Long-Range Monsoon Rainfall Pattern Recognition and Prediction for the Subdivision 'EPMB' Chhattisgarh Using Deterministic and Probabilistic Neural Network

Application of Artificial Neural Networks in Weather Forecasting: A Comprehensive Literature Review

“…Using ANNs and Fuzzy Logic, Mayilvaganan, and Naidu, 2011, have tried to predict ground water level and they have concluded that ANN performs better than Fuzzy Logic [105]. It has been also proved by the contribution of Karmakar et al, , 2010, that BPN in deterministic as well as parametric forecast are more efficient technique over the statistical model for forecasting long-range monsoon rainfall over the high resolution geographical region such as district or sub-division level [111][112][113][114]. He has successfully obtained global minima up to the level of 10 -04 during the training period.…”

Feed Forward Back Propagation Neural Network Coupled With Rice Data Simulator for Prediction of Rice Production in Tamilnadu

“…It has been also proved by the contribution of Karmakar et al, [7] that Back Propagation Network forecast are more efficient technique over the statistical model for forecasting long-range monsoon rainfall over the high resolution geographical region such as district or sub-division level. He has successfully obtained global minima up to the level of 10 -04 during the training period.…”

A Survey on Rainfall Prediction using Artificial Neural Network

“…After simulation they have concluded that the method had a high accuracy in denoising and prediction of the rainfall sequence [35]. Karmakar et al (2009) have developed a three layer perception feed forward back propagation deterministic and probabilistic artificial neural network models to predict longrange monsoon rainfall over the subdivision EPMB. 61 years data for 1945-2006 have used, of which the first 51 years (1945-1995) of data were used for training the network and data for the period 1996-2006 were used independently for validation.…”

“…61 years data for 1945-2006 have used, of which the first 51 years (1945-1995) of data were used for training the network and data for the period 1996-2006 were used independently for validation. However they have found that the performance of the model in probabilistic forecast was better evaluated over deterministic forecast [36]. In a Case Study on Jarahi Watershed, Solaimani (2009) has studied Rainfall-runoff Prediction Based on Artificial Neural Network and he concluded that Artificial Neural Network method is more appropriate and efficient to predict the river runoff than classical regression model [37].…”