Department of Information Technology, National Engineering College, Kovilpatti, 628503, Tamilnadu, India.
Department of Computer Science and Engineering, R.M.K. College of Engineering and Technology, Puduvoyal, Thiruvallur, 601206, Tamilnadu, India.
Environ Sci Pollut Res Int. 2024 Sep;31(42):54525-54533. doi: 10.1007/s11356-024-34750-4. Epub 2024 Aug 28.
Water resource management relies heavily on reliable water quality predictions. Predicting water quality metrics in the watershed system, including dissolved oxygen (DO), is the main emphasis of this work. The enhanced long short-term memory (LSTM) model was suggested to improve the model's performance. Additionally, a hybrid model was employed to calculate the ideal parameter values for the LSTM model, which helped overcome the nonstationarity, unpredictability, and nonlinearity of the data about the water quality parameters. This model recruited the COOT method. The original weekly water quality values at the Vaigai River, Madurai, Tamil Nadu, India, were tested using the suggested hybrid model. An independent LSTM, the hybrid optimisation method takes its cues from the cuckoo bird's reproductive strategy and a novel meta-heuristic optimisation technique dubbed COOT, which is based on the behaviour of a flock of coot birds. If implemented, the suggested hybrid model might serve as an alternate framework for water quality prediction, laying the groundwork for basin-wide efforts to manage water quality and control pollutants.
水资源管理严重依赖于可靠的水质预测。本工作主要关注预测流域系统中的水质指标，包括溶解氧 (DO)。提出了增强型长短时记忆 (LSTM) 模型以提高模型性能。此外，还采用了混合模型来计算 LSTM 模型的理想参数值，这有助于克服水质参数数据的非平稳性、不可预测性和非线性。该模型采用了 COOT 方法。印度泰米尔纳德邦马杜赖 Vaigai 河的原始每周水质值使用建议的混合模型进行了测试。独立的 LSTM，混合优化方法借鉴了布谷鸟的繁殖策略以及一种名为 COOT 的新颖元启发式优化技术，该技术基于黑鸭鸟群的行为。如果实施，建议的混合模型可以作为水质预测的替代框架，为流域范围内的水质管理和污染物控制工作奠定基础。
