Prediction of Precipitation Based on Recurrent Neural Networks in Jingdezhen, Jiangxi Province, China

Kang, Jinle; Wang, Huimin; Yuan, Feifei; Wang, Zhiqiang; Huang, Jing; Qiu, Tian

doi:10.3390/atmos11030246

Cited by 53 publications

(28 citation statements)

References 43 publications

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“…A network where information enters the LSTM can be judged by rules. Only the information that accords with the authentication will be left behind, and the discrepant information is forgotten by the forget gate [61].…”

Section: Literature Reviewmentioning

confidence: 99%

An ANN Model Trained on Regional Data in the Prediction of Particular Weather Conditions

et al. 2021

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Artificial Neural Networks (ANNs) have proven to be a powerful tool for solving a wide variety of real-life problems. The possibility of using them for forecasting phenomena occurring in nature, especially weather indicators, has been widely discussed. However, the various areas of the world differ in terms of their difficulty and ability in preparing accurate weather forecasts. Poland lies in a zone with a moderate transition climate, which is characterized by seasonality and the inflow of many types of air masses from different directions, which, combined with the compound terrain, causes climate variability and makes it difficult to accurately predict the weather. For this reason, it is necessary to adapt the model to the prediction of weather conditions and verify its effectiveness on real data. The principal aim of this study is to present the use of a regressive model based on a unidirectional multilayer neural network, also called a Multilayer Perceptron (MLP), to predict selected weather indicators for the city of Szczecin in Poland. The forecast of the model we implemented was effective in determining the daily parameters at 96% compliance with the actual measurements for the prediction of the minimum and maximum temperature for the next day and 83.27% for the prediction of atmospheric pressure.

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Section: Literature Reviewmentioning

confidence: 99%

An ANN Model Trained on Regional Data in the Prediction of Particular Weather Conditions

et al. 2021

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“…There are different architectures of ANN; however, the most common model is a Multi-Layer Perceptron (MLP) neural network, which has a structure with an input layer, single or multiple hidden layers, and an output layer. The MLP has been widely used to forecast several phenomena in meteorology and hydroclimatology [3,9,17,[54][55][56][57]. The typical mathematical expression of the ANN is:…”

Section: Building a Model Using Artificial Neural Networkmentioning

confidence: 99%

Monthly Rainfall Anomalies Forecasting for Southwestern Colombia Using Artificial Neural Networks Approaches

Canchala

Alfonso-Morales

Carvajal-Escobar

et al. 2020

Water

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Improving the accuracy of rainfall forecasting is relevant for adequate water resources planning and management. This research project evaluated the performance of the combination of three Artificial Neural Networks (ANN) approaches in the forecasting of the monthly rainfall anomalies for Southwestern Colombia. For this purpose, we applied the Non-linear Principal Component Analysis (NLPCA) approach to get the main modes, a Neural Network Autoregressive Moving Average with eXogenous variables (NNARMAX) as a model, and an Inverse NLPCA approach for reconstructing the monthly rainfall anomalies forecasting in the Andean Region (AR) and the Pacific Region (PR) of Southwestern Colombia, respectively. For the model, we used monthly rainfall lagged values of the eight large-scale climate indices linked to the El Niño Southern Oscillation (ENSO) phenomenon as exogenous variables. They were cross-correlated with the main modes of the rainfall variability of AR and PR obtained using NLPCA. Subsequently, both NNARMAX models were trained from 1983 to 2014 and tested for two years (2015–2016). Finally, the reconstructed outputs from the NNARMAX models were used as inputs for the Inverse NLPCA approach. The performance of the ANN approaches was measured using three different performance metrics: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Pearson’s correlation (r). The results showed suitable forecasting performance for AR and PR, and the combination of these ANN approaches demonstrated the possibility of rainfall forecasting in these sub-regions five months in advance and provided useful information for the decision-makers in Southwestern Colombia.

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“…However, due to climate changes observed in the past decades, an evaluation of the meteorological parameters has become more complex. This makes the precipitation prediction a challenging task [4].…”

Section: Introductionmentioning

confidence: 99%

Precipitation Forecasting in Northern Bangladesh Using a Hybrid Machine Learning Model

et al. 2022

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Precipitation forecasting is essential for the assessment of several hydrological processes. This study shows that based on a machine learning approach, reliable models for precipitation prediction can be developed. The tropical monsoon-climate northern region of Bangladesh, including the Rangpur and Sylhet division, was chosen as the case study. Two machine learning algorithms were used: M5P and support vector regression. Moreover, a novel hybrid model based on the two algorithms was developed. The performance of prediction models was assessed by means of evaluation metrics and graphical representations. A sensitivity analysis was also carried out to assess the prediction accuracy as the number of exogenous inputs reduces and lag times increases. Overall, the hybrid model M5P-SVR led to the best predictions among used models in this study, with R2 values up to 0.87 and 0.92 for the stations of Rangpur and Sylhet, respectively.

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Prediction of Precipitation Based on Recurrent Neural Networks in Jingdezhen, Jiangxi Province, China

Cited by 53 publications

References 43 publications

An ANN Model Trained on Regional Data in the Prediction of Particular Weather Conditions

An ANN Model Trained on Regional Data in the Prediction of Particular Weather Conditions

Monthly Rainfall Anomalies Forecasting for Southwestern Colombia Using Artificial Neural Networks Approaches

Precipitation Forecasting in Northern Bangladesh Using a Hybrid Machine Learning Model

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