Improvement of the neural network group method of data modeling using Water strider algorithm in temperature prediction
Proper temperature forecasting is of significant importance in adapting to climate change at local scales. For this purpose, in this research, feature selection is done using principal component analysis algorithm, then in the post-processing stage of the neural network, the group method of data modeling is improved using the water strider algorithm, so that the temperature prediction can be done optimally. In order to compare, the results show a decrease in mean square error of 0.0469 in the proposed method compared to the feature selection method using mlp.
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