Objective diagnosis of machine learning method applicability to land comprehensive carrying capacity evaluation: A case study based on integrated RF and DPSIR models
The evaluation of land comprehensive carrying capacity (LCCC) is a popular topic in regional sustainable development and land science research. However, these evaluations are often not objective due to the complexity and nonlinear characteristics of the LCCC evaluation indicators. This study provide...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-07-01
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| Series: | Ecological Indicators |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X23004806 |
| _version_ | 1797823173464424448 |
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| author | Wenmin Hu Shibo Zhang Yushan Fu Guanyu Jia Ruihan Yang Shouyun Shen Yi Li Guo Li |
| author_facet | Wenmin Hu Shibo Zhang Yushan Fu Guanyu Jia Ruihan Yang Shouyun Shen Yi Li Guo Li |
| author_sort | Wenmin Hu |
| collection | DOAJ |
| description | The evaluation of land comprehensive carrying capacity (LCCC) is a popular topic in regional sustainable development and land science research. However, these evaluations are often not objective due to the complexity and nonlinear characteristics of the LCCC evaluation indicators. This study provides a new framework for using machine learning methods to objectively evaluate LCCC. The Driver-Pressure-State-Impact-Response (DPSIR) indicator conceptual framework was used for the spatial visualisation of indicators and was then combined with the random forest (RF) model to optimise the LCCC evaluation. The results showed the following: (1) The accuracy of the integrated RF and DPSIR model was better than that of traditional support vector machine (SVM) and principal component analysis (PCA) methods, indicating that RF model was more suitable for LCCC evaluations. (2) The contribution of the DPSIR indicators after RF optimisation was significantly different from that of the traditional analytic hierarchy process (AHP), the contribution of the influence subsystem (I) (48.7%) was enhanced, while the contribution of the drive subsystem (D) (6.8%) was weakened, demonstrating that after RF optimisation, the DPSIR was more conducive for handling complex nonlinear systems and objectively reflecting indicator contributions. (3) The objective attributes of the DPSIR indicators were optimised through the processing of spatial visualisation models, indicating that RF was suitable for processing evaluations with a multidimensional spatial heterogeneity index system. The LCCC evaluation model can be applied to other carrying capacity case studies and operational processes and provide a scientific method for handling the multidimensional nonlinear evaluation index system and objective evaluation of LCCC. |
| first_indexed | 2024-03-13T10:19:09Z |
| format | Article |
| id | doaj.art-c37aec77b80348e0a3b4db612f0fe6d3 |
| institution | Directory Open Access Journal |
| issn | 1470-160X |
| language | English |
| last_indexed | 2024-03-13T10:19:09Z |
| publishDate | 2023-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Indicators |
| spelling | doaj.art-c37aec77b80348e0a3b4db612f0fe6d32023-05-21T04:34:32ZengElsevierEcological Indicators1470-160X2023-07-01151110338Objective diagnosis of machine learning method applicability to land comprehensive carrying capacity evaluation: A case study based on integrated RF and DPSIR modelsWenmin Hu0Shibo Zhang1Yushan Fu2Guanyu Jia3Ruihan Yang4Shouyun Shen5Yi Li6Guo Li7Central South University of Forestry and Technology, Changsha 410004, China; Research Institute of Forest Resources Information Technique, Chinese Academy of Forestry, Beijing 100001, China; Engineering Technology Research Centre of Big Data for Landscape Resources in Nature Protected Areas of Hunan Province, Changsha 410004, ChinaCentral South University of Forestry and Technology, Changsha 410004, ChinaCentral South University of Forestry and Technology, Changsha 410004, ChinaCentral South University of Forestry and Technology, Changsha 410004, ChinaCentral South University of Forestry and Technology, Changsha 410004, ChinaCentral South University of Forestry and Technology, Changsha 410004, China; Engineering Technology Research Centre of Big Data for Landscape Resources in Nature Protected Areas of Hunan Province, Changsha 410004, ChinaEngineering Technology Research Centre of Big Data for Landscape Resources in Nature Protected Areas of Hunan Province, Changsha 410004, ChinaCentral South University of Forestry and Technology, Changsha 410004, China; Engineering Technology Research Centre of Big Data for Landscape Resources in Nature Protected Areas of Hunan Province, Changsha 410004, China; Corresponding author at: Central South University of Forestry and Technology, Changsha 410004, China.The evaluation of land comprehensive carrying capacity (LCCC) is a popular topic in regional sustainable development and land science research. However, these evaluations are often not objective due to the complexity and nonlinear characteristics of the LCCC evaluation indicators. This study provides a new framework for using machine learning methods to objectively evaluate LCCC. The Driver-Pressure-State-Impact-Response (DPSIR) indicator conceptual framework was used for the spatial visualisation of indicators and was then combined with the random forest (RF) model to optimise the LCCC evaluation. The results showed the following: (1) The accuracy of the integrated RF and DPSIR model was better than that of traditional support vector machine (SVM) and principal component analysis (PCA) methods, indicating that RF model was more suitable for LCCC evaluations. (2) The contribution of the DPSIR indicators after RF optimisation was significantly different from that of the traditional analytic hierarchy process (AHP), the contribution of the influence subsystem (I) (48.7%) was enhanced, while the contribution of the drive subsystem (D) (6.8%) was weakened, demonstrating that after RF optimisation, the DPSIR was more conducive for handling complex nonlinear systems and objectively reflecting indicator contributions. (3) The objective attributes of the DPSIR indicators were optimised through the processing of spatial visualisation models, indicating that RF was suitable for processing evaluations with a multidimensional spatial heterogeneity index system. The LCCC evaluation model can be applied to other carrying capacity case studies and operational processes and provide a scientific method for handling the multidimensional nonlinear evaluation index system and objective evaluation of LCCC.http://www.sciencedirect.com/science/article/pii/S1470160X23004806Land comprehensive carrying capacityRandom forestDPSIR modelSupport vector machineLand use |
| spellingShingle | Wenmin Hu Shibo Zhang Yushan Fu Guanyu Jia Ruihan Yang Shouyun Shen Yi Li Guo Li Objective diagnosis of machine learning method applicability to land comprehensive carrying capacity evaluation: A case study based on integrated RF and DPSIR models Ecological Indicators Land comprehensive carrying capacity Random forest DPSIR model Support vector machine Land use |
| title | Objective diagnosis of machine learning method applicability to land comprehensive carrying capacity evaluation: A case study based on integrated RF and DPSIR models |
| title_full | Objective diagnosis of machine learning method applicability to land comprehensive carrying capacity evaluation: A case study based on integrated RF and DPSIR models |
| title_fullStr | Objective diagnosis of machine learning method applicability to land comprehensive carrying capacity evaluation: A case study based on integrated RF and DPSIR models |
| title_full_unstemmed | Objective diagnosis of machine learning method applicability to land comprehensive carrying capacity evaluation: A case study based on integrated RF and DPSIR models |
| title_short | Objective diagnosis of machine learning method applicability to land comprehensive carrying capacity evaluation: A case study based on integrated RF and DPSIR models |
| title_sort | objective diagnosis of machine learning method applicability to land comprehensive carrying capacity evaluation a case study based on integrated rf and dpsir models |
| topic | Land comprehensive carrying capacity Random forest DPSIR model Support vector machine Land use |
| url | http://www.sciencedirect.com/science/article/pii/S1470160X23004806 |
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