Analysis of Evaporation Trend Changes in Tai'an City and Prediction Based on Neural Networks
Evaporation is considered an essential indicator within hydrological characteristics.To scientifically and accurately analyze and predict the characteristics and trends of evaporation in Tai'an City,data from four representative hydrological observation stations in Tai'an City—Huangqian Reservoir,Dongzhou Reservoir,Dawenkou,and Daicun Dam—from 1985 to 2021 were utilized.The abrupt change characteristics were analyzed through the Mann-Kendall test and sliding t-test,while the future evaporation trend was forecasted using the R/S analysis method.Daily evaporation observation data from the"Tai'an"station from 2005 to 2022 were employed,and a model coupling the NeuralProphet algorithm with the Optuna algorithm was developed for evaporation prediction.This model's predictive performance has been compared against other forecasting models'evaluation metrics.The findings indicate that the annual and seasonal evaporation rates in Tai'an City demonstrate a clear decreasing trend.For the foreseeable future,this trend is expected to continue in most areas.The forecasted data provided by the model exhibit high accuracy and meet the set standards,proving valuable for daily operations and scientific research guidance.This study offers a novel approach to predicting evaporation.
万方数据
维普
