Pan Evaporation Prediction Using LSTM Models Based on PCA Factor Reduction and Firefly Optimization Algorithm

Evaporation is an important part of the moisture exchange between the earth and the air. Understanding the trend of pan evaporation can help to reveal the status of actual evaporation, which is very useful for the allocation of regional water resources. However, long short-term memory (LSTM) has become a mainstream algorithm for predicting pan evaporation, there are two issues worth considering. One of the issues is how to automatically find the optimal hyperparameters, the other is how to eliminate the correlation between prediction factors to improve prediction performance. To address the two issues, this article proposes LSTM models based on principal component analysis (PCA) factor reduction and firefly optimization algorithm. In the proposed model, fire-fly algorithm can find the optimal hyperparameters, and PCA can eliminate the correlation between prediction factors. Xiangjiang River Basin, an important Basin for China’s water resource management, is selected as a study area, the experimental results are evaluated by root mean square error (RMSE) and the coefficient of determination ( $R^{2}$ ). The results show that the proposed models can successfully predict daily pan evaporation of the study area.