GIS-based digital modeling of soil infiltration in calcareous soils. Issue 12 (3rd July 2020)

• Record Type: Journal Article
• Authors: Zou, Cui; Wang, Zesong; Cui, Xianping; Ostovari, Yaser
• Is Part Of: Communications in soil science and plant analysis. Volume 51:Issue 12(2020)
• Page Start: 1590
• Page End: 1601
• Publication Date: 2020-07-03
• Subjects: ANN -- S- and A- parameters -- infiltration process; Soil science -- Periodicals; Plants -- Chemical analysis -- Periodicals; Agricultural chemistry -- Periodicals; 631.405
• DOI: 10.1080/00103624.2020.1791153
• Languages: English
• ISSNs: 0010-3624

Abstract

ABSTRACT: Predicting soil infiltration at the field scale with high contents in calcareous materials is important for a better understanding of land management. The objectives of this study were to develop a GIS-based digital modeling of soil infiltration in calcareous soils using environmental data in Iran. The soil infiltration data with three replications were measured at 92 points at the regional scale. At each site, the soil readily available properties were determined. Furthermore, remote-sensing and digital elevation model data were applied as auxiliary data. The artificial neural networks (ANN) model was applied for the prediction of soil sorptivity ( S -parameter) and soil steady infiltration rate ( A -parameter). Input data in this study were classified into two groups (i) based on the soil readily available properties and (ii) based on the soil readily available properties and principal components (PCs) obtained by using the auxiliary data. The results indicated a better performance of ANN models derived according to the soil plus PCs data than derived models that used only soil data for predicting both S - and A -parameters. The R 2 evaluation criteria increased from 0.39 to 0.57 in predicting S -parameter and from 0.44 to 0.59 in predicting A -parameter. It was concluded that the applying environmental data, i.e., data derived from topography factors and remotely sensed information, could be potential data for improving S - and A -parameter prediction and developing high quality of infiltration parameter maps.