A Hybrid Water Balance Machine Learning Model to Estimate Inter-Annual Rainfall-Runoff

Sensors

Ismahen Kadri¹  Antonio Liotta²  Khodir Madani³  Amir Aieb³ 

[1] Department of Civil Engineering and Hydraulics, 8 May 1945 Guelma University, Guelma 24000, Algeria;Faculty of Computer Science, Free University of Bozen-Bolzano, 39100 Bolzano, Italy;Laboratory of Biomathematics, Biophysics, Biochemistry, and Scientometric (BBBS), Bejaia University, Bejaia 06000, Algeria;

关键词: rainfall runoff;    watershed;    climate floor;    modeling;    water balance models;    machine learning;   

DOI  :  10.3390/s22093241

来源: DOAJ

【 摘 要 】

Watershed climatic diversity poses a hard problem when it comes to finding suitable models to estimate inter-annual rainfall runoff (IARR). In this work, a hybrid model (dubbed MR-CART) is proposed, based on a combination of MR (multiple regression) and CART (classification and regression tree) machine-learning methods, applied to an IARR predicted data series obtained from a set of non-parametric and empirical water balance models in five climatic floors of northern Algeria between 1960 and 2020. A comparative analysis showed that the Yang, Sharif, and Zhang’s models were reliable for estimating input data of the hybrid model in all climatic classes. In addition, Schreiber’s model was more efficient in very humid, humid, and semi-humid areas. A set of performance and distribution statistical tests were applied to the estimated IARR data series to show the reliability and dynamicity of each model in all study areas. The results showed that our hybrid model provided the best performance and data distribution, where the R²_Adj and p-values obtained in each case were between (0.793, 0.989), and (0.773, 0.939), respectively. The MR model showed good data distribution compared to the CART method, where p-values obtained by signtest and WSR test were (0.773, 0.705), and (0.326, 0.335), respectively.

【 授权许可】

Unknown