Department of Water Resources Engineering, Faculty of Civil, Water and Environmental Engineering, Amirkabir University of Technology, Tehran, Iran.
Department of Civil and Architectural Engineering, Sultan Qaboos University, Muscat, Oman.
Environ Sci Pollut Res Int. 2023 Dec;30(59):124316-124340. doi: 10.1007/s11356-023-30774-4. Epub 2023 Nov 24.
Water quality variables, including chlorophyll-a (Chl-a), play a pivotal role in comprehending and evaluating the condition of aquatic ecosystems. Chl-a, a pigment present in diverse aquatic organisms, notably algae and cyanobacteria, serves as a valuable indicator of water quality. Thus, the objectives of this study encompass: (1) the assessment of the predictive capabilities of four deep learning (DL) models - namely, recurrent neural network (RNN), long short-term memory (LSTM), gated recurrence unit (GRU), and temporal convolutional network (TCN) - in forecasting Chl-a concentrations; (2) the incorporation of these DL models into ensemble models (EMs) employing genetic algorithm (GA) and non-dominated sorting genetic algorithm (NSGA-II) to harness the strengths of each standalone model; and (3) the evaluation of the efficacy of the developed EMs. Utilizing data collected at 15-min intervals from Small Prespa Lake (SPL) in Greece, the models employed hourly Chl-a concentration lag times, extending up to 6 h, as models' inputs to forecast Chla (t+1). The proposed models underwent training on 70% of the dataset and were subsequently validated on the remaining 30%. Among the standalone DL models, the GRU model exhibited superior performance in Chl-a forecasting, surpassing the RNN, LSTM, and TCN models by 8%, 2%, and 2%, respectively. Furthermore, the integration of DL models through single-objective GA and multi-objective NSGA-II optimization algorithms yielded hybrid models adept at effectively forecasting both low and high Chl-a concentrations. The ensemble model based on NSGA-II outperformed standalone DL models as well as the GA-based model across a range of evaluation indices. For instance, considering the R-squared metric, the study's findings demonstrated that the EM-NSGA-II stands out with exceptional effectiveness compared to DL and EM-GA models, showcasing improvements of 14% (RNN), 8% (LSTM), 6% (GRU), 8% (TCN), and 3% (EM-GA) during the testing phase.
水质变量，包括叶绿素-a（Chl-a），在理解和评估水生生态系统状况方面起着关键作用。Chl-a 是存在于各种水生生物中的一种色素，尤其是藻类和蓝藻，是水质的重要指标。因此，本研究的目标包括：（1）评估四种深度学习（DL）模型——递归神经网络（RNN）、长短期记忆（LSTM）、门控循环单元（GRU）和时间卷积网络（TCN）——预测 Chl-a 浓度的能力；（2）将这些 DL 模型纳入使用遗传算法（GA）和非支配排序遗传算法（NSGA-II）的集成模型（EM）中，以利用每个独立模型的优势；（3）评估所开发的 EM 的效果。利用从希腊小普雷斯帕湖（SPL）每 15 分钟收集一次的数据，模型将每小时 Chl-a 浓度滞后时间（最长可达 6 小时）作为模型的输入，用于预测 Chla（t+1）。所提出的模型在 70%的数据上进行训练，然后在其余 30%的数据上进行验证。在独立的 DL 模型中，GRU 模型在 Chl-a 预测方面表现出色，比 RNN、LSTM 和 TCN 模型分别高出 8%、2%和 2%。此外，通过单目标 GA 和多目标 NSGA-II 优化算法集成 DL 模型，得到了能够有效预测低和高 Chl-a 浓度的混合模型。基于 NSGA-II 的集成模型在一系列评估指标上优于独立的 DL 模型和基于 GA 的模型。例如，考虑到 R-squared 指标，该研究的结果表明，与 DL 和 EM-GA 模型相比，EM-NSGA-II 表现出了卓越的效果，在测试阶段的改进分别为 14%（RNN）、8%（LSTM）、6%（GRU）、8%（TCN）和 3%（EM-GA）。
