Multivariate Logistic Regression Model for Soil Erosion Susceptibility Assessment under Static and Dynamic Causative Factors
Soil erosion is a devastating land degradation process that needs to be spatially analyzed for identification of critical zones for sustainable management. Geospatial prediction through susceptibility analysis assesses the occurrence of soil erosion under a set of causative factors (CFs). Previous s...
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2019
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| Online Access: | http://umpir.ump.edu.my/id/eprint/26329/1/Multivariate%20Logistic%20Regression%20Model.pdf http://umpir.ump.edu.my/id/eprint/26329/ https://doi.org/10.15244/pjoes/91943 |
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| author | Sholagberu, Abdulkadir Taofeeq Muhammad Raza, Ul Mustafa Khamaruzaman, Wan Yusof Ahmad Mustafa, Hashim Shah, Mumtaz Muhammad M., Waris Isa, Mohamed H. |
| author_facet | Sholagberu, Abdulkadir Taofeeq Muhammad Raza, Ul Mustafa Khamaruzaman, Wan Yusof Ahmad Mustafa, Hashim Shah, Mumtaz Muhammad M., Waris Isa, Mohamed H. |
| author_sort | Sholagberu, Abdulkadir Taofeeq |
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| content_provider | Universiti Malaysia Pahang Al-Sultan Abdullah |
| content_source | UMPSA Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Soil erosion is a devastating land degradation process that needs to be spatially analyzed for identification of critical zones for sustainable management. Geospatial prediction through susceptibility analysis assesses the occurrence of soil erosion under a set of causative factors (CFs). Previous studies have considered majorly static CFs for susceptibility analysis, but neglect dynamic CFs. Thus, this study presents an evaluation of erosion susceptibility under the influence of both non-redundant static and dynamic CFs using multivariate logistic regression (MLR), remote sensing and geographic information system. The CFs considered include drainage density, lineament density, length-slope and soil erodibility as static CFs, and land surface temperature, soil moisture index, vegetation index and rainfall erosivity representing the dynamic CFs. These were parameterized to establish geospatial relationships with the occurrence of erosion. The results showed that length-slope had the highest positive impact on the occurrence of erosion, followed by lineament density. During the MLR classification process, predicted accuracies for the eroded and non-eroded locations were 89.1% and 83.6% respectively, with an overall prediction accuracy of 86.6%. The model’s performance was satisfactory, with 81.9% accuracy when validated using the area-under-curve method. The output map of this study will assist decision makers in sustainable watershed management to alleviate soil erosion. |
| format | Article |
| id | my.ump.umpir.26329 |
| institution | Universiti Malaysia Pahang |
| language | en |
| publishDate | 2019 |
| publisher | Pjoes |
| record_format | eprints |
| spelling | my.ump.umpir.263292019-11-06T02:43:09Z http://umpir.ump.edu.my/id/eprint/26329/ Multivariate Logistic Regression Model for Soil Erosion Susceptibility Assessment under Static and Dynamic Causative Factors Sholagberu, Abdulkadir Taofeeq Muhammad Raza, Ul Mustafa Khamaruzaman, Wan Yusof Ahmad Mustafa, Hashim Shah, Mumtaz Muhammad M., Waris Isa, Mohamed H. TA Engineering (General). Civil engineering (General) Soil erosion is a devastating land degradation process that needs to be spatially analyzed for identification of critical zones for sustainable management. Geospatial prediction through susceptibility analysis assesses the occurrence of soil erosion under a set of causative factors (CFs). Previous studies have considered majorly static CFs for susceptibility analysis, but neglect dynamic CFs. Thus, this study presents an evaluation of erosion susceptibility under the influence of both non-redundant static and dynamic CFs using multivariate logistic regression (MLR), remote sensing and geographic information system. The CFs considered include drainage density, lineament density, length-slope and soil erodibility as static CFs, and land surface temperature, soil moisture index, vegetation index and rainfall erosivity representing the dynamic CFs. These were parameterized to establish geospatial relationships with the occurrence of erosion. The results showed that length-slope had the highest positive impact on the occurrence of erosion, followed by lineament density. During the MLR classification process, predicted accuracies for the eroded and non-eroded locations were 89.1% and 83.6% respectively, with an overall prediction accuracy of 86.6%. The model’s performance was satisfactory, with 81.9% accuracy when validated using the area-under-curve method. The output map of this study will assist decision makers in sustainable watershed management to alleviate soil erosion. Pjoes 2019 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/26329/1/Multivariate%20Logistic%20Regression%20Model.pdf Sholagberu, Abdulkadir Taofeeq and Muhammad Raza, Ul Mustafa and Khamaruzaman, Wan Yusof and Ahmad Mustafa, Hashim and Shah, Mumtaz Muhammad and M., Waris and Isa, Mohamed H. (2019) Multivariate Logistic Regression Model for Soil Erosion Susceptibility Assessment under Static and Dynamic Causative Factors. Polish Journal of Environmental Studies, 28 (5). pp. 3419-3429. ISSN 1230-1485. (Published) https://doi.org/10.15244/pjoes/91943 https://doi.org/10.15244/pjoes/91943 |
| spellingShingle | TA Engineering (General). Civil engineering (General) Sholagberu, Abdulkadir Taofeeq Muhammad Raza, Ul Mustafa Khamaruzaman, Wan Yusof Ahmad Mustafa, Hashim Shah, Mumtaz Muhammad M., Waris Isa, Mohamed H. Multivariate Logistic Regression Model for Soil Erosion Susceptibility Assessment under Static and Dynamic Causative Factors |
| title | Multivariate Logistic Regression Model for Soil Erosion Susceptibility Assessment under Static and Dynamic Causative Factors |
| title_full | Multivariate Logistic Regression Model for Soil Erosion Susceptibility Assessment under Static and Dynamic Causative Factors |
| title_fullStr | Multivariate Logistic Regression Model for Soil Erosion Susceptibility Assessment under Static and Dynamic Causative Factors |
| title_full_unstemmed | Multivariate Logistic Regression Model for Soil Erosion Susceptibility Assessment under Static and Dynamic Causative Factors |
| title_short | Multivariate Logistic Regression Model for Soil Erosion Susceptibility Assessment under Static and Dynamic Causative Factors |
| title_sort | multivariate logistic regression model for soil erosion susceptibility assessment under static and dynamic causative factors |
| topic | TA Engineering (General). Civil engineering (General) |
| url | http://umpir.ump.edu.my/id/eprint/26329/1/Multivariate%20Logistic%20Regression%20Model.pdf http://umpir.ump.edu.my/id/eprint/26329/ https://doi.org/10.15244/pjoes/91943 https://doi.org/10.15244/pjoes/91943 |
| url_provider | http://umpir.ump.edu.my/ |