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Prediction of groundwater fluctuation based on hybrid ANFIS-GWO approach in arid Watershed, India

  • Data analytics and machine learning
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Abstract

The knowledge about groundwater level (GWL) fluctuations is very significant in water resources planning and management. In Loisingha and Saintala watersheds situated in Balangir district of Odisha, there is overexploitation of groundwater. Management of groundwater resources needs a thorough understanding about its dynamic nature. However, the dynamic nature of GW flow is constantly varying in response to climatic and human stresses and is too complex. This involves many uncertain and nonlinear factors. Present research examines applicability of two data-driven models, i.e., CFBPNN (Cascade Forward Back Propagation Neural Network), ANFIS (Adaptive Neuro-Fuzzy Inference System), and one hybrid model ANFIS-GWO combining ANFIS with a robust optimization algorithm, namely GWO (Grey Wolf Optimizer) for forecasting GWL at two watersheds of Balangir, India. As ANFIS parameters impact forecasting accurateness, adjustment and optimization of these parameters utilizing GWO are essential. In this context, 18 years of data comprising different hydrological constraints such as precipitation, average discharge, evapotranspiration, minimum and maximum temperature are utilized as input data for predicting GWL. To assess the performance of proposed models, quantitative statistical measures, namely R2 (coefficient of determination), RMSE (root-mean-squared error), WI (Willmott index), and NSE (Nash–Sutcliffe efficiency), are applied. Based on analysis of results obtained, it is concluded that ANFIS-GWO with NSE-0.9745, WI-0.9763, and RMSE-0.0450 performed superiorly to standalone ANFIS and conventional CFBPNN model. Predictions with these conditions revealed that proposed models show better performance considering evapotranspiration as an input parameter.

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Abbreviations

GWL:

Groundwater level

CFBPNN:

Cascade forward back propagation neural network

ANFIS:

Adaptive neuro-fuzzy inference system

GWO:

Grey wolf optimizer

R2 :

Coefficient of determination

RMSE:

Root mean squared error

WI:

Willmott index

NSE:

Nash–Sutcliffe efficiency

ML:

Machine learning

ANN:

Artificial neural network

GEP:

Gene expression programming

SVM:

Support vector machine

FFNN:

Feed-forward neural network

MLP:

Multilayer perceptron

BPNN:

Back propagation neural network

FIS:

Fuzzy inference system

PSO:

Particle swarm optimization

GP:

Genetic programming

GA:

Genetic algorithm

ACO:

Ant colony optimization

DE:

Differential evolution

\(P_{{\text{t}}}\) :

Precipitation

\(T_{\min }\) :

Minimum temperature

\(T_{\max }\) :

Maximum temperature

\(Q_{{\text{t}}}\) :

Monthly average discharge

\(E_{{\text{t}}}\) :

Evapotranspiration

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This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Department of Civil Engineering, College of Engineering and Technology, Bhubaneswar, Odisha, India

    Sandeep Samantaray, Chinmayee Biswakalyani & Deba Prakash Satapathy

  2. Adjunct Faculty, Jaipuria Institute of Management, Lucknow, India

    Deepak Kumar Singh

  3. Department of Civil Engineering, National Institute of Technology, Silchar, Assam, India

    Abinash Sahoo

Authors
  1. Sandeep Samantaray
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  2. Chinmayee Biswakalyani
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  3. Deepak Kumar Singh
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  4. Abinash Sahoo
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  5. Deba Prakash Satapathy
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Correspondence to Sandeep Samantaray.

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Samantaray, S., Biswakalyani, C., Singh, D.K. et al. Prediction of groundwater fluctuation based on hybrid ANFIS-GWO approach in arid Watershed, India. Soft Comput 26, 5251–5273 (2022). https://doi.org/10.1007/s00500-022-07097-6

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