Study on the Prediction of Daily Temperature Based on GDWSSA-KELM Model

Weather forecast is very important to people's daily life. In order to improve the accuracy of weather forecast, in this paper, an improved Gaussian disturbance weighted salp swarm algorithm (GDWSSA) and a GDWSSA-optimized kernel extreme learning machine (GDWSSA-KELM) model is proposed for weather forecasting. GDWSSA is proposed in the model to improve the shortcomings of the original algorithm (SSA) that the convergence speed is too slow and it is easy to fall into the local optimal solution. Simulation results show that GDWSSA outperforms SSA, GA, PSO and GWO in the benchmark functions. Then, GDWSSA optimizes the parameters in the KELM model called GDWSSA-KELM. Finally, the model is used to build the weather prediction model, the results show that the temperature prediction accuracy is better than the traditional machine learning methods. Therefore, the proposed GDWSSA-KELM model prediction model is promising to serve as a powerful auxiliary tool for weather forecast with excellent performance in temperature prediction.