Prediction of rainfall using artificial neural networks for synoptic station of Mashhad: a case study

Khalili, Najmeh; Khodashenas, Saeed Reza; Davary, Kamran; Baygi, Mohammad Mousavi; Karimaldini, Fatemeh

doi:10.1007/s12517-016-2633-1

Cited by 33 publications

(28 citation statements)

References 11 publications

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“…Likewise, Abbot and Marohasy [3] predicted monthly and seasonal precipitations up to 3 months in advance over Queensland, Australia, by using dynamic, recurrent and time-delay ANNs. More recently, Khalili et al [7] employed the Hurst rescaled range statistical analysis to evaluate the predictability of the available data for monthly precipitation prediction for Mashhad City, Iran. Devi et al [8] applied ANNs for forecasting the rainfall time series using the temporal and spatial rainfall intensity data and proved wavelet Elman models as the best model for rainfall forecasting.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Based Ensemble Modeling for Multi-Station Prediction of Precipitation

et al. 2019

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The aim of ensemble precipitation prediction in this paper was to achieve the best performance via artificial intelligence (AI) based modeling. In this way, ensemble AI based modeling was proposed for prediction of monthly precipitation with three different AI models (feed forward neural network-FFNN, adaptive neural fuzzy inference system-ANFIS and least square support vector machine-LSSVM) for the seven stations located in the Turkish Republic of Northern Cyprus (TRNC). Two scenarios were examined each having specific inputs set. The scenario 1 was developed for predicting each station’s precipitation through its own data at previous time steps while in scenario 2, the central station’s data were imposed into the models, in addition to each station’s data, as exogenous input. Afterwards, the ensemble modeling was generated to improve the performance of the precipitation predictions. To end this aim, two linear and one non-linear ensemble techniques were used and then the obtained outcomes were compared. In terms of efficiency measures, the averaging methods employing scenario 2 and non-linear ensemble method revealed higher prediction efficiency. Also, in terms of Skill score, non-linear neural ensemble method could enhance predicting efficiency up to 44% in the verification step.

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Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Based Ensemble Modeling for Multi-Station Prediction of Precipitation

et al. 2019

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“…For instance, Peyghami and Khanduzi () reported very good accuracy in one lead forecast using an ANN for automotive price time series data with a higher Hurst exponent. Similarly, in rainfall prediction studies, the work reported by Khalili et al () is the only study which shows a good one lead prediction of a monthly time series with a higher Hurst exponent. Since only limited work has been reported on the relationship between rainfall predictability and the Hurst exponent, it is necessary to have a more detailed understanding of this relationship.…”

Section: Introductionmentioning

confidence: 90%

An investigation on the relationship between the Hurst exponent and the predictability of a rainfall time series

Chandrasekaran

Poomalai

Balamurali

et al. 2019

Meteorological Applications

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Rainfall prediction is a very challenging task due to its dependence on many meteorological parameters. Because of the complex nature of rainfall, the uncertainty associated with its predictability continues to be an issue in rainfall forecasting. The Hurst exponent is considered as a measure of persistence and it is believed that if a time series has persistence (as reflected by a Hurst exponent value greater than 0.5) it is also predictable. However, very limited studies have been carried out to demonstrate this hypothesis. This study, through experimental work on hypothetical data as well as real data, demonstrates that the Hurst exponent can be taken as an indicator for predictability provided that the exponent values at “smaller levels” of the time series are also significantly greater than 0.5 together with the Hurst exponent of the overall time series. It is also shown that it is better to predict the “similarity” aspect associated with a time series (and derive the predicted rainfall) than to predict the rainfall directly.

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“…The architecture of an artificial neural network, that is, its structure and type of network is one of the most important choices concerning the implementation of neural networks as forecasting tools. The design of MLPNN is motivated by the structure of a biological neuron system capable of parallel processing like a human brain, but the processing elements of this machine learning tool has gone far from their biological inspiration [1,2,3]. For this reason, MLPNN have been successfully used by most of the researchers in the field of forecasting, science and engineering to predict the behavior of both linear and nonlinear systems without the need to make assumptions that are implicit in most traditional statistical approaches [2,4,5,6].…”

Section: Introductionmentioning

confidence: 99%

“…For this reason, MLPNN have been successfully used by most of the researchers in the field of forecasting, science and engineering to predict the behavior of both linear and nonlinear systems without the need to make assumptions that are implicit in most traditional statistical approaches [2,4,5,6]. With all its promising results, the biggest challenge with MLPNN is the selection of an appropriate model since there are different MLPNN model structures, training algorithms, activation functions, learning rate, momentum and number of epochs to choose from [1,7]. This makes it hard to find the proper model for a particular problem [4].…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Multilayer Perceptron Neural Network Models in Week-Ahead Rainfall Forecasting

Velasco¹,

Serquiña²,

Zamad³

et al. 2019

ijacsa

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Multilayer perceptron neural network (MLPNN) is considered as one of the most efficient forecasting techniques which can be implemented for the prediction of weather occurrence. As with any machine learning implementation, the challenge on the utilization of MLPNN in rainfall forecasting lies in the development and evaluation of MLPNN models which delivers optimal forecasting performance. This research conducted performance analysis of MLPNN models through data preparation, model designing, and model evaluation in order to determine which parameters are the best-fit configurations for MLPNN model implementation in rainfall forecasting. During rainfall data preparation, imputation process and spatial correlation evaluation of weather variables from various weather stations showed that the geographical location of the chosen weather stations did not have a direct correlation between stations with respect to rainfall behavior leading to the decision of utilizing the weather station having the most complete weather data to be fed in the MLPNN. By conducting performance analysis of MLPNN models with different combinations of training algorithms, activation functions, learning rate, and momentum, it was found out that MLPNN model having 100 hidden neurons with Scaled Conjugate Gradient training algorithm and Sigmoid activation function delivered the lowest RMSE of 0.031537 while another MLPNN model having the same number of hidden neurons, the same activation function but Resilient Propagation as training algorithm had the lowest MAE of 0.0209. The results of this research showed that performance analysis of MLPNN models is a crucial process in model implementation of MLPNN for week-ahead rainfall forecasting.

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Prediction of rainfall using artificial neural networks for synoptic station of Mashhad: a case study

Cited by 33 publications

References 11 publications

Artificial Intelligence Based Ensemble Modeling for Multi-Station Prediction of Precipitation

Artificial Intelligence Based Ensemble Modeling for Multi-Station Prediction of Precipitation

An investigation on the relationship between the Hurst exponent and the predictability of a rainfall time series

Performance Analysis of Multilayer Perceptron Neural Network Models in Week-Ahead Rainfall Forecasting

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