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

Shrivastava, Gyanesh; Karmakar, S.; Kowar, M. K.; Guhathakurta, Pulak

doi:10.5120/8142-1867

Cited by 53 publications

(31 citation statements)

References 70 publications

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“…Aviles et al, [7], have proposed Markov Chain (MC) and Bayesian Network (BN) based models for drought forecasting and used the BNs and the ranked probability skill score (RPSS) to evaluate the performance of the models in copula functions and concluded that the above model provides better forecast of droughts. Ali et al, [6], have used a multilayer perceptron neural network (MLPNN) algorithm for drought forecasting and found that the MLPNN has potential capability for SPEI drought forecasting based on performance measures, like Mean Average Error (MAE), the coefficient of correlation (R), and Root Mean Square Error (RMSE) and finally they have concluded that the above ANN model is very useful for forecasting drought.…”

Section: Literature Reviewmentioning

confidence: 99%

Application of Neural Network in Drought Forecasting; An Intense Literature Review

Yadu¹,

Shrivastava²

2019

IJCET

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India is the agrarian country. The overall economy of our country is based on agriculture. Although the methods of cultivation are traditional and not hi-tech thus more over 75% of our farmers are dependent on monsoon. Prediction of actual monsoon is a challenge for meteorological scientists. Since the climatic data time series shows highly non-linear and chaotic behavior thus its forecast is still an enigma. Thus, forecasting of climate phenomenon is a challenging issue for the researchers round the globe. However, it is a prime necessity to forecast climatic changes such as Rainfall (daily rainfall, monthly rainfall, heavy rainfall etc.), Flood, Drought, minimum and maximum Temperature, River flow etc. To recognize applications of Artificial Neural Network (ANNs) in weather forecasting, especially in drought forecasting a comprehensive literature review from 2000 to 2017 is done and presented in this paper. In the study, more over 90 contributions have been surveyed and it has been observed that the architecture of ANN such as BPN, RBFN, MLP, ANFIS, ARIMA etc. are found best to forecast chaotic behavior and have efficient enough to forecast drought as well as other weather phenomenon over broader or smaller homogeneous region.

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Section: Literature Reviewmentioning

confidence: 99%

Application of Neural Network in Drought Forecasting; An Intense Literature Review

Yadu¹,

Shrivastava²

2019

IJCET

Self Cite

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“…There are many types of neural networks used to solve classification, regression and clustering problems. NN has been utilized for diverse applications like precipitation prediction [7][8][9], water resources studies [10], meteorology and oceanography [11], weather forecasting [12,13], climate variability [7,9] and other climate-related studies. Neural networks have been found useful to extract the non-linear relationships in climate variables [14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Future Climate of Colombo Downscaled with SDSM-Neural Network

Dorji

Herath

Mishra

2017

Climate

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Abstract:The Global Climate Model (GCM) run at a coarse spatial resolution cannot be directly used for climate impact studies. Downscaling is required to extract the sub-grid and local scale information. This paper investigates if the artificial neural network (ANN) is better than the widely-used regression-based statistical downscaling model (SDSM) for downscaling climate for a site in Colombo, Sri Lanka. Based on seasonal and annual model biases and the root mean squared error (RMSE), the ANN performed better than the SDSM for precipitation. This paper proposes a novel methodology for improving climate predictions by combining SDSM with neural networks. This method will allow a user to apply SDSM with a neural network model for higher skills in downscaling. The study uses the Canadian Earth System Model (CanESM2) of the IPCC Fifth Assessment Report, reanalysis from the National Center for Environmental Prediction (NCEP), and the Asian Precipitation Highly Resolved Observational Data Integration towards Evaluation of Water Resources (APHRODITE) project data as the observation. SDSM and the focused time-delayed neural network (TDNN) models are used for the downscaling. The projected annual increase for Representative Concentration Pathway (RCP) is 8.5; the average temperature is 2.83 • C (SDSM) and 3.03 • C (TDNN), and rainfall is 33% (SDSM) and 63% (TDNN) for 2080's.

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“…Artificial Neural Network (ANN) algorithms have been successfully applied in rainfall classification. Among the different ANN algorithms applied, the Backpropagation Neural Network (BPN) and Radial Basis Function Network (RBFN) are the two most commonly used in rainfall prediction and yield satisfactory results [4]. However, there is no research on verifying which method applied better accuracy result as comparing to the other.…”

Section: Introductionmentioning

confidence: 99%

Rainfall Classification for Flood Prediction Using Meteorology Data of Kuching, Sarawak, Malaysia: Backpropagation vs Radial Basis Function Neural Network

Chai¹,

Wong²,

Goh³

2017

IJESD

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Abstract-Rainfall is often defined by stochastic process due to its random characteristics, i.e. space and time dependent and it is therefore, not easy to predict. In general, rainfall is a highly non-linear and complicated phenomenon. In order to acquire an accurate prediction, advanced computer modeling and simulation is required. Artificial Neural Network (ANN) has been successfully used to predict the behavior of such non-linear system. Among the different types of ANN models used, Backpropagation Network (BPN) and Radial Basis Function Networks (RBFN) are the two common ANN models that had produced valuable results. However, there was no study conducted to research on which, among these two methods, is the better model for rainfall forecast. Therefore, this study will fill this gap by comparing the capabilities of these two ANN models in rainfall forecast using metrological data from year 2009 to 2013 obtained from Malaysian Meteorological Department for Kuching, Sarawak, Malaysia. From the research, it is concluded that, BPN (MSE≈0.16, R≈0.86) performs better as compared to RBFN (MSE≈0.22, R≈0.82). The strengths and weaknesses of these models are also presented in this paper.

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

Cited by 53 publications

References 70 publications

Application of Neural Network in Drought Forecasting; An Intense Literature Review

Application of Neural Network in Drought Forecasting; An Intense Literature Review

Future Climate of Colombo Downscaled with SDSM-Neural Network

Rainfall Classification for Flood Prediction Using Meteorology Data of Kuching, Sarawak, Malaysia: Backpropagation vs Radial Basis Function Neural Network

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