Machine Learning-Based Prediction of Drainage in Layered Soils Using a Soil Drainability Index

Autor(es)

Kotlar, Ali Mehmandoost ^[1] ; Iversen, V, Bo ; van Lier, Quirijn de Jong ^[1]

Afiliação do(s) autor(es)

^[1] Univ Sao Paulo, Ctr Nucl Energy Agr CENA, Trop Ecosyst Div, Caixa Postal 96, BR-13416903 Piracicaba, SP - Brazil

• Tipo de documento: Artigo Científico
• Fonte: SOIL SYSTEMS; v. 3, n. 2 JUN 2019.
• Citações Web of Science: 1

Resumo

Numerical modelling of water flow allows for the prediction of rainwater partitioning into evaporation, deep drainage, and transpiration for different seasonal crop and soil type scenarios. We proposed and tested a single indicator for drainage estimation, the soil drainability index (SDI) based on the near saturated hydraulic conductivity of each layer. We studied rainfall partitioning for eight soils from Brazil and seven different real and generated weather data under scenarios without crop and with a permanent grass cover with three rooting depths, using the HYDRUS-1D model. The SDI showed a good correlation to simulated drainage of the soils. Moreover, well-trained supervised machine-learning methods, including the linear and stepwise linear models (LM, SWLM), besides ensemble regression with boosting and bagging algorithm (ENS-LB, ENS-B), support vector machines (SVMs), and Gaussian process regression (GPR), predicted monthly drainage from bare soil (BS) and grass covered lands (G) using soil-plant-atmosphere parameters (i.e., SDI, monthly precipitation, and evapotranspiration or transpiration). The RMSE values for testing data in BS and G were low, around 1.2 and 1.5 cm month(-1) for all methods. (AU)

• Processo FAPESP: 16/18636-7 - Mitigação da lixiviação de Nitrato em solos tropicais usando Hidróxidos duplos lamelares
• Beneficiário: Ali Mehmandoost Kotlar
• Modalidade de apoio: Bolsas no Brasil - Doutorado