Paper Info
Title
A Novel Interannual Rainfall Runoff Equation Derived from Ol'Dekop's Model Using Artificial Neural Networks
Abstract
In water resources management, modeling water balance factors is necessary to control dams, agriculture, irrigation, and also to provide water supply for drinking and industries. Generally, conceptual and physical models present challenges to find more hydro-climatic parameters, which show good performance in the assessment of runoff in different climatic regions. Accordingly, a dynamic and reliable model is proposed to estimate inter-annual rainfall-runoff in five climatic regions of northern Algeria. This is a new improvement of Ol'Dekop's equation, which models the residual values obtained between real and predicted data using artificial neuron networks (ANN(s)), namely by ANN(1) and ANN(2) sub-models. In this work, a set of climatic and geographical variables, obtained from 16 basins, which are inter-annual rainfall (IAR), watershed area (S), and watercourse (WC), were used as input data in the first model. Further, the ANN(1) output results and De Martonne index (I) were classified, and were then processed by ANN(2) to further increase reliability, and make the model more dynamic and unaffected by the climatic characteristic of the area. The final model proved the best performance in the entire region compared to a set of parametric and non-parametric water balance models used in this study, where the R-Adj(2) obtained from each test gave values between 0.9103 and 0.9923.
Year
DOI
Venue
2022
10.3390/s22124349
SENSORS
Keywords
DocType
Volume
rainfall-runoff modeling, water balance model, ANN model, watercourse, De Martonne index, inter-annual time scale, northern Algeria, watershed
Journal
22
Issue
ISSN
Citations 
12
1424-8220
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Omar Mimeche100.34
Amir Aieb200.68
Antonio Liotta392.45
Khodir Madani400.68