Forecasting and Prediction of Seasonal Rainfall in Drought and Flood Affected Zones of Karnataka Using Artificial Neural Network (ANN)

Abstract

Rainfall prediction plays a pivotal role in managing climate-sensitive sectors such as agriculture, water resource planning, disaster mitigation, and infrastructure development. In regions with high climatic variability, such as Karnataka, accurate and timely forecasting is essential for decision-making. Karnataka has many different types of land and landscapes, few districts often face droughts, while others experience floods. Traditional statistical models often fall short in capturing the complex, non-linear dynamics of seasonal rainfall. This study explores the application of Artificial Neural Network (ANN) techniques to forecast rainfall across two categories of high-risk zones in Karnataka. The drought-prone such as Bagalkote, Chitradurga, Koppala, Raichur and flood-prone such as Dakshina Kannada, Udupi, Uttara Kannada, Chikkamagalur, Kodagu, Shivamogga districts. The model predicts rainfall for three key monsoon seasons such as Pre-monsoon (PRM), Southwest monsoon (SWM), and Northeast monsoon (NEM).