Artificial Neural Network Model for Rainfall Data Analysis During 2004-2017 in Tamil Nadu, India – Prevailing Pattern Evaluation on Climate Change

A Stanley Raj,K Saranya,S Akshaya,R Josephine Usha,D Shyamilee,Babak Mohammadi

doi:10.14302/issn.2643-2811.jmbr-20-3402

Abstract

This research paper focuses on rainfall variations in Tamil Nadu, India using Wavelet, Linear regression and Artificial Neural Networks model from 2004 to 2017. As the rainfall is the key factor in understanding climate change, the seasonal datasets from 2004-2017 of Tamil Nadu state has been taken for study. The salient feature of this study is the application of Neural Networks and wavelet analysis. It reveals that the rainfall variations are ambiguous that it does not maintain a constant pattern. Wavelet coefficients of multiresolution spectrogram reveals that the intensity of rainfall in each year. Linear regression model divulge the pattern of rainfall followed in every season and the results show that except winter season all other season suffers deficient rainfall. The deficiency of rainfall may be due to different parameters like ElNino or LaNina pattern or global warming. Results showed that all seasons except winter does not maintain consistency in the rainfall variability. Winter season provides the positive slope values of 4.7 and 0.6 for January and February respectively. Moreover Artificial Neural Networks training provides prominent results of Regression value 0.98 which is comparably high with other seasons taken for study.

Highlights

Rainfall a natural occurrence plays major role in determining groundwater level, and in particular helps agricultural sector
During the month of March, rainfall decreases at the rate of -1.8202 and Aril and May month it increases slope increases to certain value of 1.8202 and 1.6497 respectively (Figure 7). (Figure 11) shows the Artificial Neural Networks (ANN) predicted premonsoon rainfall variation with error percentage
Neural Networks attempts to estimate and the error percentage are very less in training the winter season

Summary

Introduction

Rainfall a natural occurrence plays major role in determining groundwater level, and in particular helps agricultural sector. India receives most of the rainfall on monsoon so it is very essential for understanding the rainfall pattern and its trend. Several studies over the pattern of rainfall showed that there is no evident of increasing or decreasing trend in average rainfall in India [8, 9, 10, and 11]. Some studies revealed that there is considerable change in trend of rainfall over regional scale [11, 12, 13, 14, 15, and 16]. 2019 [23] applied long term change in the monthly forecasting model using statistical evaluation techniques like SARIMA, SVR and SVR-FA in Iran. Babak Mohammadi et al, 2020 [26] applied ANFIS with shuffled frog leaping algorithm for river flow forecast model. Afshin Ashrafzadeh, 2019 [28] applied integrative data intelligence model for evaporation estimates over Northern Iran. It is very important to study the rainfall pattern using standard methodologies

Methods

Results

Conclusion