Neural computing techniques to estimate the hydraulic conductivity of porous media
Accurate hydraulic conductivity (K) estimation of porous media is crucial in hydrological studies. Recently, groundwater investigators have utilized neural computing techniques to indirectly estimate soil sample K instead of time-consuming direct methods. The present study utilizes easily measurable...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IWA Publishing
2023-06-01
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| Series: | Water Supply |
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| Online Access: | http://ws.iwaponline.com/content/23/6/2586 |
| _version_ | 1797783062941007872 |
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| author | Abhishish Chandel Vijay Shankar Navsal Kumar |
| author_facet | Abhishish Chandel Vijay Shankar Navsal Kumar |
| author_sort | Abhishish Chandel |
| collection | DOAJ |
| description | Accurate hydraulic conductivity (K) estimation of porous media is crucial in hydrological studies. Recently, groundwater investigators have utilized neural computing techniques to indirectly estimate soil sample K instead of time-consuming direct methods. The present study utilizes easily measurable characteristics, i.e., grain size at 10 and 50% finer by weight, porosity, and uniformity coefficient as input variables to examine the efficacy of feed-forward neural network (FFNN), Kohonen self-organizing maps (KSOM), and multiple linear regression (MLR) models in estimating the K of soil samples. Model development and validation used 70 and 30% of datasets, respectively. The determination coefficient (R2), root mean square error (RMSE), and mean bias error (MBE) were used to compare the model performance with the measured K values. The study's outcome indicates that the FFNN and KSOM models better estimate the K value, while the MLR model performs merely satisfactorily. Overall, during validation, the FFNN model correlates better with the measured values having R2, RMSE, and MBE of 0.94, 0.016, and 0.006, whereas the corresponding values for KSOM are 0.91, 0.024, and −0.004, and that for MLR are 0.87, 0.024, and 0.013, respectively. Notably, the FFNN model exhibits superior prediction performance and can be employed in aquifers for precise K estimation.
HIGHLIGHTS
The study examines the efficacy of FFNN, KSOM, and MLR models in estimating the K of porous media.;
The statistical indicators reveal that the FFNN model performs better in estimating the K of porous media and can be employed in sub-surface flow studies for precise K estimation.; |
| first_indexed | 2024-03-13T00:20:48Z |
| format | Article |
| id | doaj.art-e5270f6db8c9476dbd48cf5631486006 |
| institution | Directory Open Access Journal |
| issn | 1606-9749 1607-0798 |
| language | English |
| last_indexed | 2024-03-13T00:20:48Z |
| publishDate | 2023-06-01 |
| publisher | IWA Publishing |
| record_format | Article |
| series | Water Supply |
| spelling | doaj.art-e5270f6db8c9476dbd48cf56314860062023-07-11T16:32:57ZengIWA PublishingWater Supply1606-97491607-07982023-06-012362586260310.2166/ws.2023.143143Neural computing techniques to estimate the hydraulic conductivity of porous mediaAbhishish Chandel0Vijay Shankar1Navsal Kumar2 National Institute of Technology, Hamirpur, India National Institute of Technology, Hamirpur, India Shoolini University, Solan, India Accurate hydraulic conductivity (K) estimation of porous media is crucial in hydrological studies. Recently, groundwater investigators have utilized neural computing techniques to indirectly estimate soil sample K instead of time-consuming direct methods. The present study utilizes easily measurable characteristics, i.e., grain size at 10 and 50% finer by weight, porosity, and uniformity coefficient as input variables to examine the efficacy of feed-forward neural network (FFNN), Kohonen self-organizing maps (KSOM), and multiple linear regression (MLR) models in estimating the K of soil samples. Model development and validation used 70 and 30% of datasets, respectively. The determination coefficient (R2), root mean square error (RMSE), and mean bias error (MBE) were used to compare the model performance with the measured K values. The study's outcome indicates that the FFNN and KSOM models better estimate the K value, while the MLR model performs merely satisfactorily. Overall, during validation, the FFNN model correlates better with the measured values having R2, RMSE, and MBE of 0.94, 0.016, and 0.006, whereas the corresponding values for KSOM are 0.91, 0.024, and −0.004, and that for MLR are 0.87, 0.024, and 0.013, respectively. Notably, the FFNN model exhibits superior prediction performance and can be employed in aquifers for precise K estimation. HIGHLIGHTS The study examines the efficacy of FFNN, KSOM, and MLR models in estimating the K of porous media.; The statistical indicators reveal that the FFNN model performs better in estimating the K of porous media and can be employed in sub-surface flow studies for precise K estimation.;http://ws.iwaponline.com/content/23/6/2586ffnngrain-sizehydraulic conductivityksomporosity |
| spellingShingle | Abhishish Chandel Vijay Shankar Navsal Kumar Neural computing techniques to estimate the hydraulic conductivity of porous media Water Supply ffnn grain-size hydraulic conductivity ksom porosity |
| title | Neural computing techniques to estimate the hydraulic conductivity of porous media |
| title_full | Neural computing techniques to estimate the hydraulic conductivity of porous media |
| title_fullStr | Neural computing techniques to estimate the hydraulic conductivity of porous media |
| title_full_unstemmed | Neural computing techniques to estimate the hydraulic conductivity of porous media |
| title_short | Neural computing techniques to estimate the hydraulic conductivity of porous media |
| title_sort | neural computing techniques to estimate the hydraulic conductivity of porous media |
| topic | ffnn grain-size hydraulic conductivity ksom porosity |
| url | http://ws.iwaponline.com/content/23/6/2586 |
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