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Rainfall is a complex process and forecasting rainfall is complicated because it involves a lot of parameters but precise rainfall forecasting is necessary for many human activities. Even though considerable research work has been done in rainfall prediction, comparatively fewer efforts have been made on Sri Lankan monsoon rainfall prediction using large scale climatic teleconnections. In this paper,...
Prediction of monsoon rainfall in a timely manner can be highly beneficial for Pakistan, where monsoon is the major source of rain. Presently, Multiple Linear Regression and Statistical Downscaling Models are being used for monsoon rainfall prediction. In spite of making use of a large number of resources and having dependency on a number of parameters, the results of these models have not been satisfactory...
Standing the perspective of data mining and using the basic principles of artificial neural network to establish a average extreme rainfall prediction model which is based on BP neural netwok.This model only use the extreme precipitation indexes as the factors to predict the average extreme rainfall in the coming year.The model combined with stepwise regression to select input vectors and used bayesian...
The artificial neural networks (ANNs) have been applied to various hydrologic problems recently. This research demonstrates a temporal approach by applying Jordan and Elman network for rainfall-runoff modeling for the upper area of Wardha River in India. The model is developed by processing online data over time using recurrent connections. Methodologies and techniques of the two models are presented...
Rainfall prediction generally requires reliable hydrological models as well as relevant information of meteorological and geographical data. In this paper, a model based on artificial neural networks (ANNs) and wavelet decomposition is proposed as a learning tool to predict consecutive daily rainfalls on accounts of the preceding events of rainfall data. Two sets of wavelet coefficients, for which...
In this paper, a novel artificial neural network ensemble rainfall forecasting model is proposed for rainfall forecasting based on K-nearest neighbor nonparametric estimation of regression. In this model, original data set are partitioned into some different training subsets via Bagging technology. Then different ANN algorithms and different network architecture generate diverse individual neural...
Attempts to predict long-range monsoon rainfall over the subdivision EPMB, Three layer perception feed forward back propagation deterministic and probabilistic artificial neural network models have been developed. 61 years data for 1945-2006 have been used, of which the first 51 years (1945-1995) of data are used for training the network and data for the period 1996-2006 are used independently for...
True information about rainfall is crucial for human activities such as the use and the management of water resources, hydroelectric power projects, warning to impend droughts or floods, urban areas sewer systems and many others. This paper investigates the development of an efficient model to forecast monthly monsoon rainfall for a number of stations, namely Barishal, Chittagong, Dhaka, Khulna, Rajshahi...
In this paper, the primitive monthly precipitation series are reconstructed as independent variables by mean generating function, and the primitive rainfall series are dependent variables. The factor affecting is withdrew by means of principal component analysis method to extract the most important components so that it can be input as the neural network, and a forecast model of the neural network...
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