dataset · Zenodo (CERN European Organization for Nuclear Research)
The resource provides a numerical simulation dataset and Python codebase designed to model and optimise self-regulating, low-energy, clay-based irrigation systems. Using physics-based simulation data covering varying operating pressure heads, emitter installation depths, and maximum root depths, the pipeline models subsurface soil-water dynamics. It incorporates machine learning surrogate models, specifically Extra Trees, CatBoost, and LightGBM, to predict hydraulic and performance variables such as cumulative emitter discharge, wetting front distance, and volumetric moisture content. Furthermore, the repository implements an NSGA-II multi-objective optimisation algorithm to maximise water application efficiency, distribution uniformity, and the ratio of effective infiltrated volume. This process identifies three-dimensional Pareto frontiers and optimal compromise points across different root depths, supported by statistical analysis scripts to evaluate the resulting parameter sets.
Clay-based subsurface irrigation offers low-energy watering for crops, but configuring operating pressures and emitter depths across different root profiles is complex. Combining numerical simulation with machine learning surrogate models allows for rapid prediction of soil moisture behaviour. This approach helps balance water application efficiency and uniformity across varied crop conditions without relying solely on slow, computationally intensive physical simulations.
This dataset and codebase represent an early-stage computational tool intended for irrigation researchers and agricultural engineers designing low-energy, clay-based irrigation systems. The tool could inform the design and operational settings of subsurface emitter installations tailored to specific crop rooting depths. Because the release consists strictly of numerical simulation data and surrogate modelling scripts, it remains at an exploratory research stage rather than near commercial deployment.
AI-generated from the published abstract. Always read the original work before citing.
This repository contains the numerical simulation dataset and Python source code associated with the study on Self-regulating, Low Energy, Clay-based Irrigation (SLECI) system performance, surrogate modeling, and multi-objective optimization. This dataset and codebase provide a reproducible pipeline for modeling complex, non-linear subsurface soil-water dynamics using machine learning (ML) surrogate models (Extra Trees, CatBoost, and LightGBM) and conducting multi-objective Pareto optimization across varying maximum root depths (Zr, max) Repository Contents 1. Simulation Dataset (/data) Physics-based Simulation Data: Numerical soil-water distribution data generated across various operating pressure heads (He), emitter installation depths (De), and maximum root depths (Zr, max). Target Hydraulic & Performance Variables: Includes cumulative emitter discharge (VQe), horizontal wetting front distance (dx), volumetric soil moisture content (θv), water application efficiency (εa), soil water distribution uniformity (CUθv), and the ratio of effective infiltrated volume (rv). 2. Python Scripts (/code) Scripts to train and evaluate ML surrogate models (Extra Trees, CatBoost, LightGBM) for hydraulic variables and performance indicators. Code for executing simultaneous maximising (εa, CUθv, rv) via an NGSA-II multi-objective optimisation to generate 3D Pareto frontiers and identify ideal compromise (knee) points across root depths. Code for performing omnibus ANOVA and post-hoc Tukey HSD pairwise comparisons on Pareto-optimal parameter sets. System Requirements & Dependencies The scripts are written in Python 3.13.9. The required libraries include: numpy pandas scikit-learn catboost lightgbm scipy matplotlib / seaborn How to Use Clone or extract the repository contents. Place the simulation dataset inside the designated /data directory. Run the model training script to reproduce surrogate model results, or execute the optimization script to evaluate Pareto trade-offs across root depths.
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DOI: 10.5281/zenodo.21939017
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