Rainfall Prediction for Solapur City (Maharashtra State) Using Artificial Neural Networks (ANN)

Abstract

Predictive rainfall data is extremely important in fields where rainwater is primarily used for crop irrigation. In this study attempt has been made to predict rainfall of Solapur city, Maharashtra state. The rainfall data from 2010 to 2019 was collected. The length of the training data set varied from 70% to 90%. The study evaluates the performance of Artificial intelligence and its applicability to rainfall prediction modelling specifically applied to Solapur city. Computational networks with biological inspiration are called artificial neural networks (ANNs). Among the various types of ANNs , in this study, We focused on learning methods for feed-forward back propagation. The most popular ANNs, which can be used for a wide range of problems comprise three layers: input, hidden, and output, which are based on a supervised process. The outcome suggest that the artificial neural networks (ANNs) prediction model demonstrate satisfactory accuracy.

Introduction

The text discusses rainfall as precipitation from clouds caused by moisture condensation in the atmosphere, influenced by humidity, wind, and temperature. It explains basic meteorological concepts like humidity, wind pressure, temperature, and precipitation types.

An Artificial Neural Network (ANN) approach is introduced, modeling rainfall prediction based on the structure of human brain neurons. ANNs consist of interconnected nodes that process information in layers, with learning achieved by adjusting connection weights. The study uses a feed-forward backpropagation ANN model with TANSIG and PURELIN transfer functions for rainfall prediction.

The climate of Solapur city is described, noting its average annual rainfall of 735 mm over 44 wet days, distinct seasons, and vulnerability to tropical cyclones causing heavy rainfall. The city has a dry climate with hot summers and moderate monsoon rainfall.

For methodology, Solapur's geographic and climatic data are used along with ten years (2010–2019) of monthly records of temperature, wind speed, humidity (inputs), and rainfall (output). This data trains and tests the ANN model to predict rainfall accurately.

Conclusion

Predictive rainfall data is extremely important in fields where rainwater is primarily used for crop irrigation. In this study attempt has been made to predict rainfall of Solapur city, Maharashtra state. The rainfall data from 2010 to 2019 was collected. The length of the training data set varied from 70% to 90%. The study evaluates the performance of Artificial intelligence and its applicability to rainfall prediction modelling specifically applied to Solapur city. The conclusion of the study is as follow, the best results are obtained from where the transfer function in layers TANSIG & PURELIN are used and number of neurons are used is 100. For training the ANN network feed-forward back-propagation was used and it gives the acceptable results. After comparing all the results, the acceptable R² value obtained is 0.8626.

References

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Copyright

Copyright © 2025 Shoaib Sayyad. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.