# Modeling Soil Water Dynamics and Hydrogel Doses Optimization Using a Machine Learning Approach: A Case Study on Sandy Clay Loam Soil under Varying Bulk Densities

## TL;DR

This paper uses machine learning to optimize hydrogel doses in soil to improve water retention, helping agriculture in drought-prone areas like Brazil.

## Contribution

A novel machine learning method is proposed to rapidly estimate optimal hydrogel doses without needing soil density data.

## Key findings

- Hydrogel at 3 g L–1 significantly increased soil water retention in sandy clay loam soil.
- A locally weighted regression model achieved high accuracy (0.875 correlation) in predicting hydrogel doses from short water content data.

## Abstract

Climate change has
intensified droughts in Brazil, threatening
agriculture through altered rainfall and temperature patterns. A promising
approach to mitigating the soil water deficit is the addition of biodegradable
hydrophilic polymers (hydrogels). However, water dynamics in soil
hydrogel systems remain complex and depend on soil type, bulk density,
and hydrogel dosage. The hydrophilic properties of the matrix may
persist over time, highlighting the importance of hydrogel residual
effects. The influence of bulk density on polymer dosage dynamics
remains underexplored, and no rapid analytical method currently exists
to estimate the dose equivalence of active hydrogels for agricultural
practices with accuracy and without excessive time consumption. This
study addresses two main goals: (1) to evaluate the effectiveness
of hydrogel dosages in sandy clay loam soil at varying densities for
enhanced water retention and (2) to develop a machine learning-based
analytical method for rapid estimation of active hydrogel doses from
short soil water content time series. Results showed a significant
increase in soil water retention at the 3 g L–1 dosage.
The proposed method, using a locally weighted regression model, achieved
a high correlation (0.875) and low error (0.749 g L–1) in cross-validation without requiring density information, offering
a practical tool for agricultural applications. These findings support
the efficient and sustainable use of hydrogels, providing a practical
framework that facilitates their management and enables rapid field-scale
interventions to improve water use in agriculture.