Integrating piecewise and symbolic regression with remote sensing data for spatiotemporal analysis of surface water total dissolved solids in the Karun River, Iran
Monitoring water quality, including total dissolved solids (TDS), across various spatial and temporal scales is essential for comprehending water health level. The Karun River's water is a vital resource for drinking water supply and agricultural irrigation, making accurate monitoring of its qu...
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| Language: | English |
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Elsevier
2025-03-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025002476 |
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| author | Javad Zahiri Mohammad Reza Nikoo Adell Moradi-Sabzkouhi Mitra Cheraghi Nazmi Mat Nawi |
| author_facet | Javad Zahiri Mohammad Reza Nikoo Adell Moradi-Sabzkouhi Mitra Cheraghi Nazmi Mat Nawi |
| author_sort | Javad Zahiri |
| collection | DOAJ |
| description | Monitoring water quality, including total dissolved solids (TDS), across various spatial and temporal scales is essential for comprehending water health level. The Karun River's water is a vital resource for drinking water supply and agricultural irrigation, making accurate monitoring of its quality crucial for ensuring public health and sustainable resource management. This study presents a novel hybrid modeling approach that combines piecewise and symbolic regression (SR) models with Landsat imagery to predict TDS levels in river systems. Specific spectral bands, including the red and near-infrared reflectance from Landsat-9, were used as input variables. Field sampling of TDS, as the output variable, was conducted along the Karun River on three dates, synchronized with Landsat satellite images. The innovative use of a fuzzy-based uncertainty analysis, coupled with AHP weighting, allowed for a comprehensive assessment of TDS estimation accuracy and uncertainty. The Composite Uncertainty Index (CUI) approach revealed that the Multivariate Adaptive Regression Splines (MARS) and M5 models performed better than other models, with CUI values of 0.83 and 0.72, respectively. MARS demonstrated higher accuracy under low uncertainty conditions, while M5P excelled in scenarios of elevated uncertainty due to its reduced sensitivity and strong Nash-Sutcliffe coefficient. The hybrid modeling approach presented in this study offers a unique contribution to remote sensing-based water quality monitoring. By leveraging advanced regression techniques and uncertainty quantification, the findings enable more reliable predictions of TDS levels, which are crucial for sustainable river management. |
| format | Article |
| id | doaj-art-771969c56cad4b3d8df51f7078f32faf |
| institution | Kabale University |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-771969c56cad4b3d8df51f7078f32faf2025-01-31T05:12:20ZengElsevierResults in Engineering2590-12302025-03-0125104159Integrating piecewise and symbolic regression with remote sensing data for spatiotemporal analysis of surface water total dissolved solids in the Karun River, IranJavad Zahiri0Mohammad Reza Nikoo1Adell Moradi-Sabzkouhi2Mitra Cheraghi3Nazmi Mat Nawi4Department of Water Engineering, Agricultural Sciences and Natural Resources University of Khuzestan, Iran; Corresponding authors.Department of Civil and Architectural Engineering, Sultan Qaboos University, Muscat, Oman; Corresponding authors.Department of Water Engineering, Agricultural Sciences and Natural Resources University of Khuzestan, IranDepartment of Nature Engineering, Agricultural Sciences and Natural Resources University of Khuzestan, IranInstitute of Plantation Studies, Universiti Putra Malaysia, MalaysiaMonitoring water quality, including total dissolved solids (TDS), across various spatial and temporal scales is essential for comprehending water health level. The Karun River's water is a vital resource for drinking water supply and agricultural irrigation, making accurate monitoring of its quality crucial for ensuring public health and sustainable resource management. This study presents a novel hybrid modeling approach that combines piecewise and symbolic regression (SR) models with Landsat imagery to predict TDS levels in river systems. Specific spectral bands, including the red and near-infrared reflectance from Landsat-9, were used as input variables. Field sampling of TDS, as the output variable, was conducted along the Karun River on three dates, synchronized with Landsat satellite images. The innovative use of a fuzzy-based uncertainty analysis, coupled with AHP weighting, allowed for a comprehensive assessment of TDS estimation accuracy and uncertainty. The Composite Uncertainty Index (CUI) approach revealed that the Multivariate Adaptive Regression Splines (MARS) and M5 models performed better than other models, with CUI values of 0.83 and 0.72, respectively. MARS demonstrated higher accuracy under low uncertainty conditions, while M5P excelled in scenarios of elevated uncertainty due to its reduced sensitivity and strong Nash-Sutcliffe coefficient. The hybrid modeling approach presented in this study offers a unique contribution to remote sensing-based water quality monitoring. By leveraging advanced regression techniques and uncertainty quantification, the findings enable more reliable predictions of TDS levels, which are crucial for sustainable river management.http://www.sciencedirect.com/science/article/pii/S2590123025002476Total dissolved solidsSymbolic regression modelM5 model treeMARS modelLandsat imagery |
| spellingShingle | Javad Zahiri Mohammad Reza Nikoo Adell Moradi-Sabzkouhi Mitra Cheraghi Nazmi Mat Nawi Integrating piecewise and symbolic regression with remote sensing data for spatiotemporal analysis of surface water total dissolved solids in the Karun River, Iran Results in Engineering Total dissolved solids Symbolic regression model M5 model tree MARS model Landsat imagery |
| title | Integrating piecewise and symbolic regression with remote sensing data for spatiotemporal analysis of surface water total dissolved solids in the Karun River, Iran |
| title_full | Integrating piecewise and symbolic regression with remote sensing data for spatiotemporal analysis of surface water total dissolved solids in the Karun River, Iran |
| title_fullStr | Integrating piecewise and symbolic regression with remote sensing data for spatiotemporal analysis of surface water total dissolved solids in the Karun River, Iran |
| title_full_unstemmed | Integrating piecewise and symbolic regression with remote sensing data for spatiotemporal analysis of surface water total dissolved solids in the Karun River, Iran |
| title_short | Integrating piecewise and symbolic regression with remote sensing data for spatiotemporal analysis of surface water total dissolved solids in the Karun River, Iran |
| title_sort | integrating piecewise and symbolic regression with remote sensing data for spatiotemporal analysis of surface water total dissolved solids in the karun river iran |
| topic | Total dissolved solids Symbolic regression model M5 model tree MARS model Landsat imagery |
| url | http://www.sciencedirect.com/science/article/pii/S2590123025002476 |
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