Application of Artificial Neural Networks in Weather Forecasting: A Comprehensive Literature Review
TLDR
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.Abstract:
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.read more
Citations
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Journal ArticleDOI
Rainfall Monthly Prediction Based on Artificial Neural Network: A Case Study in Tenggarong Station, East Kalimantan - Indonesia
TL;DR: An Artificial Neural Network with the Backpropagation Neural Network (BPNN) algorithm has provided a good model to predict rainfall in Tenggarong, East Kalimantan - Indonesia.
Journal ArticleDOI
A Survey on Rainfall Prediction using Artificial Neural Network
TL;DR: A survey of available literature of some methodologies employed by different researchers to utilize ANN for rainfall prediction reports that rainfall prediction using ANN technique is more suitable than traditional statistical and numerical methods.
Journal ArticleDOI
Deep learning-based effective fine-grained weather forecasting model
TL;DR: A novel lightweight data-driven weather forecasting model is proposed by exploring temporal modelling approaches of long short-term memory (LSTM) and temporal convolutional networks (TCN) and compared with the existing classical machine learning approaches, statistical forecasting approaches, and a dynamic ensemble method.
Journal ArticleDOI
Weather forecasting based on hybrid neural model
TL;DR: In this paper, a hybrid neural model (MLP and RBF) was proposed to enhance the accuracy of weather forecasting in Saudi Arabia, where the main input features employed to train individual and hybrid neural networks that include average dew point, minimum temperature, maximum temperature, mean temperature, average relative moistness, precipitation, normal wind speed, high wind speed and average cloudiness.
References
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Proceedings ArticleDOI
A review of wind power and wind speed forecasting methods with different time horizons
TL;DR: In this article, the main challenges and problems associated with wind power prediction are discussed, and an overview of comparative analysis of various available forecasting techniques is discussed as well as a major challenges and major challenges.
Journal ArticleDOI
An artificial neural network approach to rainfall-runoff modelling
TL;DR: The ability of the ANN to cope with missing data and to “learn” from the event currently being forecast in real time makes it an appealing alternative to conventional lumped or semi-distributed flood forecasting models.
Journal ArticleDOI
A neuro-fuzzy computing technique for modeling hydrological time series
TL;DR: Results showed that the ANFIS forecasted flow series preserves the statistical properties of the original flow series, and a comparative analysis suggests that the proposed modeling approach outperforms ANNs and other traditional time series models in terms of computational speed, forecast errors, efficiency, peak flow estimation etc.
Journal ArticleDOI
A hybrid neural network and ARIMA model for water quality time series prediction
TL;DR: A hybrid ARIMA and neural network model is proposed that is capable of exploiting the strengths of traditional time series approaches and artificial neural networks to provide a robust modeling framework capable of capturing the nonlinear nature of the complex time series and thus producing more accurate predictions.
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