Suspended sediment load prediction using long short-term memory neural network.
Suspended sediment load prediction using long short-term memory neural network.
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DOI：
10.1038/s41598-021-87415-4
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发表时间：
2021-04-09
影响因子：
4.6
通讯作者：
Elshafie A
中科院分区：
文献类型：
--
作者：
AlDahoul N;Essam Y;Kumar P;Ahmed AN;Sherif M;Sefelnasr A;Elshafie A
河流在流动时携带着悬浮沉积物。这些沉积物根据河流的流量和路线沉积在不同的地方。然而，这些沉积物的沉积会影响环境健康、农业活动和饮用水源。悬浮泥沙的沉积减少了过流面积，从而影响水生生物的运动，最终导致河道的变化。因此，悬浮沉积物的数据及其变化对于各当局来说是至关重要的信息。各个主管部门都需要河流中悬浮沉积物的预报数据，以便各种水工建筑物的正常运行。通常，由于各种因素，包括场地相关数据、场地相关模型、缺乏用于预测的多个观测因素以及模式复杂性，悬浮泥沙浓度（SSC）的预测具有挑战性。因此，为了解决以前的问题，本研究提出了一种长短期记忆模型，仅利用包括流量数据在内的一个观测因素来预测马来西亚柔佛河的悬浮泥沙。数据收集时间为 1988 年至 1998 年。在本研究中，测试了四种不同的模型来预测悬浮沉积物，它们是：ElasticNet 线性回归 (L.R.)、多层感知器 (MLP) 神经网络、极限梯度提升和长短期记忆。基于四种不同的场景（例如每日、每周、10 天和每月）对预测进行了分析。性能评估表明，长短期记忆优于其他模型，每日、每周、10 天和每月场景的回归值分别为 92.01%、96.56%、96.71% 和 99.45%。
Rivers carry suspended sediments along with their flow. These sediments deposit at different places depending on the discharge and course of the river. However, the deposition of these sediments impacts environmental health, agricultural activities, and portable water sources. Deposition of suspended sediments reduces the flow area, thus affecting the movement of aquatic lives and ultimately leading to the change of river course. Thus, the data of suspended sediments and their variation is crucial information for various authorities. Various authorities require the forecasted data of suspended sediments in the river to operate various hydraulic structures properly. Usually, the prediction of suspended sediment concentration (SSC) is challenging due to various factors, including site-related data, site-related modelling, lack of multiple observed factors used for prediction, and pattern complexity.Therefore, to address previous problems, this study proposes a Long Short Term Memory model to predict suspended sediments in Malaysia's Johor River utilizing only one observed factor, including discharge data. The data was collected for the period of 1988–1998. Four different models were tested, in this study, for the prediction of suspended sediments, which are: ElasticNet Linear Regression (L.R.), Multi-Layer Perceptron (MLP) neural network, Extreme Gradient Boosting, and Long Short-Term Memory. Predictions were analysed based on four different scenarios such as daily, weekly, 10-daily, and monthly. Performance evaluation stated that Long Short-Term Memory outperformed other models with the regression values of 92.01%, 96.56%, 96.71%, and 99.45% daily, weekly, 10-days, and monthly scenarios, respectively.
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影响因子：
6
作者：
Najah, A.;El-Shafie, A.;El-Shafie, Amr H.
通讯作者：
El-Shafie, Amr H.
影响因子：
2.9
作者：
Gers, FA;Schmidhuber, J;Cummins, F
通讯作者：
Cummins, F
影响因子：
2.7
作者：
Ehteram, Mohammad;Ghotbi, Samira;EL-Shafie, Ahmed
通讯作者：
EL-Shafie, Ahmed
影响因子：
5.8
作者：
Jumin, Ellysia;Basaruddin, Faridah Bte;Ahmed, Ali Najah
通讯作者：
Ahmed, Ali Najah
影响因子：
6.2
作者：
Mohammadi, Babak;Guan, Yiqing;Safari, Mir Jafar Sadegh
通讯作者：
Safari, Mir Jafar Sadegh