Understanding the spatiotemporal behaviour of soil moisture in tropical forests is fundamental because it mediates processes such as infiltration, groundwater recharge, runoff and evapotranspiration. This study aims to model the spatiotemporal dynamics of soil moisture in an Atlantic forest remnant (AFR) through four machine learning algorithms, as these dynamics represent an important knowledge gap under tropical conditions. Random forest (RF), support vector machine, average neural network and weighted k-nearest neighbour were studied. The abilities of the models were evaluated by means of root mean square error, mean absolute error, coefficient of determination (R²) and Nash-Sutcliffe efficiency (NS) for two calibration approaches: (a) chronological and (b) randomized. The models were further compared with a multilinear regression (MLR). The study period spans from September 2012 to November 2019 and relies on variables representing the weather, geographical location, forest structure, soil physics and morphology. RF was the best algorithm for modelling the spatiotemporal dynamics of the soil moisture with an NS of 0.77 and R² of 0.51 in the randomized approach. This finding highlights the ability of RF to generalize a dataset with contrasting weather conditions. Kriging maps highlighted the suitability of RF to track the spatial distribution of soil moisture in the AFR. Throughfall (TF), potential evapotranspiration (ETo), longitude (Long), diameter at breast height (DBH) and species diversity (H) were the most important variables controlling soil moisture. MLR performed poorly in modelling the spatiotemporal dynamics of soil moisture due to the highly nonlinear condition of this process.

中文翻译：

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通过机器学习算法对大西洋森林土壤湿度进行时空建模

了解热带森林中土壤水分的时空行为非常重要，因为它可以调节入渗、地下水补给、径流和蒸发蒸腾等过程。本研究旨在通过四种机器学习算法模拟大西洋残余森林 (AFR) 中土壤水分的时空动态，因为这些动态代表了热带条件下的重要知识差距。研究了随机森林(RF)、支持向量机、平均神经网络和加权k-最近邻。通过均方根误差、平均绝对误差、决定系数（R ²) 和 Nash-Sutcliffe 效率 (NS) 用于两种校准方法：（a）时间顺序和（b）随机。这些模型进一步与多线性回归 (MLR) 进行了比较。研究期间从 2012 年 9 月到 2019 年 11 月，依赖于代表天气、地理位置、森林结构、土壤物理和形态的变量。RF 是模拟土壤水分时空动态的最佳算法，NS 为 0.77，R ²在随机方法中为 0.51。这一发现突出了 RF 概括具有对比天气条件的数据集的能力。克里金图突出了 RF 跟踪 AFR 中土壤水分空间分布的适用性。穿透量 (TF)、潜在蒸散量 (ETo)、经度 (Long)、胸高直径 (DBH) 和物种多样性 (H) 是控制土壤水分的最重要变量。由于该过程的高度非线性条件，MLR 在模拟土壤水分的时空动态方面表现不佳。