Lake eutrophication prediction based on improved MIMO-DD-3Q Learning

Author:

Wang Li,Ning Chaoran,Wang Xiaoyi,Xu JipingORCID,Zhao Zhiyao,Yu Jiabin,Zhang Huiyan,Sun Qian,Bai Yuting,Jin Xuebo,Tang Qianhui

Abstract

As for the problem that the traditional single depth prediction model has poor strain capacity to the prediction results of time series data when predicting lake eutrophication, this study takes the multi-factor water quality data affecting lake eutrophication as the main research object. A deep reinforcement learning model is proposed, which can realize the mutual conversion of water quality data prediction models at different times, select the optimal prediction strategy of lake eutrophication at the current time according to its own continuous learning, and improve the reinforcement learning algorithm. Firstly, the greedy factor, the fixed parameter of Agent learning training in reinforcement learning, is introduced into an arctangent function and the mean value reward factor is defined. On this basis, three Q estimates are introduced, and the weight parameters are obtained by calculating the realistic value of Q, taking the average value and the minimum value to update the final Q table, so as to get an Improved MIMO-DD-3Q Learning model. The preliminary prediction results of lake eutrophication are obtained, and the errors obtained are used as the secondary input to continue updating the Q table to build the final Improved MIMO-DD-3Q Learning model, so as to achieve the final prediction of water eutrophication. In this study, multi-factor water quality data of Yongding River in Beijing were selected from 0:00 on July 26, 2021 to 0:00 on September 5, 2021. Firstly, data smoothing and principal component analysis were carried out to confirm that there was a certain correlation between all factors in the occurrence of lake eutrophication. Then, the Improved MIMO-DD-3Q Learning prediction model was used for experimental verification. The results show that the Improved MIMO-DD-3Q Learning model has a good effect in the field of lake eutrophication prediction.

Publisher

Public Library of Science (PLoS)

Subject

Multidisciplinary

Reference35 articles.

1. Paleo-diatom records reveal ecological change not detected using traditional measures of lake eutrophication.[J];Gregersen Rose;The Science of the total environment,2023

2. Adsorption of humic acids to lake sediments: Compositional fractionation, inhibitory effect of phosphate, and implications for lake eutrophication[J];ShuaiLong Wen;Journal of Hazardous Materials,2022

3. Evaluation of Phosphorus Flows in the Dianchi Watershed, Southwest of China[J].;Yi Liu;Population and Environment,2004

4. Three-layered Feedforward artificial neural network with dropout for short-term prediction of class-differentiated Chl-a based on weekly water-quality observations in a eutrophic agricultural reservoir[J].;Ren Yamamoto;Paddy and Water Environment,2021

5. The application of stressor–response relationships in the management of lake eutrophication[J].;Marc Schallenberg;Inland Waters,2020

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3