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A Protocol for Conducting Rainfall Simulation to Study Soil Runoff

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A Novel Interannual Rainfall Runoff Equation Derived from Ol'Dekop's Model Using Artificial Neural Networks.

Omar Mimeche¹, Amir Aieb², Antonio Liotta³

¹Research Laboratory in Applied Hydraulics and Environment (LRHAE), Department of Hydraulics, Faculty of Technology, University of Bejaia, Targa Ouzemour, Bejaia 06000, Algeria.

Sensors (Basel, Switzerland)

|June 24, 2022

Summary

A new model estimates rainfall-runoff in Algeria using artificial neuron networks (ANNs). This dynamic approach improves water resources management by accurately predicting water balance factors across diverse climatic regions.

Keywords:

ANN model De Martonne index inter-annual time scale northern Algeria rainfall-runoff modeling water balance model watercourse watershed

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Area of Science:

Hydrology and Water Resources Management
Artificial Intelligence in Environmental Science

Background:

Accurate water balance modeling is crucial for water resources management, including agriculture, irrigation, and water supply.
Traditional conceptual and physical models face challenges in parameter estimation for runoff assessment across varied climatic zones.

Purpose of the Study:

To develop a dynamic and reliable model for estimating inter-annual rainfall-runoff in northern Algeria.
To improve upon existing water balance equations using artificial neuron networks (ANNs).

Main Methods:

An enhanced Ol'Dekop's equation was developed, incorporating two artificial neuron network (ANN) sub-models (ANN1 and ANN2).
Input variables included inter-annual rainfall (IAR), watershed area (S), and watercourse (WC) from 16 basins.
ANN2 processed ANN1 outputs and the De Martonne index (I) for enhanced reliability and climate independence.

Main Results:

The proposed model demonstrated superior performance compared to traditional parametric and non-parametric water balance models.
Adjusted R-squared (R²Adj) values ranged from 0.9103 to 0.9923, indicating high accuracy in rainfall-runoff estimation.
The model proved to be dynamic and less affected by specific climatic characteristics of the study area.

Conclusions:

The novel ANN-based model offers a significant advancement in rainfall-runoff estimation for water resources management.
The model's high accuracy and dynamic nature make it suitable for diverse climatic regions, particularly in northern Algeria.
This approach provides a reliable tool for optimizing water control, irrigation, and supply strategies.