Estimating root zone soil moisture using multilayer feedforward neural networks based on Levenberg-Maquardt and scaled conjugate gradient algorithms Online publication date: Tue, 24-Sep-2019
by John Ogony Odiyo; Rachel Makungo
International Journal of Hydrology Science and Technology (IJHST), Vol. 9, No. 4, 2019
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.
Online publication date: Tue, 24-Sep-2019
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Hydrology Science and Technology (IJHST):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email email@example.com