Climate Forecasting Through Stochastic Modelling and Neural Networks

Predictive Climate Models and Stochastic Hydrology with Neural Networks explores advanced time series modeling for forecasting hydrological processes. The book focuses on rainfall prediction in Junagadh using a 32-year climatic dataset. Various models like ARMA ANN ANFIS and Hybrid Wavelet-ANN are applied and assessed for their forecasting efficacy over one-year five-year and ten-year periods. Statistical tests such as Chi-square Anderson and Kolmogorov-Smirnov identify the best-fit probability distributions. The performance of ARIMA configurations for short-term forecasts and the effectiveness of algorithms in ANN and ANFIS models are detailed highlighting their superiority in long-term rainfall prediction. This work is vital for those in hydrology and climate science demonstrating how machine learning enhances predictive accuracy in stochastic hydrology.