title: Modeling water outflow from tile-drained agricultural fields.
abstract: The estimation of the pollution risk of surface and ground water with plant protection products applied on fields depends highly on the reliable prediction of the water outflows over (surface runoff) and through (discharge through sub-surface drainage systems) the soil. In previous studies, water movement through the soil has been simulated mainly using physically-based models. The most frequently used models for predicting soil water movement are MACRO, HYDRUS-1D/2D and Root Zone Water Quality Model. However, these models are difficult to apply to a small portion of land due to the information required about the soil and climate, which are difficult to obtain for each plot separately. In this paper, we focus on improving the performance and applicability of water outflow modeling by using a modeling approach based on machine learning techniques. It allows us to overcome the major drawbacks of physically-based models e.g., the complexity and difficulty of obtaining the information necessary for the calibration and the validation, by learning models from data collected from experimental fields that are representative for a wider area (region). We evaluate the proposed approach on data obtained from the La Jaillière experimental site, located in Western France. This experimental site represents one of the ten scenarios contained in the MACRO system. Our study focuses on two types of water outflows: discharge through sub-surface drainage systems and surface runoff. The results show that the proposed modeling approach successfully extracts knowledge from the collected data, avoiding the need to provide the information for calibration and validation of physically-based models. In addition, we compare the overall performance of the learned models with the performance of existing models MACRO and RZWQM. The comparison shows overall improvement in the prediction of discharge through sub-surface drainage systems, and partial improvement in the prediction of the surface runoff, in years with intensive rainfall.
authors.affiliation: International Postgraduate School, Jožef Stefan Institute, Jamova cesta 39, 1000 Ljubljana, Slovenia; Department of Knowledge Technologies, Jožef Stefan Institute, Jamova cesta 39, 1000 Ljubljana, Slovenia. Electronic address: vladimir.kuzmanovski@ijs.si.
authors.forename: Vladimir
authors.initials: V
authors.lastname: Kuzmanovski
authors.affiliation: Department of Knowledge Technologies, Jožef Stefan Institute, Jamova cesta 39, 1000 Ljubljana, Slovenia. Electronic address: aneta.trajanov@ijs.si.
authors.forename: Aneta
authors.initials: A
authors.lastname: Trajanov
authors.affiliation: ARVALIS - Institut du Végétal, 21, Chemin de Pau, 64121 Montardon, France. Electronic address: f.leprince@arvalisinstitutduvegetal.fr.
authors.forename: Florence
authors.initials: F
authors.lastname: Leprince
authors.affiliation: International Postgraduate School, Jožef Stefan Institute, Jamova cesta 39, 1000 Ljubljana, Slovenia; Department of Knowledge Technologies, Jožef Stefan Institute, Jamova cesta 39, 1000 Ljubljana, Slovenia; Center of Excellence for Integrated Approaches in Chemistry and Biology of Proteins, Jamova cesta 39, 1000 Ljubljana, Slovenia. Electronic address: saso.dzeroski@ijs.si.
authors.forename: Sašo
authors.initials: S
authors.lastname: Džeroski
authors.affiliation: International Postgraduate School, Jožef Stefan Institute, Jamova cesta 39, 1000 Ljubljana, Slovenia; Department of Knowledge Technologies, Jožef Stefan Institute, Jamova cesta 39, 1000 Ljubljana, Slovenia. Electronic address: marko.debeljak@ijs.si.
authors.forename: Marko
authors.initials: M
authors.lastname: Debeljak
keywords: Agriculture; Data mining; Drainage discharge; Machine learning; Modeling; Surface runoff