6533b82dfe1ef96bd1291e05

RESEARCH PRODUCT

Artificial Neural Networks for Predicting the Water Retention Curve of Sicilian Agricultural Soils

Rosa AielloMassimo IovinoDaniela VanellaAlessandro D'emilioSimona Consoli

subject

lcsh:Hydraulic engineeringneural networkSoil textureWater retention curvesoil water retention curve0208 environmental biotechnologyGeography Planning and DevelopmentSoil science02 engineering and technologyAquatic ScienceSiltBiochemistryAkaike criterion; Neural network; Soil water retention curve; Van Genuchten function; Biochemistry; Geography Planning and Development; Aquatic Science; Water Science and Technologyvan Genuchten functionlcsh:Water supply for domestic and industrial purposesHydraulic conductivityPedotransfer functionlcsh:TC1-978Settore AGR/08 - Idraulica Agraria E Sistemazioni Idraulico-ForestaliWater Science and TechnologyPlanning and Developmentlcsh:TD201-500GeographySoil organic matter04 agricultural and veterinary sciences020801 environmental engineeringAkaike criterionSoil water040103 agronomy & agriculture0401 agriculture forestry and fisheriesEnvironmental scienceAkaike information criterion

description

Modeling soil-water regime and solute transport in the vadose zone is strategic for estimating agricultural productivity and optimizing irrigation water management. Direct measurements of soil hydraulic properties, i.e., the water retention curve and the hydraulic conductivity function, are often expensive and time-consuming, and represent a major obstacle to the application of simulation models. As a result, there is a great interest in developing pedotransfer functions (PTFs) that predict the soil hydraulic properties from more easily measured and/or routinely surveyed soil data, such as particle size distribution, bulk density (&rho

yearjournalcountryeditionlanguage
2018-10-12Water
https://doi.org/10.3390/w10101431