Use of machine learning for drain flows predictions in tile-drained agricultural areas

Abstract

Temporal drain flow dynamics and their underlying controlling factors are important for understanding the needs for water resource management in tile drained agricultural areas. The use of physics-based water flow models to understand tile drained systems is quite common. These physics-based models are complex and have high computational demand due to the high spatial and temporal dimensionality of the problem. We examine whether machine learning (ML) models can offer a simpler tool for water management. The main aim of our study is to assess the potential of ML tools for predicting drain flow with varying climate parameters and hydrogeological properties in different catchments in Denmark. We rely on unique data containing time series of daily drain flow in 26 field-scale tile drained catchments in Denmark: climate data (precipitation, potential evapotranspiration, temperature); geological properties (clay fraction, first sand layer thickness, first clay layer thickness); and topographical indexes (curvature, topographical wetness indexes, topographical position index, elevation etc.). The ML algorithm XGBoost is used to predict drain flow in the 26 drain catchments based on both static and dynamic variables. This algorithm also provides an independent measure of the value of information contained in variables related to climate, geology and topography for the prediction of tile drain flows. The ML approach examined could provide a more transferable, faster, and less computationally expensive tool to predict drain flow dynamics. Simultaneously, the results of the study offer insight into the underlying factors that control drain flow, allowing for improved data collection and physics-based model development.