In the recent past, the prediction of rail fall based on environmental changes plays important role in whether forecasting based on remote sensing application. India is highly based on the agriculture and the economy of agriculture is highly dependent on the crop productivity. For proper investigation of the rainfall prediction is needed for all farmers. The rainfall prediction is the application of science and technology to predict the state of atmosphere. The rainfall datasets are collected from the period of 2010 to 2013 from 50 meters meteorological tower of Sathyabama University, Kanchipuram district, Tamil Nadu, India. In this paper, we are proposed rainfall prediction technique using the classification technique such as Deep convolutional Neural Network (DCNN). The predicted rainfall parameters are further optimized using Improved Particle Swarm Optimization (IPSO). The spatial data mining technique is applied to estimate the rainfall. The experimental results are shown with comparing other existing techniques to prove that the proposed algorithm such as BPNN and IPSO are highly accurate and efficient than other systems. The proposed BPNN is tested perfectly with architecture learning rate and momentum parameters.
Volume 11 | 02-Special Issue
Pages: 318-328