent flow and transport processes in a coastal aquifer. A simulation model, SEAWAT, was used to generate data required for the training and testing of the data-based models. Statistical analysis of the simulation results obtained by the four models show that the data-based models could simulate the complex salt water intrusion process successfully. The selected models were also compared based on their computational ability, and the results show that the ELM is the fastest technique, taking just 0.5 s to simulate the dataset; however, the SVM is the most accurate, with a Nash-Sutcliffe efficiency (NSE) ≥ 0.95 and correlation coefficient R ≥ 0.92 for all the wells. The root mean square error (RMSE) for the SVM is also significantly less, ranging from 12.28 to 77.61 mg/L. |
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| Keywords: | salt water intrusion extreme learning machine (ELM) support vector machine (SVM) genetic programming (GP) artificial neural network (ANN) SEAWAT model |
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