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

• Published in: PloS one Vol. 18; no. 11; p. e0294278
• Main Authors: Wang, Li, Ning, Chaoran, Wang, Xiaoyi, Xu, Jiping, Zhao, Zhiyao, Yu, Jiabin, Zhang, Huiyan, Sun, Qian, Bai, Yuting, Jin, Xuebo, Tang, Qianhui
• Format: Journal Article
• Language: English
• Published: San Francisco Public Library of Science 14.11.2023; Public Library of Science (PLoS)
• Subjects: Accuracy; Algorithms; Aquatic resources; China; Data smoothing; Decision making; Deep learning; Eutrophic lakes; Eutrophication; Evaluation; Greedy algorithms; Lakes; Learning; Machine learning; Parameters; Prediction models; Predictions; Principal components analysis; Reinforcement; Rivers; Time series; Water quality; China; Chaohu Lake
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0294278

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.