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Comparative Analysis of Artificial Neural Network (ANN) and Wavelet Integrated Artificial Neural Network (W-ANN) Approaches for Rainfall Modeling of Southern Rajasthan, India

Vinayak Paradkar1 * and H K Mittal2

1 Centre for Protected Cultivation Technology, Indian Agricultural Research Institute, New Delhi, India

2 Department of Soil and Water Engineering, Maharana Pratap University of Agriculture and Technology, Udaipur, Rajasthan India

Corresponding author Email: paradkarvd@gmail.com

DOI: http://dx.doi.org/10.12944/CWE.18.3.17

This paper addresses the challenge of predicting erratic rainfall in Rajasthan state of India, particularly in southern regions. Reliable rainfall predictions are crucial for water resource management and agriculture planning. The research involved selecting 58 stations across seven districts of southern Rajasthan and identifying the best fit computational neural (ANN) and wavelet integrated computational neural (W-ANN) architectures based on performance metrics. Different combinations of input characters, hidden layer neurons, learning algorithms, and training cycles were tested to determine optimal models. Hybrid models, combining wavelet analysis with ANN, were explored to tackle non-stationary hydrologic signals effectively. Results showed that ANN Model C with ten input layer neurons performed best for 74% of stations, followed by Model B (21% of stations) and Model A (5% of stations). Models with increased input and hidden layer neurons performed better. Among the selected stations, 81% of stations demonstrated improved performance using W-ANN models due to effective signal decomposition and information extraction. The hybrid W-ANN models outperformed simple ANN models for rainfall prediction. Both ANN and W-ANN models accurately forecasted weekly rainfall, as observed in the comparison of actual and forecasted values.

ANN; Hybrid Models; Rainfall Forecasting; Wavelet Transforms; W-ANN

Copy the following to cite this article:

Paradkar V, Mittal H. K. Comparative Analysis of Artificial Neural Network (ANN) and Wavelet Integrated Artificial Neural Network (W-ANN) Approaches for Rainfall Modeling of Southern Rajasthan, India. Curr World Environ 2023;18(3). DOI:http://dx.doi.org/10.12944/CWE.18.3.17

Copy the following to cite this URL:

Paradkar V, Mittal H. K. Comparative Analysis of Artificial Neural Network (ANN) and Wavelet Integrated Artificial Neural Network (W-ANN) Approaches for Rainfall Modeling of Southern Rajasthan, India. Curr World Environ 2023;18(3).

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