Analysis and prediction of rainfall in agriculture based on deep learning framework

Abstract

Agriculture has been paid great attention in the field of the newlinesocio-economic framework of India. Agriculture is also considered as the newlineunique business based on factors, such as climate and economy. Therefore, newlinerainfall prediction is the esteemed area of the research, which impacts the newlineday-to-day life of the Indians, as their source is agriculture. Similarly, rainfall newlineprediction is also a live research area that supports the farmers in taking a newlineclear decision concerning to agriculture especially in irrigation and in newlinecultivation. The existing rainfall prediction methodologies resulted in newlinemassive time consumption and influenced huge computational efforts newlineassociated with the investigation. newlineIn this research work, the problems in the prediction of rainfall newlinehave been addressed. A comprehensive survey is presented which newlineinvestigates the rainfall prediction methodologies based on the traditional newlineforecasting, statistical methods-based prediction, data mining-based newlineprediction, machine learning based prediction and deep learning-based newlineprediction. newlineThe datasets have been taken form the Open Government Data newline(OGD) Platform India. This dataset comprises of the subdivision wise newlineRainfall and its departure for 117 years of whole India and the state-wise newlinedata. We have incorporated the data of India and Tamilnadu (A Southern newlineState in India). These datasets are then segregated into 6 different datasets. newlineThe segregated data falls in three categories based on year-wise data, monthwise newlinedata, and quarterly wise data. newline