Work

Comparative Analysis of ANN, GEP, and Water Advance Power Function for Predicting Infiltrated Water Volume in Furrow of Permeable Surface

Year: 2025

Type: article

Abstract: The present investigation utilized artificial neural networks (ANN) and gene expression programming (GEP) in comparison with the two-point method (TPM) to develop a generalized solution for predicting infiltrated water volume (∀Z) across various soil types under furrow conditions. This work assesses infiltration behavior with respect to experimental data from several temporal contexts. Data distribution and model performance are evaluated via descriptive statistics and correlation tests. Artificial intelligence (AI) models (ANN and GEP) trained and evaluated utilizing input variables—inflow rate (Qin); furrow length (L); waterfront advance time at the end of the furrow (TL); infiltration opportunity time (To); and cross-sectional area of the inflow (Ao) are compared with TPM performance. More precisely and consistently than the water advance power function, AI-based algorithms hope to be invading water volume. Statistical analysis shows that ANN and GEP have lower error metrics, increased generalizability, and better representation of complex infiltration dynamics. The determination coefficient (R2) of ANN data produced 98.1% for testing and 97.8% for validation, while TPM showed accuracy reductions of 2.5% and 4.6%, respectively. On the other side, the R2 of GEP produced 95.7% for testing and 96.1% for validation, while TPM showed accuracy reductions of 0.7% and 3%, respectively. During ANN model computation, TPMs root mean square error (RMSE) of 0.0135 m3/m exceeded all mean values. Errors within 10% relative deviation were displayed using the ANN model ∀Z. Particularly, ANN and GEP, the study revealed that AI techniques predict furrow irrigation penetration of water volume better than the water advance power function. These models advance soil and furrow adaptation, extrapolation, and accuracy. Results show that AI-driven modeling may maximize hydrological assessments and irrigation control.

Source: Water

DOI: 10.3390/w17091304

Authors: A A Alazba, Mohamed A. Mattar, Ahmed El-Shafei, Farid Radwan, Mahmoud Ezzeldin, Nasser Alrdyan

Institution: King Saud University

Language: English

Topic: Hydrological Forecasting Using AI

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDGs: Clean water and sanitation, Zero hunger

Open Access status: gold

Estimated APC paid: $3,137