Water quality prediction based on sparse dataset using enhanced machine learning

IF 14 1区 环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES Environmental Science and Ecotechnology Pub Date : 2024-03-01 DOI:10.1016/j.ese.2024.100402
Sheng Huang , Jun Xia , Yueling Wang , Jiarui Lei , Gangsheng Wang
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Abstract

Water quality in surface bodies remains a pressing issue worldwide. While some regions have rich water quality data, less attention is given to areas that lack sufficient data. Therefore, it is crucial to explore novel ways of managing source-oriented surface water pollution in scenarios with infrequent data collection such as weekly or monthly. Here we showed sparse-dataset-based prediction of water pollution using machine learning. We investigated the efficacy of a traditional Recurrent Neural Network alongside three Long Short-Term Memory (LSTM) models, integrated with the Load Estimator (LOADEST). The research was conducted at a river-lake confluence, an area with intricate hydrological patterns. We found that the Self-Attentive LSTM (SA-LSTM) model outperformed the other three machine learning models in predicting water quality, achieving Nash-Sutcliffe Efficiency (NSE) scores of 0.71 for CODMn and 0.57 for NH3N when utilizing LOADEST-augmented water quality data (referred to as the SA-LSTM-LOADEST model). The SA-LSTM-LOADEST model improved upon the standalone SA-LSTM model by reducing the Root Mean Square Error (RMSE) by 24.6% for CODMn and 21.3% for NH3N. Furthermore, the model maintained its predictive accuracy when data collection intervals were extended from weekly to monthly. Additionally, the SA-LSTM-LOADEST model demonstrated the capability to forecast pollution loads up to ten days in advance. This study shows promise for improving water quality modeling in regions with limited monitoring capabilities.

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利用增强型机器学习进行基于稀疏数据集的水质预测
地表水体的水质仍然是全球面临的一个紧迫问题。虽然一些地区拥有丰富的水质数据,但对缺乏足够数据的地区关注较少。因此,在数据收集不频繁(如每周或每月一次)的情况下,探索以源头为导向的地表水污染管理新方法至关重要。在此,我们利用机器学习展示了基于稀疏数据集的水污染预测。我们研究了传统循环神经网络与三个长短期记忆(LSTM)模型的功效,并将其与负荷估算器(LOADEST)进行了整合。研究是在河流与湖泊交汇处进行的,该地区的水文格局错综复杂。我们发现,自注意 LSTM(SA-LSTM)模型在预测水质方面的表现优于其他三种机器学习模型,在利用 LOADEST 增强的水质数据(简称为 SA-LSTM-LOADEST 模型)时,CODMn 的纳什-苏特克利夫效率(NSE)得分达到 0.71,NH3N 的纳什-苏特克利夫效率(NSE)得分达到 0.57。与独立的 SA-LSTM 模型相比,SA-LSTM-LOADEST 模型将 CODMn 和 NH3N 的均方根误差(RMSE)分别降低了 24.6% 和 21.3%。此外,当数据收集间隔从每周延长到每月时,该模型仍能保持其预测准确性。此外,SA-LSTM-LOADEST 模型还展示了提前十天预测污染负荷的能力。这项研究为改善监测能力有限地区的水质建模工作带来了希望。
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来源期刊
CiteScore
20.40
自引率
6.30%
发文量
11
审稿时长
18 days
期刊介绍: Environmental Science & Ecotechnology (ESE) is an international, open-access journal publishing original research in environmental science, engineering, ecotechnology, and related fields. Authors publishing in ESE can immediately, permanently, and freely share their work. They have license options and retain copyright. Published by Elsevier, ESE is co-organized by the Chinese Society for Environmental Sciences, Harbin Institute of Technology, and the Chinese Research Academy of Environmental Sciences, under the supervision of the China Association for Science and Technology.
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