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Forecasting the moisture dynamics of a landfill capping system comprising different geosynthetics: A NARX neural network approach
https://orcid.org/0000-0002-6057-3885
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, RMSEr has been less than 10% for the cuspated core geocomposite. Comparatively, RMSEr values increased to approximately 15% and 19% for the non-woven needle-punched geotextile and the non-woven needle-punched geotextile with band drains, respectively.
Highlights NARX neural network predicted the volumetric water content (VWC) in a landfill cap experiencing precipitation and dry events. Field data (e.g.,VWC) came from a capping system comprised of veneer soil and different geosynthetic drainage products. The NARX model VWC forecasts are compared against the state-space statistical model predictions to validate the robustness. NARX-predicted VWCs for a period of 21-days ahead are distributed with a mean error of 0.05 and a standard deviation of 0.2. The accuracy of NARX predictions was found to be far superior to that of the state-space statistical model.
Forecasting the moisture dynamics of a landfill capping system comprising different geosynthetics: A NARX neural network approach
https://orcid.org/0000-0002-6057-3885
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, RMSEr has been less than 10% for the cuspated core geocomposite. Comparatively, RMSEr values increased to approximately 15% and 19% for the non-woven needle-punched geotextile and the non-woven needle-punched geotextile with band drains, respectively.
Highlights NARX neural network predicted the volumetric water content (VWC) in a landfill cap experiencing precipitation and dry events. Field data (e.g.,VWC) came from a capping system comprised of veneer soil and different geosynthetic drainage products. The NARX model VWC forecasts are compared against the state-space statistical model predictions to validate the robustness. NARX-predicted VWCs for a period of 21-days ahead are distributed with a mean error of 0.05 and a standard deviation of 0.2. The accuracy of NARX predictions was found to be far superior to that of the state-space statistical model.
Forecasting the moisture dynamics of a landfill capping system comprising different geosynthetics: A NARX neural network approach
https://orcid.org/0000-0002-6057-3885
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, RMSEr has been less than 10% for the cuspated core geocomposite. Comparatively, RMSEr values increased to approximately 15% and 19% for the non-woven needle-punched geotextile and the non-woven needle-punched geotextile with band drains, respectively.
Highlights NARX neural network predicted the volumetric water content (VWC) in a landfill cap experiencing precipitation and dry events. Field data (e.g.,VWC) came from a capping system comprised of veneer soil and different geosynthetic drainage products. The NARX model VWC forecasts are compared against the state-space statistical model predictions to validate the robustness. NARX-predicted VWCs for a period of 21-days ahead are distributed with a mean error of 0.05 and a standard deviation of 0.2. The accuracy of NARX predictions was found to be far superior to that of the state-space statistical model.
Forecasting the moisture dynamics of a landfill capping system comprising different geosynthetics: A NARX neural network approach
Dassanayake, S.M. (author) / Mousa, Ahmad (author) / Fowmes, Gary J. (author) / Susilawati, S. (author) / Zamara, K. (author)
Geotextiles and Geomembranes ; 51 ; 282-292
2022-08-29
11 pages
Article (Journal)
Electronic Resource
English
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