dataset · Zenodo (CERN European Organization for Nuclear Research)
This repository provides numerical simulation data and Python code for modelling and optimising a self-regulating, low-energy, clay-based subsurface irrigation system. The pipeline evaluates non-linear soil-water dynamics using machine learning surrogate models, including Extra Trees, CatBoost, and LightGBM. It uses physics-based numerical simulation data across varying operating pressure heads, emitter installation depths, and maximum root depths. Performance indicators tracked include cumulative discharge, wetting front distance, volumetric soil moisture, water application efficiency, distribution uniformity, and effective infiltrated volume ratios. The codebase features multi-objective optimisation scripts that use the NSGA-II algorithm to map trade-offs and locate compromise operating points across different root depths, alongside statistical routines for post-hoc analysis.
Efficient irrigation is critical for sustainable food production and water conservation. By supplying open-source computational tools and simulation data for low-energy clay emitters, this pipeline allows specialists to explore optimal subsurface watering conditions across various crop root depths without the immediate expense and delay of exhaustive physical field trials.
This codebase represents early-stage analytical software. It could enable irrigation engineers, agritech developers, and equipment manufacturers to design and calibrate low-energy clay irrigation systems for specific crop types and soil conditions. Because the resource consists solely of simulation data and predictive algorithms, commercial application would first require extensive real-world field validation and packaging into commercial agronomic design software.
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.
This page summarises published work. The authoritative version sits with the publisher.
DOI: 10.5281/zenodo.21939018
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