Andere Kunden interessierten sich auch für
![The Multi-model Ensemble Rainfall Forecasting over the GHA Region]( "The Multi-model Ensemble Rainfall Forecasting over the GHA Region")
The Multi-model Ensemble Rainfall Forecasting over the GHA Region39,99 €![Convolution Recurrent Neural Networks for Image Classification]( "Convolution Recurrent Neural Networks for Image Classification")
Convolution Recurrent Neural Networks for Image Classification31,99 €![DHR Modelling of Cycles in Rainfall and Riverflow across Ghana]( "DHR Modelling of Cycles in Rainfall and Riverflow across Ghana")
DHR Modelling of Cycles in Rainfall and Riverflow across Ghana39,99 €![Deep Neural Networks in Medical Imaging]( "Deep Neural Networks in Medical Imaging")
Deep Neural Networks in Medical Imaging90,99 €![On Spatio-Temporal Data Modelling and Uncertainty Quantification Using Machine Learning and Information Theory]( "On Spatio-Temporal Data Modelling and Uncertainty Quantification Using Machine Learning and Information Theory")
On Spatio-Temporal Data Modelling and Uncertainty Quantification Using Machine Learning and Information Theory121,99 €![On Spatio-Temporal Data Modelling and Uncertainty Quantification Using Machine Learning and Information Theory]( "On Spatio-Temporal Data Modelling and Uncertainty Quantification Using Machine Learning and Information Theory")
On Spatio-Temporal Data Modelling and Uncertainty Quantification Using Machine Learning and Information Theory160,49 €![Long Term Forecasting of Climate Change and Effects on Fars Province]( "Long Term Forecasting of Climate Change and Effects on Fars Province")
Long Term Forecasting of Climate Change and Effects on Fars Province35,99 €
"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.