干旱区生态环境知识资源中心(Arid): Areal Precipitation Coverage Ratio for Enhanced AI Modelling of Monthly Runoff: A New Satellite Data-Driven Scheme for Semi-Arid Mountainous Climate

Arid

DOI	10.3390/rs14020270
	Areal Precipitation Coverage Ratio for Enhanced AI Modelling of Monthly Runoff: A New Satellite Data-Driven Scheme for Semi-Arid Mountainous Climate
	Hosseini, Seyyed Hasan; Hashemi, Hossein; Fakheri Fard, Ahmad; Berndtsson, Ronny
通讯作者	Hosseini, SH (corresponding author)，Lund Univ, Fac Engn, Div Water Resources Engn, POB 118, S-22100 Lund, Sweden. ; Hosseini, SH (corresponding author)，Univ Tabriz, Fac Agr, Dept Water Engn, Tabriz 5166616471, Iran. ; Hosseini, SH (corresponding author)，Lund Univ, Ctr Adv Middle Eastern Studies, POB 201, S-22100 Lund, Sweden.
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2022
卷号	14 期号:2
英文摘要	Satellite remote sensing provides useful gridded data for the conceptual modelling of hydrological processes such as precipitation-runoff relationship. Structurally flexible and computationally advanced AI-assisted data-driven (DD) models foster these applications. However, without linking concepts between variables from many grids, the DD models can be too large to be calibrated efficiently. Therefore, effectively formulized, collective input variables and robust verification of the calibrated models are desired to leverage satellite data for the strategic DD modelling of catchment runoff. This study formulates new satellite-based input variables, namely, catchment- and event-specific areal precipitation coverage ratios (CCOVs and ECOVs, respectively) from the Global Precipitation Mission (GPM) and evaluates their usefulness for monthly runoff modelling from five mountainous Karkheh sub-catchments of 5000-43,000 km(2) size in west Iran. Accordingly, 12 different input combinations from GPM and MODIS products were introduced to a generalized deep learning scheme using artificial neural networks (ANNs). Using an adjusted five-fold cross-validation process, 420 different ANN configurations per fold choice and 10 different random initial parameterizations per configuration were tested. Runoff estimates from five hybrid models, each an average of six top-ranked ANNs based on six statistical criteria in calibration, indicated obvious improvements for all sub-catchments using the new variables. Particularly, ECOVs were most efficient for the most challenging sub-catchment, Kashkan, having the highest spacetime precipitation variability. However, better performance criteria were found for sub-catchments with lower precipitation variability. The modelling performance for Kashkan indicated a higher dependency on data partitioning, suggesting that long-term data representativity is important for modelling reliability.
英文关键词	artificial intelligence data scarcity GPM-IMERG k-fold data partitioning MODIS Terra NDVI rainfall semiarid soil moisture streamflow
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000757570600001
WOS关键词	NEURAL-NETWORK ; RAINFALL ; RETRIEVAL ; DISCHARGE
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/376667
作者单位	[Hosseini, Seyyed Hasan; Hashemi, Hossein; Berndtsson, Ronny] Lund Univ, Fac Engn, Div Water Resources Engn, POB 118, S-22100 Lund, Sweden; [Hosseini, Seyyed Hasan; Fakheri Fard, Ahmad] Univ Tabriz, Fac Agr, Dept Water Engn, Tabriz 5166616471, Iran; [Hosseini, Seyyed Hasan; Hashemi, Hossein; Berndtsson, Ronny] Lund Univ, Ctr Adv Middle Eastern Studies, POB 201, S-22100 Lund, Sweden
推荐引用方式 GB/T 7714	Hosseini, Seyyed Hasan,Hashemi, Hossein,Fakheri Fard, Ahmad,et al. Areal Precipitation Coverage Ratio for Enhanced AI Modelling of Monthly Runoff: A New Satellite Data-Driven Scheme for Semi-Arid Mountainous Climate[J],2022,14(2).
APA	Hosseini, Seyyed Hasan,Hashemi, Hossein,Fakheri Fard, Ahmad,&Berndtsson, Ronny.(2022).Areal Precipitation Coverage Ratio for Enhanced AI Modelling of Monthly Runoff: A New Satellite Data-Driven Scheme for Semi-Arid Mountainous Climate.REMOTE SENSING,14(2).
MLA	Hosseini, Seyyed Hasan,et al."Areal Precipitation Coverage Ratio for Enhanced AI Modelling of Monthly Runoff: A New Satellite Data-Driven Scheme for Semi-Arid Mountainous Climate".REMOTE SENSING 14.2(2022).