Forecasting summer rainfall over India using genetic algorithm

Abstract

[1] In this study we have assessed the feasibility of a nonlinear technique based on genetic algorithm for the prediction of summer rainfall over India. The genetic algorithm finds the equations that best describe the temporal variations of the seasonal rainfall over India, and therefore enables the forecasting of the future rainfall. The forecast equation developed in the present study uses the monthly mean rainfall during June, July, and August for past years over five rainfall homogeneous zones of India to predict the seasonal rainfall (JJA combined) over the Indian landmass.

Analysing Predictability in Indian Monsoon Rainfall: A Data Analytic Approach

“…These models make use of historical data to make predictions; however, they have been proved generally unsuccessful for operational forecasts. There have been many attempts to find the most appropriate method for rainfall prediction, and several new methods have been extensively employed in the recent past, for example: wavelet transform (Sahay and Srivastava 2014;Maheswaran and Khosa 2014;Sehgal et al 2014); coupled wavelet-neural network models (Ramana et al 2013); genetic algorithms (Kishtawal et al 2003); empirical mode decomposition (Iyengar and Kanth 2004); and uncertainty analysis (Narsimlu et al 2015), among others. Although, in meteorology, data analytic studies of historical data sets have been traditionally very useful, the most obvious reason for the failure of empirical prediction is the stochastic nature of the rainfall data.…”

“…Keppene and Ghil (1992) have shown that an empirical prediction can be improved by separating the deterministic oscillations from noise in the original time series data. Kishtawal et al (2003) evoked the feasibility of a nonlinear technique based on genetic algorithm (Artificial Intelligence) for the prediction of summer rainfall over India. Guhathakurta (2006) introduced ANN to forecast the summer-monsoon rainfall over the Kerala state in India.…”

Estimating potential evapotranspiration from limited weather data over Gangetic West Bengal, India: a neurocomputing approach

“…Some noteworthy examples towards implementation of such techniques in meteorological modelling are mentioned below. Fuzzy expert systems were applied to predict hazardous weather conditions (Kuciauskas et al, 1998) and a genetic algorithm was employed in prediction of Indian summer monsoon rainfall (Kishtawal et al, 2003). Rough set based rule induction was proved suitable for analysis of environmental data (Berger, 2004) and ANN techniques have been shown to be a potential technique for weather forecasting by several authors, including Silverman and Dracup (2000); Lu et al (2005); Ramírez et al (2005); Lee (2008), and Chattopadhyay and Chattopadhyay (2008).…”

Quantitative Precipitation Forecast of a Tropical Cyclone through Optimal Parameter Estimation in a Convective Parameterization

“…The GAs are based on natural genetic and natural selection mechanism, and some fundamental ideas are borrowed from Genetics in order to artificially construct an optimization procedure (Holland 1992). GAs have been successfully applied to a range of atmospheric problems and have characteristics of easy implementation and capability of achieving global optimal solution (Fang et al 2009;Kishtawal et al 2003;Singh et al 2005a, b).…”