Optimizing convolutional neural network with Water strider algorithm in weather forecasting

One of the most important and practical issues in today's world is weather forecasting. Forecasting the weather condition reduces the losses and damages of critical weather conditions. Weather forecasting can be effective in applications such as agriculture and air transportation. One of the methods that can be used to learn and predict the weather is deep learning networks, including convolutional neural networks. One of the important challenges of the convolutional neural network is that it performs feature selection unintelligent by using a number of convolution operations. In this article, a collective intelligence method is presented to improve the accuracy of forecasting weather conditions by convolutional neural network. The evaluations using the data sets related to weather conditions show that the proposed method, which has an accuracy and sensitivity of 96.32% and 96.14%, respectively, compared to the CNN deep learning network, has been able to achieve prediction accuracy of about increase by 8.35%.