Pedotransfer functions for estimating the van Genuchten model parameters in the Cerrado biome

ABSTRACT The Cerrado biome has presented challenges in reconciling its agricultural expansion with water availability. In this sense, water resources planning and management are fundamental for the economic, social, and environmental development of the Cerrado biome, which has been hampered by the lack of data, especially those referring to irrigation strategies, such as, for example, the water retention curve. The water retention curve is essential to understand water dynamics in the soil; however, obtaining it can be laborious, opening an opportunity for Pedotransfer Functions (PTFs). The current study aimed to develop and evaluate PTFs to estimate the fit parameters of the van Genuchten model for the Cerrado biome. Multiple Linear Regression (MLR) and four machine learning (ML) algorithms were used to develop the PTFs. The ML algorithms were the Multivariate Adaptive Regression Splines (MARS), Random Forest (RF), Support Vector Regression (SVR), and K Nearest Neighbors (KNN). Two combinations of soil data were evaluated, and the predictor variables used in each set were different. Using the RF and SVR models, the best estimates were obtained concerning the parameter θs (saturated water content). As for θr (residual water content), the models showed a moderate predictive capacity. For the other parameters, the models did not perform satisfactorily for α and n (fit parameters).

摘要 塞拉多生物群落（Cerrado biome）在协调农业扩张与水资源可用性方面面临诸多挑战。在此背景下，水资源规划与管理对塞拉多生物群落的经济、社会与环境发展至关重要，但该进程因数据匮乏而受阻，尤其是涉及灌溉策略的相关数据，例如持水曲线（water retention curve）。持水曲线是理解土壤水分运动的关键依据，但其获取过程耗时费力，这为土壤传递函数（Pedotransfer Functions，PTFs）的应用提供了可行空间。本研究旨在开发并评估用于估算塞拉多生物群落范·盖努赫滕模型（van Genuchten model）拟合参数的土壤传递函数。研究采用多元线性回归（Multiple Linear Regression，MLR）以及四种机器学习（machine learning，ML）算法开发土壤传递函数，这四种算法分别为多元自适应回归样条（Multivariate Adaptive Regression Splines，MARS）、随机森林（Random Forest，RF）、支持向量回归（Support Vector Regression，SVR）以及K近邻（K Nearest Neighbors，KNN）。本研究评估了两组土壤数据组合，每组所使用的预测变量存在差异。针对饱和含水量（saturated water content，θs）的估算，随机森林与支持向量回归模型取得了最优预测效果。就残余含水量（residual water content，θr）而言，各模型表现出中等水平的预测能力。对于其余拟合参数α与n，各模型的表现均未达到预期要求。