The Library
Forecasting the moisture dynamics of a landfill capping system comprising different geosynthetics : a NARX neural network approach
Tools
Tools
Tools
Dassanayake, S. M., Mousa, Ahmad, Fowmes, Gary J., Susilawati, S. and Zamara, K. (2022) Forecasting the moisture dynamics of a landfill capping system comprising different geosynthetics : a NARX neural network approach. Geotextiles and Geomembranes . doi:10.1016/j.geotexmem.2022.08.005 ISSN 0266-1144. (In Press)
![[img]](http://wrap.warwick.ac.uk/style/images/fileicons/application_pdf.png) |
PDF
WRAP-Forecasting-the-moisture-dynamics-of-a-landfill-capping-system-comprising-different-geosynthetics-a-NARX-neural-network-approach-Fowmes-2022.pdf - Accepted Version Embargoed item. Restricted access to Repository staff only until 23 September 2023. Contact author directly, specifying your specific needs. - Requires a PDF viewer. Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. Download (5Mb) |
Official URL: https://doi.org/10.1016/j.geotexmem.2022.08.005
Abstract
Engineered landfill capping systems consist of geosynthetics and soil layers, which often experience inconsistent and extreme weather events throughout their service life. Complex moisture dynamics in the capping layers can be created by these weather events in combination with other field conditions and can be detrimental to the system's integrity. The limited data on the hydraulic performance of landfill capping systems is a major challenge that hinders the development, validation, and calibration of models that can be used for realistic forecasting of these dynamics. Using the field-level data collected at the Bletchley landfill site, UK, this study develops a data-driven forecasting approach employing a non-linear autoregressive neural network with exogenous inputs (NARX). The data includes precipitation and volumetric water content (VWC) of the capping soil overlaying different geosynthetic layers recorded from Nov 2011 to July 2012. The NARX network was trained using the VWC data as inputs and precipitation data as the exogenous input. Also, the accuracy of NARX predictions was compared against that of a state-space statistical model. NARX-predicted VWC values for a period of 21-days ahead are distributed with a mean error of 0.05 and a standard deviation of 0.2. In the majority of prediction windows, NARX approach outperforms the state-space model. For all NARX prediction periods,
| Item Type: | Journal Article | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) T Technology > TD Environmental technology. Sanitary engineering |
||||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||||
| Library of Congress Subject Headings (LCSH): | Sanitary landfills , Geosynthetics , Hydrology, Groundwater | ||||||||
| Journal or Publication Title: | Geotextiles and Geomembranes | ||||||||
| Publisher: | Elsevier | ||||||||
| ISSN: | 0266-1144 | ||||||||
| Official Date: | 2022 | ||||||||
| Dates: |
|
||||||||
| Number of Pages: | 11 | ||||||||
| DOI: | 10.1016/j.geotexmem.2022.08.005 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | In Press | ||||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||||
| Copyright Holders: | Elsevier | ||||||||
| Date of first compliant deposit: | 13 September 2022 | ||||||||
| RIOXX Funder/Project Grant: |
|
||||||||
| Related URLs: |
Request changes or add full text files to a record
Repository staff actions (login required)
 |
View Item |
