Open AccessArtificial Neural Network for Rainfall Analysis Using Deep Learning Techniques
Author(s) -
S. Nandakumar,
R. Valarmathi,
P Sudha Juliet,
G. Brindha
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1964/4/042022
Subject(s) - artificial neural network , productivity , precipitation , computer science , agriculture , agricultural productivity , nonlinear system , production (economics) , weather forecasting , machine learning , meteorology , environmental science , artificial intelligence , agricultural engineering , engineering , geography , physics , archaeology , quantum mechanics , economics , macroeconomics
The estimation of rainfall is one of the most critical and daunting challenges in today’s environment. Weather and rainfall are typically extremely nonlinear and dynamic, needing sophisticated machine models and simulation for forecasting accurately. The economy of India is agriculture and is focused primarily on crop production and precipitation. Predictions of rainfall are important for all farmers to assess crop productivity. Rainfall forecast involves the application of science and technology to determine weather conditions. In order to utilize water supplies efficiently, the crop productivity and the pre-program of water systems, it is necessary to determine the precipitation in detail. The actions of such nonlinear processes can be modeled using an Artificial Neural Network (ANN). Most researchers in this area have been effectively utilizing ANN for the past 25 years. This article offers you an summary of some of the methodologies valid for using ANN for rainfall prediction by numerous researchers.The survey also states that forecasts of rainfall using ANN technologies are more accurate than conventional mathematical and numerical approaches.