Prediction and Analysis of Spatiotemporal Evolution Trends of Water Quality in Lake Chaohu Based on the WOA-Informer Model

Junyue Tian, Lejun Wang, Qingqing Tian (), Hongyu Yang, Yu Tian, Lei Guo and Wei Luo
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Junyue Tian: Shencheng Sishui Tongzhi Engineering Management Co., Ltd. of Henan Water Conservancy Investment Group, Xinyang 464000, China
Lejun Wang: Shencheng Sishui Tongzhi Engineering Management Co., Ltd. of Henan Water Conservancy Investment Group, Xinyang 464000, China
Qingqing Tian: School of Water Conservancy, North China University of Water Resources and Electric Power, Zhengzhou 450046, China
Hongyu Yang: School of Water Conservancy, North China University of Water Resources and Electric Power, Zhengzhou 450046, China
Yu Tian: China Institute of Water Resources and Hydropower Research, Beijing 100038, China
Lei Guo: Henan Water Valley Innovation Technology Research Institute Co., Ltd., Zhengzhou 450000, China
Wei Luo: Guizhou Water & Power Survey-Design Institute Co., Ltd., Guiyang 550002, China

Sustainability, 2025, vol. 17, issue 21, 1-28

Abstract: Lakes, as key freshwater reserves and ecosystem cores, supply human water, regulate climate, sustain biodiversity, and are vital for global ecological balance and human sustainability. Lake Chaohu, as a crucial ecological barrier in the middle and lower reaches of the Yangtze River, faces significant environmental challenges to regional sustainable development due to water quality deterioration and consequent eutrophication issues. To address the limitations of conventional monitoring techniques, including insufficient spatiotemporal coverage and high operational costs in lake water quality assessment, this study proposes an enhanced Informer model optimized by the Whale Optimization Algorithm (WOA) for predictive analysis of concentration trends of key water quality parameters—dissolved oxygen (DO), permanganate index (CODMn), total phosphorus (TP), and total nitrogen (TN)—across multiple time horizons (4 h, 12 h, 24 h, 48 h, and 72 h). The results demonstrate that the WOA-optimized Informer model (WOA-Informer) significantly improves long-term water quality prediction performance. Comparative evaluation shows that the WOA-Informer model achieves average reductions of 9.45%, 8.76%, 7.79%, 8.54%, and 11.80% in RMSE metrics for 4 h, 12 h, 24 h, 48 h, and 72 h prediction windows, respectively, along with average improvements of 3.80%, 5.99%, 11.23%, 17.37%, and 23.26% in R 2 values. The performance advantages become increasingly pronounced with extended prediction durations, conclusively validating the model’s superior capability in mitigating error accumulation effects and enhancing long-term prediction stability. Spatial visualization through Kriging interpolation confirms strong consistency between predicted and measured values for all parameters (DO, CODMn, TP, and TN) across all time horizons, both in concentration levels and spatial distribution patterns, thereby verifying the accuracy and reliability of the WOA-Informer model. This study successfully enhances water quality prediction precision through model optimization, providing robust technical support for water environment management and decision-making processes.

Keywords: informer model; whale optimization algorithm; water quality prediction; machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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