Advances in establishing real-time river water quality monitoring networks combined with novel artificial intelligence techniques for more accurate forecasting is at the forefront of urban water management. The preservation and improvement of the quality of our impaired urban streams are at the core of the global challenge of ensuring water sustainability. This work adopted a genetic-algorithm (GA)-optimized long short-term memory (LSTM) technique to predict river water temperature (WT) as a key indicator of the health state of the aquatic habitat, where its modeling is crucial for effective urban water quality management. To our knowledge, this is the first attempt to adopt a GA-LSTM to predict the WT in urban rivers. In recent research trends, large volumes of real-time water quality data, including water temperature, conductivity, pH, and turbidity, are constantly being collected. Specifically, in the field of water quality management, this provides countless opportunities for understanding water quality impairment and forecasting, and to develop models for aquatic habitat assessment purposes. The main objective of this research was to develop a reliable and simple urban river water temperature forecasting tool using advanced machine learning methods that can be used in conjunction with a real-time network of water quality monitoring stations for proactive water quality management. We proposed a hybrid time series regression model for WT forecasting. This hybrid approach was applied to solve problems regarding the time window size and architectural factors (number of units) of the LSTM network. We have chosen an hourly water temperature record collected over 5 years as the input. Furthermore, to check its robustness, a recurrent neural network (RNN) was also tested as a benchmark model and the performances were compared. The experimental results revealed that the hybrid model of the GA-LSTM network outperformed the RNN and the basic problem of determining the optimal time window and number of units of the memory cell was solved. This research concluded that the GA-LSTM can be used as an advanced deep learning technique for time series analysis.

中文翻译：

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水温预测的遗传算法优化序列模型

建立实时河流水质监测网络与新型人工智能技术相结合以实现更准确预测的进展处于城市水管理的前沿。保护和改善受损城市河流的质量是确保水资源可持续性这一全球挑战的核心。这项工作采用了遗传算法 (GA) 优化的长短期记忆 (LSTM) 技术来预测河流水温 (WT) 作为水生栖息地健康状况的关键指标，其建模对于有效的城市环境至关重要。水质管理。据我们所知，这是首次尝试采用 GA-LSTM 来预测城市河流中的 WT。在最近的研究趋势中，大量实时水质数据，包括水温、电导率、pH、和浊度，不断被收集。具体而言，在水质管理领域，这为了解水质损害和预测以及开发用于水生栖息地评估目的的模型提供了无数机会。这项研究的主要目标是使用先进的机器学习方法开发一种可靠且简单的城市河流水温预测工具，该工具可与水质监测站的实时网络结合使用，以进行主动水质管理。我们提出了一种用于 WT 预测的混合时间序列回归模型。这种混合方法用于解决有关 LSTM 网络的时间窗口大小和架构因素（单元数）的问题。我们选择了超过 5 年收集的每小时水温记录作为输入。此外，为了检查其鲁棒性，还测试了循环神经网络 (RNN) 作为基准模型并比较了性能。实验结果表明，GA-LSTM网络的混合模型优于RNN，解决了确定最佳时间窗口和记忆单元单元数的基本问题。该研究得出的结论是，GA-LSTM 可以用作时间序列分析的高级深度学习技术。实验结果表明，GA-LSTM网络的混合模型优于RNN，解决了确定最佳时间窗口和记忆单元单元数的基本问题。该研究得出的结论是，GA-LSTM 可以用作时间序列分析的高级深度学习技术。实验结果表明，GA-LSTM网络的混合模型优于RNN，解决了确定最佳时间窗口和记忆单元单元数的基本问题。该研究得出的结论是，GA-LSTM 可以用作时间序列分析的高级深度学习技术。