School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, QLD 4300, Australia; Department of Infrastructure Engineering, The University of Melbourne, Victoria 3010, Australia.
School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, QLD 4300, Australia.
Sci Total Environ. 2022 Jul 20;831:154722. doi: 10.1016/j.scitotenv.2022.154722. Epub 2022 Mar 24.
Forecasting river water levels or streamflow water levels (SWL) is vital to optimising the practical and sustainable use of available water resources. We propose a new deep learning hybrid model for SWL forecasting using convolutional neural networks (CNN), bi-directional long-short term memory (BiLSTM), and ant colony optimisation (ACO) with a two-phase decomposition approach at the 7-day, 14-day, and 28-day forecast horizons. The newly developed CBILSTM method is coupled with complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and variational mode decomposition (VMD) methods to extract the most significant features within predictor variables to build a hybrid CVMD-CBiLSTM model. We integrate three distinct datasets (satellite-derived, climate mode indices, and ground-based meteorological observations) to improve the forecasting capability of the CVMD-CBiLSTM model, applied at nineteen different gauging stations in the Australian Murray River system. This proposed model returns a significantly accurate performance with ~98% of all prediction errors within less than ±0.020 m and a low relative root mean square of ~0.08%, demonstrating its superiority over several benchmark models. The results show that using the new hybrid deep learning algorithm with ACO feature selection can significantly improve the accuracy of forecasted river water levels, and therefore, the method is attractive for adopting remote sensing data to the model ground-based river flow for strategic water savings planning initiatives and dealing with climate change-induced extreme events such as drought events.
预测河流水位或河道流量水位（SWL）对于优化可用水资源的实际和可持续利用至关重要。我们提出了一种新的深度学习混合模型，用于使用卷积神经网络（CNN）、双向长短期记忆（BiLSTM）和蚁群优化（ACO）进行 SWL 预测，采用两阶段分解方法，预测期限为 7 天、14 天和 28 天。新开发的 CBILSTM 方法与完全集成经验模态分解与自适应噪声（CEEMDAN）和变分模态分解（VMD）方法相结合，提取预测变量内最重要的特征，构建混合 CVMD-CBiLSTM 模型。我们整合了三个不同的数据集（卫星衍生、气候模式指数和地面气象观测），以提高 CVMD-CBiLSTM 模型的预测能力，该模型应用于澳大利亚墨累河系统的 19 个不同的测量站。该提出的模型具有非常准确的性能，所有预测误差中约有 98%都在±0.020 m 以内，相对均方根误差较低，约为 0.08%，表明其优于几个基准模型。结果表明，使用带有 ACO 特征选择的新混合深度学习算法可以显著提高预测河流水位的准确性，因此，该方法对于采用遥感数据到模型地面河流流量以进行战略节水规划举措以及应对气候变化引起的干旱等极端事件具有吸引力。
