Premium
Modelling of water reserves in mineral soils with different retention properties
Author(s) -
Orzepowski Wojciech,
Paruch Adam M.,
Kowalczyk Tomasz,
Pokładek Ryszard,
Pulikowski Krzysztof
Publication year - 2017
Publication title -
water and environment journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.437
H-Index - 37
eISSN - 1747-6593
pISSN - 1747-6585
DOI - 10.1111/wej.12255
Subject(s) - soil water , precipitation , environmental science , soil science , water content , artificial neural network , hydrology (agriculture) , geology , meteorology , geotechnical engineering , computer science , machine learning , geography
This work focuses on modelling soil water reserves using an Artificial Neural Network (ANN). Four model variants were established based on 843 records (verified through 268 measurements) of soil water content (SWC) measured at full‐scale field sites located in Southwest Poland. It is revealed that commonly recorded climatic data (precipitation and temperature) linked with SWC and field water capacity (FWC) are applicable in the ANN modelling. The basic model (utilising the meteorological data) was the most suitable for soil profiles with thicknesses of 0–25 cm, while in profiles with thicknesses of 0–50 cm and 0–100 cm the comprehensive ANN model (linking climatic data, FWC and SWC) was the most appropriate. Furthermore, comparative studies of the measured and modelled data indicated their statistical convergence, thus providing support for the practical implementation of the proposed ANN modelling.