Multiple kernel fusion: A novel approach for lake water depth modeling

Safari, Mir Jafar Sadegh; Arashloo, Shervin Rahimzadeh; Vaheddoost, Babak

doi:10.1016/j.envres.2022.114856

Cited by 1 publication

(1 citation statement)

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“…The hyperparameters that usually have significant impacts on the performance of BPNN are as follows: the number of hidden layer neurons, the activation function, the initial weights and biases, the learning rate, the number of epochs, and the error goal [39]. In this paper, the range for the number of hidden layer neurons is determined by Equation (1).…”

Section: Back-propagation Neural Network (Bpnn)mentioning

confidence: 99%

A Study of Precipitation Forecasting for the Pre-Summer Rainy Season in South China Based on a Back-Propagation Neural Network

Wang,

Liu,

Zeng

et al. 2024

Water

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In South China, the large quantity of rainfall in the pre-summer rainy season can easily lead to natural disasters, which emphasizes the importance of improving the accuracy of precipitation forecasting during this period for the social and economic development of the region. In this paper, the back-propagation neural network (BPNN) is used to establish the model for precipitation forecasting. Three schemes are applied to improve the model performance: (1) predictors are selected based on individual meteorological stations within the region rather than the region as a whole; (2) the triangular irregular network (TIN) is proposed to preprocess the observed precipitation data for input of the BPNN model, while simulated/forecast precipitation is the expected output; and (3) a genetic algorithm is used for the hyperparameter optimization of the BPNN. The first scheme reduces the mean absolute percentage error (MAPE) and the root mean square error (RMSE) of the simulation by roughly 5% and more than 15 mm; the second reduces the MAPE and RMSE by more than 15% and 15 mm, respectively, while the third improves the simulation inapparently. Obviously, the second scheme raises the upper limit of the model simulation capability greatly by preprocessing the precipitation data. During the training and validation periods, the MAPE of the improved model can be controlled at approximately 35%. For precipitation hindcasting in the test period, the anomaly rate is less than 50% in only one season, and the highest is 64.5%. According to the anomaly correlation coefficient and Ps score of the hindcast precipitation, the improved model performance is slightly better than the FGOALS-f2 model. Although global climate change makes the predictors more variable, the trend of simulation is almost identical to that of the observed values over the whole period, suggesting that the model is able to capture the general characteristics of climate change.

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Section: Back-propagation Neural Network (Bpnn)mentioning

confidence: 99%