Performance of neural networks in forecasting daily precipitation using multiple sources

Weerasinghe, Hesiri; Premaratne, H. L.; Sonnadara, D.U.J.

doi:10.4038/jnsfsr.v38i3.2305

Cited by 18 publications

(12 citation statements)

References 10 publications

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“…Weerasinghe et al, 2010, have tested the performance of neural network, in an experiment, for forecasting daily precipitation using multiple sources, A cluster of ten neighboring weather stations having 30 years of daily precipitation data (1970 -1999) was used in training and testing the models. Twenty years of daily precipitation data were used to train the networks while ten years of daily precipitation data were used to test the effectiveness of the models.…”

Section: Comprehansive Literature Review 21 Rainfall Predictionmentioning

confidence: 99%

Appropriateness of Neural Networks in Climate Prediction and Interpolations: A Comprehensive Literature Review

Karmakar¹,

Choubey²,

Mishra³

2016

IJAIS

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To be familiar with appropriateness of Neural Network in climate prediction and spatial interpolation, e comprehensive literature review of past 50 years is done and offered in this paper. And it is established that Neural Network such as BPN, RBF is best appropriate to be predicted chaotic behavior of climate variables like rainfall, rainfall runoff, and have efficient enough for prediction in long period. It is also found that Neural Network is significant for spatial interpolation of mean climate variables.

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Section: Comprehansive Literature Review 21 Rainfall Predictionmentioning

confidence: 99%

Appropriateness of Neural Networks in Climate Prediction and Interpolations: A Comprehensive Literature Review

Karmakar¹,

Choubey²,

Mishra³

2016

IJAIS

View full text Add to dashboard Cite

show abstract

“…Twenty years of daily precipitation data were used to train the networks while ten years of daily precipitation data were used to test the effectiveness of the models. They found that the models were able to predict the occurrence of daily precipitation with an accuracy of 79±3% and Fuzzy classification produced a higher accuracy in predicting 'trace' precipitation than other categories [81]. Luenam et al, , 2010 have presented a Neuro-Fuzzy approach for daily rainfall prediction, and their experimental results show that overall classification accuracy of the neuro-fuzzy classifier is satisfactory [82].…”

Section: Literature Reviewmentioning

confidence: 99%

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

Shrivastava¹,

Karmakar²,

Kowar³

et al. 2012

IJCA

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To recognize application of Artificial Neural Networks (ANNs) in weather forecasting, especially in rainfall forecasting a comprehensive literature review from 1923 to 2012 is done and presented in this paper. And it is found that architectures of ANN such as BPN, RBFN is best established to be forecast chaotic behavior and have efficient enough to forecast monsoon rainfall as well as other weather parameter prediction phenomenon over the smaller geographical region.

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“…rainfall-runoff models up to a few hours (Hung et al, 2009) or monthly amounts (Wu et al, 2015;Mislan et al, 2015)). For daily predictions, Weerasinghe et al (2010) used a feedforward back-propagation neural network for daily rainfall prediction in Sri Lanka, which was inspired by the chain-dependent approach from statistics. Kisi & Shiri (2011) applied Genetic Programming (GP) to daily rainfall data, but the GP performed poorly by itself, although when assisted by wavelets the predictive accuracy did improve.…”

Section: Introductionmentioning

confidence: 99%

An extensive evaluation of seven machine learning methods for rainfall prediction in weather derivatives

Cramer

Kampouridis

Freitas

et al. 2017

Expert Systems with Applications

178

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Regression problems provide some of the most challenging research opportunities in the area of machine learning, and more broadly intelligent systems, where the predictions of some target variables are critical to a specific application. Rainfall is a prime example, as it exhibits unique characteristics of high volatility and chaotic patterns that do not exist in other time series data. This work's main impact is to show the benefit machine learning algorithms, and more broadly intelligent systems have over the current state-of-the-art techniques for rainfall prediction within rainfall derivatives. We apply and compare the predictive performance of the current state-of-the-art (Markov chain extended with rainfall prediction) and six other popular machine learning algorithms, namely: Genetic Programming, Support Vector Regression, Radial Basis Neural Networks, M5 Rules, M5 Model trees, and k-Nearest Neighbours. To assist in the extensive evaluation, we run tests using the rainfall time series across data sets for 42 cities, with very diverse climatic features. This thorough examination shows that the machine learning methods are able to outperform the current state-of-the-art. Another contribution of this work is to detect correlations between different climates and predictive accuracy. Thus, these results show the positive effect that machine learning-based intelligent systems have for predicting rainfall based on predictive accuracy and with minimal correlations existing across climates.

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Performance of neural networks in forecasting daily precipitation using multiple sources

Cited by 18 publications

References 10 publications

Appropriateness of Neural Networks in Climate Prediction and Interpolations: A Comprehensive Literature Review

Appropriateness of Neural Networks in Climate Prediction and Interpolations: A Comprehensive Literature Review

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

An extensive evaluation of seven machine learning methods for rainfall prediction in weather derivatives

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