An AI-Driven Precision Irrigation Framework for Wheat Decadal Analysis in the Nile Delta

  This repository contains the computational resources supporting the study “AI-Driven Precision Irrigation Framework for Wheat Water Management in the Nile Delta, Egypt.” The repository provides the code and associated computational materials used for meteorological data preprocessing, evapotranspiration estimation, feature engineering, machine-learning and deep-learning model development, temporal validation, and analysis of wheat water requirements for the Nile Delta region. The study uses daily meteorological data from the NASA Prediction of Worldwide Energy Resources (NASA POWER) archive for the Sakha Agricultural Research Station in the North Nile Delta, Egypt, covering the 2014–2024 study period. The computational workflow includes the evaluation of Random Forest and Hybrid CNN-LSTM approaches, together with the locally calibrated Hargreaves–Samani formulation and FAO-56 Penman–Monteith as a model-based reference formulation. The repository is intended to support computational transparency and reproducibility of the analyses reported in the associated manuscript. The archived materials allow users to examine the data-processing workflow, model development procedures, validation strategy, and generation of the reported analytical outputs. Because direct field measurements of evapotranspiration, such as lysimeter or eddy-covariance observations, were not available for the complete study period, the FAO-56 Penman–Monteith estimates should be interpreted as a physically based reference baseline rather than measured ground truth. Accordingly, model-performance metrics reported in the associated study represent agreement with the selected reference formulation and should not be interpreted as independent observational validation of actual evapotranspiration. The repository is provided to facilitate scientific transparency, methodological inspection, and future development of field-validated AI-based irrigation decision-support systems for climate-resilient wheat production.