Summer Precipitation Forecast Using an Optimized Artificial Neural Network with a Genetic Algorithm for Yangtze-Huaihe River Basin, China

Owing to the complexity of the climate system and limitations of numerical dynamical models, machine learning based on big data has been used for climate forecasting in recent years. In this study, we attempted to use an artificial neural network (ANN) for summer precipitation forecasts in the Yangt...

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Vydáno v:Atmosphere Ročník 13; číslo 6; s. 929
Hlavní autoři: Zhang, Zhi-Cheng, Zeng, Xin-Min, Li, Gen, Lu, Bo, Xiao, Ming-Zhong, Wang, Bing-Zeng
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.06.2022
Témata:
Algorithms
Artificial intelligence
artificial neural network
Artificial neural networks
Atmospheric circulation
Atmospheric models
Back propagation
Back propagation networks
Big Data
Climate
Climate models
Climate system
Coefficients
Correlation coefficient
Correlation coefficients
Dynamic models
genetic algorithm
Genetic algorithms
Machine learning
Modelling
Monthly precipitation
Neural networks
Precipitation
Precipitation forecasting
River basins
Rivers
Sea surface
Sea surface temperature
Summer
Summer precipitation
summer precipitation forecast
Support vector machines
Surface temperature
Training
Weather forecasting
Yangtze-Huaihe River Basin
China
Huaihe River
ISSN:2073-4433, 2073-4433
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Shrnutí:Owing to the complexity of the climate system and limitations of numerical dynamical models, machine learning based on big data has been used for climate forecasting in recent years. In this study, we attempted to use an artificial neural network (ANN) for summer precipitation forecasts in the Yangtze-Huaihe River Basin (YHRB), eastern China. The major ANN employed here is the standard backpropagation neural network (BPNN), which was modified for application to the YHRB. Using the analysis data of precipitation and the predictors/factors of atmospheric circulation and sea surface temperature, we calculated the correlation coefficients between precipitation and the factors. In addition, we sorted the top six factors for precipitation forecasts. In order to obtain accurate forecasts, month (factor)-to-month (precipitation) forecast models were applied over the training and validation periods (i.e., summer months over 1979–2011 and 2012–2019, respectively). We compared the standard BPNN with the BPNN using a genetic algorithm-based backpropagation (GABP), support vector machine (SVM) and multiple linear regression (MLR) for the summer precipitation forecast after the model training period, and found that the GABP method is the best among the above methods for precipitation forecasting, with a mean absolute percentage error (MAPE) of approximately 20% for the YHRB, which is substantially lower than the BPNN, SVM and MLR values. We then selected the best summer precipitation forecast of the GABP month-to-month models by summing up monthly precipitation, in order to obtain the summer scale forecast, which presents a very successful performance in terms of evaluation measures. For example, the basin-averaged MAPE and anomaly rate reach 4.7% and 88.3%, respectively, for the YHRB, which can be a good recommendation for future operational services. It appears that sea surface temperatures (SST) in some key areas dominate the factors for the forecasts. These results indicate the potential of applying GABP to summer precipitation forecasts in the YHRB.
Bibliografie:ObjectType-Article-1
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ISSN:2073-4433
2073-4433
DOI:10.3390/atmos13060929