Improvement of streamflow simulation by combining physically hydrological model with deep learning methods in data-scarce glacial river basin

水流 计算机科学 冰期 数据挖掘 特征(语言学) 环境科学 人工智能 机器学习 流域 地质学 地貌学 地图学 地理 语言学 哲学
作者
Chengde Yang,Min Xu,Shichang Kang,Congsheng Fu,Didi Hu
出处
期刊:Journal of Hydrology [Elsevier]
卷期号:625: 129990-129990 被引量:6
标识
DOI:10.1016/j.jhydrol.2023.129990
摘要

Robust streamflow simulation at glacial basins is essential for the improvement of water sustainability assessment, water security evaluation, and water resource management under the rapidly changing climate. Therefore, we proposed a hybrid modelling framework to link the SWAT+ model considering glacial hydrological processes (GSWAT+) with Gated Recurrent Unit (GRU) neural networks to improve the model simulations and to establish a framework for the robust simulation and forecast of high and low flows in glacial river basins, which could be further used for the explorations of extreme hydrological events under a warming climate. The performance of different models (GSWAT+, GRU, and GRU-GSWAT+, respectively) were thoroughly investigated based on numerical experiments for two data-scarce glacial watersheds in Northwest China. The results suggested that the hybrid model (GRU-GSWAT+) outperformed both the individual deep learning (DL) model (GRU) and the conventional hydrological model (GSWAT+) in terms of simulation and prediction accuracy. Notably, the proposed hybrid model considerably enhanced the simulations of low and high flows that the conventional GSWAT+ failed to capture. Furthermore, utilizing suitable data integration (DI) schemes on feature and target sequences can substantially help to strengthen model stability and representativeness for monthly and annual streamflow sequences. Specifically, introducing one order differential method and decomposition approach, such as ensemble empirical signal decomposition (EEMD) and complete EEMD with adaptive noise (CEEMDAN), into feature and target sequences enriched the learnable ancillary information, which consequently strengthened the predictive performance of the proposed model. Overall, the proposed hybrid model with the suitable DI scheme has the potential to significantly enhance the accuracy of streamflow simulation in data-scarce glacial river basins. This hybrid model not only upheld the fundamental physical principles from the GSWAT+ model, but also considerably mitigated the accumulated bias errors, which caused by the shortage of climate data and inadequate hydrological principles, by using DL based model and DI schemes.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
大幅提高文件上传限制,最高150M (2024-4-1)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
哈哈发布了新的文献求助10
刚刚
胤子墨铭完成签到,获得积分20
刚刚
科研通AI2S应助Zxy采纳,获得10
刚刚
今后应助Jarvis采纳,获得10
2秒前
不去明知山完成签到 ,获得积分10
2秒前
8464368完成签到,获得积分10
3秒前
科研通AI2S应助Zxy采纳,获得10
3秒前
4秒前
炸炸洋芋完成签到,获得积分10
5秒前
addestay完成签到 ,获得积分10
5秒前
路内里完成签到,获得积分10
7秒前
xiaoran发布了新的文献求助10
9秒前
Sky完成签到,获得积分10
10秒前
10秒前
sail完成签到,获得积分10
10秒前
优雅紫槐应助123采纳,获得20
10秒前
11秒前
汉堡包完成签到 ,获得积分10
12秒前
Lynk369完成签到,获得积分10
12秒前
高昊完成签到,获得积分20
13秒前
抵澳报了完成签到,获得积分10
14秒前
15秒前
15秒前
ww发布了新的文献求助10
16秒前
zuoyou完成签到,获得积分10
17秒前
苏航发布了新的文献求助10
20秒前
咕噜完成签到,获得积分20
20秒前
哈哈完成签到,获得积分20
22秒前
Hello应助小马采纳,获得10
23秒前
英俊的念寒完成签到,获得积分10
23秒前
小二郎应助大方太清采纳,获得10
23秒前
善学以致用应助贾世冰采纳,获得10
24秒前
赵清完成签到,获得积分10
25秒前
我是老大应助sherrinford采纳,获得10
26秒前
不知道发布了新的文献求助10
27秒前
Hello应助文献文献采纳,获得10
28秒前
好的完成签到 ,获得积分10
29秒前
30秒前
南枝完成签到,获得积分10
30秒前
一抹浅色完成签到 ,获得积分10
31秒前
高分求助中
One Man Talking: Selected Essays of Shao Xunmei, 1929–1939 1000
A Chronicle of Small Beer: The Memoirs of Nan Green 1000
From Rural China to the Ivy League: Reminiscences of Transformations in Modern Chinese History 900
Eric Dunning and the Sociology of Sport 850
QMS18Ed2 | process management. 2nd ed 800
Operative Techniques in Pediatric Orthopaedic Surgery 510
The Making of Détente: Eastern Europe and Western Europe in the Cold War, 1965-75 500
热门求助领域 (近24小时)
化学 医学 材料科学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 基因 遗传学 物理化学 催化作用 免疫学 细胞生物学 电极
热门帖子
关注 科研通微信公众号,转发送积分 2915059
求助须知:如何正确求助?哪些是违规求助? 2553120
关于积分的说明 6907872
捐赠科研通 2214957
什么是DOI,文献DOI怎么找? 1177449
版权声明 588353
科研通“疑难数据库(出版商)”最低求助积分说明 576390