Journal ArticleDOI
All India summer monsoon rainfall prediction using an artificial neural network
Reads0
Chats0
TLDR
In this article, the authors used an ANN with error-back-propagation algorithm to predict Indian summer monsoon rainfall on a seasonal time scale using the Parthasarathy data set.Abstract:
The prediction of Indian summer monsoon rainfall (ISMR) on a seasonal time scales has been attempted by various research groups using different techniques including artificial neural networks The prediction of ISMR on monthly and seasonal time scales is not only scientifically challenging but is also important for planning and devising agricultural strategies This article describes the artificial neural network (ANN) technique with error- back-propagation algorithm to provide prediction (hindcast) of ISMR on monthly and seasonal time scales The ANN technique is applied to the five time series of June, July, August, September monthly means and seasonal mean (June + July + August + September) rainfall from 1871 to 1994 based on Parthasarathy data set The previous five years values from all the five time-series were used to train the ANN to predict for the next year The details of the models used are discussed Various statistics are calculated to examine the performance of the models and it is found that the models could be used as a forecasting tool on seasonal and monthly time scales It is observed by various researchers that with the passage of time the relationships between various predictors and Indian monsoon are changing, leading to changes in monsoon predictability This issue is discussed and it is found that the monsoon system inherently has a decadal scale variation in predictabilityread more
Citations
More filters
Journal ArticleDOI
Current Approaches to Seasonal to Interannual Climate Predictions
Lisa Goddard,Simon J. Mason,Stephen E. Zebiak,Chester F. Ropelewski,Reid Basher,Mark A. Cane +5 more
TL;DR: This article presented an assessment of the current state of knowledge and capability in seasonal climate prediction at the end of the 20th century, including the theory and empirical evidence for predictability, predictions of surface boundary conditions, such as sea surface temperatures (SSTs), and predictions of the climate.
Journal ArticleDOI
Estimation and forecasting of daily suspended sediment data using wavelet–neural networks
Turgay Partal,H. Kerem Cigizoglu +1 more
TL;DR: In this paper, a combined wavelet-ANN method was proposed to estimate and predict the suspended sediment load in rivers by using measured data were decomposed into wavelet components via discrete wavelet transform, and the new wavelet series was used as input for the ANN model.
Journal ArticleDOI
Analyzing trend and forecasting of rainfall changes in India using non-parametrical and machine learning approaches.
Bushra Praveen,Swapan Talukdar,Shahfahad,Susanta Mahato,Jayanta Mondal,Pritee Sharma,Abu Reza Md. Towfiqul Islam,Atiqur Rahman +7 more
TL;DR: The results derived from ECMWF ERA5 reanalysis data exhibit that increasing/decreasing precipitation convective rate, elevated low cloud cover and inadequate vertically integrated moisture divergence might have influenced on change of rainfall in India.
Posted Content
Prediction of crop yields across four climate zones in germany: an artificial neural network approach
Thomas Heinzow,Richard S.J. Tol +1 more
TL;DR: In this paper, the ability of artificial neural network technology to be used for the approximation and prediction of crop yields at rural district and federal state scales in different climate zones based on reported daily weather data was demonstrated.
Journal ArticleDOI
Seasonal forecasting of Thailand summer monsoon rainfall
TL;DR: In this article, the authors developed a statistical forecasting method for summer monsoon rainfall over Thailand, where the identified predictors are part of the broader El Nino Southern Oscillation (ENSO) phenomenon.
References
More filters
Book
Neural Networks: A Comprehensive Foundation
TL;DR: Thorough, well-organized, and completely up to date, this book examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks.
Book
Introduction To The Theory Of Neural Computation
TL;DR: This book is a detailed, logically-developed treatment that covers the theory and uses of collective computational networks, including associative memory, feed forward networks, and unsupervised learning.
Journal ArticleDOI
Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications
Holger R. Maier,Graeme C. Dandy +1 more
TL;DR: The steps that should be followed in the development of artificial neural network models are outlined, including the choice of performance criteria, the division and pre-processing of the available data, the determination of appropriate model inputs and network architecture, optimisation of the connection weights (training) and model validation.
Book
Practical neural network recipes in C
TL;DR: This paper presents a meta-modelling framework for evaluating the performance of Neural Networks using the NEURAL Program, which automates the very labor-intensive and therefore time-heavy and expensive process of unsupervised training.
Journal ArticleDOI
Recurrent neural networks and robust time series prediction
J. Connor,R.D. Martin,Les Atlas +2 more
TL;DR: A robust learning algorithm is proposed and applied to recurrent neural networks, NARMA(p,q), which show advantages over feedforward neural networks for time series with a moving average component and are shown to give better predictions than neural networks trained on unfiltered time series.
Related Papers (5)
On the Weakening Relationship Between the Indian Monsoon and ENSO
Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications
Holger R. Maier,Graeme C. Dandy +1 more