Estimating root zone soil moisture using multilayer feedforward neural networks based on Levenberg-Maquardt and scaled conjugate gradient algorithms. (24th September 2019)

• Record Type: Journal Article
• Authors: Odiyo, John Ogony; Makungo, Rachel
• Is Part Of: International journal of hydrology science and technology. Volume 9:Number 4(2019)
• Page Start: 399
• Page End: 411
• Publication Date: 2019-09-24
• Subjects: algorithm -- estimation accuracy -- Levenberg-Maquardt -- multilayer feedforward -- neural networks -- root zone -- scaled conjugate gradient -- SCG -- soil moisture; Hydrology -- Periodicals; Water resources development -- Periodicals; Hydrology; 553.705
• Languages: English
• ISSNs: 2042-7808

Abstract

This paper presents the use of multilayer feedforward neural networks for root zone soil moisture estimation based on Levenberg-Maquardt (LM) and scaled conjugate gradient (SCG) algorithms. Data driven approaches such as artificial neural networks overcome some limitations of remote sensing, conceptual and semi-analytical models in root zone soil moisture estimation. This creates the need to compare neural networks training algorithms to determine those with best estimation accuracy. Observed volumetric soil moisture at 80 cm depth, rainfall and evaporation data were used to estimate soil moisture at 120 and 180 cm depths, within MATLAB. SCG trained network underestimated and/or overestimated soil moisture as compared to LM trained network. LM trained network has better performance and estimation accuracy. Estimated soil moisture is useful for irrigation scheduling, hydrological modelling, and groundwater recharge estimation in the study area.