| Cite this article: | 黄林显,张明芳,钱永,等.三种水质动态预测模型在米山水库的应用与对比[J].灌溉排水学报,0,():-. |
| HUANG Linxian,ZHANG Mingfang,QIAN Yong,et al.三种水质动态预测模型在米山水库的应用与对比[J].灌溉排水学报,0,():-. |
|
| |
|
|
| This article has been：Browse 787Times Download 0Times |
|
|
|
| DOI： |
|
| Application and Comparison of Three Water Quality Prediction Models in Mishan Reservoir |
|
HUANG Linxian1, ZHANG Mingfang2, QIAN Yong3, XING Xuerui4, XING Liting1, HAN Zhong5
|
|
1.School of Water Conservancy and Environment,University of Jinan;2.Weihai Hydrological Center;3.Institute of Hydrogeology and Environmental Geology,Chinese Academy of Geological Sciences;4.Shandong Zhengyuan Geological Resources Exploration coltd;5.No Institution of Geology and Mineral Resources Exploration of Shandong Province
|
| Abstract: |
| Accurate prediction of water quality is of great significance to scientific water ecological environment protection and water resource management planning. However, due to the influence of various factors such as human activities, rainfall, temperature, and hydrodynamic conditions, water quality dynamics have complex random characteristics, resulting in large prediction errors. To verify the practicability of different models for water quality prediction, three prediction models of SARIMA, Holt-Winters, and LSTM neural network were respectively constructed. The monthly conductivity data of Mishan Reservoir from 2012 to 2018 was used for model training, and the monthly conductivity data of 2019 was used for verification to examine the accuracy and stability of the three prediction models. The results show that the modeling process of SARIMA and Holt-Winters models is relatively simple, but because they can only consider the time series evolution trend of water quality data, the prediction accuracy is low. The LSTM neural network model can simultaneously consider the time-series evolution trend of water quality data and the before-and-after dependencies between different moments and has strong nonlinear mapping capabilities, so the prediction accuracy is the highest. Generally speaking, the LSTM neural network prediction model has a relatively large error only at the sudden change in the conductivity value, but the overall prediction effect is relatively ideal, so it has more application value in water quality prediction. |
| Key words: Time series model; LSTM model; conductivity; water quality prediction; Mishan reservoir |
|
|
|
|
|
|
