Daily river flow simulation using ensemble disjoint aggregating M5-Prime model
Khabat Khosravi,
Nasrin Attar,
Sayed M. Bateni,
Changhyun Jun,
Dongkyun Kim,
Mir Jafar Sadegh Safari,
Salim Heddam,
Aitazaz Farooque,
Soroush Abolfathi
Affiliations
Khabat Khosravi
Canadian Centre for Climate Change and Adaptation, University of Prince Edward Island, Charlottetown, Canada; Corresponding author.
Nasrin Attar
Department of Statistical Sciences, University of Padova, Padova, Italy
Sayed M. Bateni
Department of Civil, Environmental and Construction Engineering and Water Resources Research Center, University of Hawaii at Manoa, Honolulu, HI, USA; UNESCO-UNISA Africa Chair in Nanoscience and Nanotechnology College of Graduates Studies, University of South Africa, Muckleneuk Ridge, Pretoria, 392, South Africa
Changhyun Jun
School of Civil, Environmental and Architectural Engineering, College of Engineering, Korea University, Seoul, Republic of Korea; Corresponding author.
Dongkyun Kim
Department of Civil and Environmental Engineering, Hongik University, Seoul, Republic of Korea
Mir Jafar Sadegh Safari
Department of Geography and Environmental Studies, Toronto Metropolitan University, Toronto, Ontario, Canada; Department of Civil Engineering, Yaşar University, Izmir, Turkey
Salim Heddam
Faculty of Science, Agronomy Department, Hydraulics Division, Laboratory of Research in Biodiversity Interaction Ecosystem and Biotechnology, University 20 Août 1955, Route El Hadaik, BP 26, Skikda, Algeria
Aitazaz Farooque
Canadian Centre for Climate Change and Adaptation, University of Prince Edward Island, Charlottetown, Canada; Faculty of Sustainable Design Engineering, University of Prince Edward Island, Charlottetown, PE, Canada
Soroush Abolfathi
School of Engineering, University of Warwick, CV4 7AL, Coventry, UK
Accurate prediction of daily river flow (Qt) remains a challenging yet essential task in hydrological modeling, particularly crucial for flood mitigation and water resource management. This study introduces an advanced M5 Prime (M5P) predictive model designed to estimate Qt as well as one- and two-day-ahead river flow forecasts (i.e. Qt+1 and Qt+2). The predictive performance of M5P ensembles incorporating Bootstrap Aggregation (BA), Disjoint Aggregating (DA), Additive Regression (AR), Vote (V), Iterative classifier optimizer (ICO), Random Subspace (RS), and Rotation Forest (ROF) were comprehensively evaluated. The proposed models were applied to a case study data in Tuolumne County, US, using a dataset comprising measured precipitation (Pt), evaporation (Et), and Qt. A wide range of input scenarios were explored for predicting Qt, Qt+1, and Qt+2. Results indicate that Pt and Qt significantly influence prediction accuracy. Notably, relying solely on the most correlated variable (e.g., Qt-1) does not guarantee robust prediction of Qt. However, extending the forecast horizon mitigates the influence of low-correlation input variables on model accuracy. Performance metrics indicate that the DA-M5P model achieves superior results, with Nash-Sutcliff Efficiency of 0.916 and root mean square error of 23 m3/s, followed by ROF-M5P, BA-M5P, AR-M5P, AR-M5P, RS-M5P, V-M5P, ICO-M5P, and the standalone M5P model. The ensemble M5P modeling framework enhanced the predictive capability of the stand-alone M5P algorithm by 1.2 %–22.6 %, underscoring its efficacy and potential for advancing hydrological forecasting.
