气候变化领域集成服务门户(CCPortal): Utilizing Hybrid Machine Learning Techniques and Gridded Precipitation Data for Advanced Discharge Simulation in Under-Monitored River Basins

CCPortal

DOI	10.3390/hydrology11040048
	Utilizing Hybrid Machine Learning Techniques and Gridded Precipitation Data for Advanced Discharge Simulation in Under-Monitored River Basins
	Morovati, Reza; Kisi, Ozgur
发表日期	2024
EISSN	2306-5338
起始页码	11
结束页码	4
卷号	11 期号:4
英文摘要	This study addresses the challenge of utilizing incomplete long-term discharge data when using gridded precipitation datasets and data-driven modeling in Iran's Karkheh basin. The Multilayer Perceptron Neural Network (MLPNN), a rainfall-runoff (R-R) model, was applied, leveraging precipitation data from the Asian Precipitation-Highly Resolved Observational Data Integration Toward Evaluation (APHRODITE), Global Precipitation Climatology Center (GPCC), and Climatic Research Unit (CRU). The MLPNN was trained using the Levenberg-Marquardt algorithm and optimized with the Non-dominated Sorting Genetic Algorithm-II (NSGA-II). Input data were pre-processed through principal component analysis (PCA) and singular value decomposition (SVD). This study explored two scenarios: Scenario 1 (S1) used in situ data for calibration and gridded dataset data for testing, while Scenario 2 (S2) involved separate calibrations and tests for each dataset. The findings reveal that APHRODITE outperformed in S1, with all datasets showing improved results in S2. The best results were achieved with hybrid applications of the S2-PCA-NSGA-II for APHRODITE and S2-SVD-NSGA-II for GPCC and CRU. This study concludes that gridded precipitation datasets, when properly calibrated, significantly enhance runoff simulation accuracy, highlighting the importance of bias correction in rainfall-runoff modeling. It is important to emphasize that this modeling approach may not be suitable in situations where a catchment is undergoing significant changes, whether due to development interventions or the impacts of anthropogenic climate change. This limitation highlights the need for dynamic modeling approaches that can adapt to changing catchment conditions.
英文关键词	Levenberg-Marquardt algorithm; Multilayer Perceptron Neural Network; Non-dominated Sorting Genetic Algorithm-II; principal component analysis; rainfall-runoff modeling; singular value decomposition
语种	英语
WOS研究方向	Water Resources
WOS类目	Water Resources
WOS记录号	WOS:001210194300001
来源期刊	HYDROLOGY
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/300813
作者单位	Utah System of Higher Education; Utah State University; Ilia State University; Luebeck University of Applied Sciences
推荐引用方式 GB/T 7714	Morovati, Reza,Kisi, Ozgur. Utilizing Hybrid Machine Learning Techniques and Gridded Precipitation Data for Advanced Discharge Simulation in Under-Monitored River Basins[J],2024,11(4).
APA	Morovati, Reza,&Kisi, Ozgur.(2024).Utilizing Hybrid Machine Learning Techniques and Gridded Precipitation Data for Advanced Discharge Simulation in Under-Monitored River Basins.HYDROLOGY,11(4).
MLA	Morovati, Reza,et al."Utilizing Hybrid Machine Learning Techniques and Gridded Precipitation Data for Advanced Discharge Simulation in Under-Monitored River Basins".HYDROLOGY 11.4(2024).