Comparing Four Types Methods for Karst NDVI Prediction Based on Machine Learning
As a link for energy transfer between the land and atmosphere in the terrestrial ecosystem, karst vegetation plays an important role. Karst vegetation is not only affected by environmental factors but also by intense human activities. The nonlinear characteristics of vegetation growth are induced by...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/12/10/1341 |
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| author | Yuju Ma Liyuan Zuo Jiangbo Gao Qiang Liu Lulu Liu |
| author_facet | Yuju Ma Liyuan Zuo Jiangbo Gao Qiang Liu Lulu Liu |
| author_sort | Yuju Ma |
| collection | DOAJ |
| description | As a link for energy transfer between the land and atmosphere in the terrestrial ecosystem, karst vegetation plays an important role. Karst vegetation is not only affected by environmental factors but also by intense human activities. The nonlinear characteristics of vegetation growth are induced by the interaction mechanism of these factors. Previous studies of this relationship were not comprehensive, and it is necessary to further explore it using a suitable method. In this study, we selected climate, human activities, topography, and soil texture as the response factors; a nonlinear relationship model between the karst normalized difference vegetation index (NDVI) and these factors was established by applying a back propagation neural network (BPNN), a radial basis function neural network (RBFNN), the random forest (RF) algorithm, and support vector regression (SVR); and then, the karst NDVI was predicted. The coefficient of determination (R<sup>2</sup>), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE) of the obtained results were calculated, and the mean R<sup>2</sup> values of the BPNN, RBFNN, RF, and SVR models were determined to be 0.77, 0.86, 0.89, and 0.91, respectively. Compared with the BPNN, RBFNN, and RF models, the SVR model had the lowest errors, with mean MSE, RMSE, and MAPE values of 0.001, 0.02, and 2.77, respectively. The results show that the BPNN, RBFNN, RF, and SVR models are within acceptable ranges for karst NDVI prediction, but the overall performance of the SVR model is the best, and it is more suitable for karst vegetation prediction. |
| first_indexed | 2024-03-10T06:43:15Z |
| format | Article |
| id | doaj.art-3cef0befa83f454cbaf6f8ec4cb2dcb2 |
| institution | Directory Open Access Journal |
| issn | 2073-4433 |
| language | English |
| last_indexed | 2024-03-10T06:43:15Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj.art-3cef0befa83f454cbaf6f8ec4cb2dcb22023-11-22T17:26:06ZengMDPI AGAtmosphere2073-44332021-10-011210134110.3390/atmos12101341Comparing Four Types Methods for Karst NDVI Prediction Based on Machine LearningYuju Ma0Liyuan Zuo1Jiangbo Gao2Qiang Liu3Lulu Liu4College of Engineering, Ocean University of China, Qingdao 266100, ChinaKey Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaKey Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaCollege of Engineering, Ocean University of China, Qingdao 266100, ChinaKey Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaAs a link for energy transfer between the land and atmosphere in the terrestrial ecosystem, karst vegetation plays an important role. Karst vegetation is not only affected by environmental factors but also by intense human activities. The nonlinear characteristics of vegetation growth are induced by the interaction mechanism of these factors. Previous studies of this relationship were not comprehensive, and it is necessary to further explore it using a suitable method. In this study, we selected climate, human activities, topography, and soil texture as the response factors; a nonlinear relationship model between the karst normalized difference vegetation index (NDVI) and these factors was established by applying a back propagation neural network (BPNN), a radial basis function neural network (RBFNN), the random forest (RF) algorithm, and support vector regression (SVR); and then, the karst NDVI was predicted. The coefficient of determination (R<sup>2</sup>), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE) of the obtained results were calculated, and the mean R<sup>2</sup> values of the BPNN, RBFNN, RF, and SVR models were determined to be 0.77, 0.86, 0.89, and 0.91, respectively. Compared with the BPNN, RBFNN, and RF models, the SVR model had the lowest errors, with mean MSE, RMSE, and MAPE values of 0.001, 0.02, and 2.77, respectively. The results show that the BPNN, RBFNN, RF, and SVR models are within acceptable ranges for karst NDVI prediction, but the overall performance of the SVR model is the best, and it is more suitable for karst vegetation prediction.https://www.mdpi.com/2073-4433/12/10/1341karst NDVInatural and anthropogenic factorsBPNNRBFNNRFSVR |
| spellingShingle | Yuju Ma Liyuan Zuo Jiangbo Gao Qiang Liu Lulu Liu Comparing Four Types Methods for Karst NDVI Prediction Based on Machine Learning Atmosphere karst NDVI natural and anthropogenic factors BPNN RBFNN RF SVR |
| title | Comparing Four Types Methods for Karst NDVI Prediction Based on Machine Learning |
| title_full | Comparing Four Types Methods for Karst NDVI Prediction Based on Machine Learning |
| title_fullStr | Comparing Four Types Methods for Karst NDVI Prediction Based on Machine Learning |
| title_full_unstemmed | Comparing Four Types Methods for Karst NDVI Prediction Based on Machine Learning |
| title_short | Comparing Four Types Methods for Karst NDVI Prediction Based on Machine Learning |
| title_sort | comparing four types methods for karst ndvi prediction based on machine learning |
| topic | karst NDVI natural and anthropogenic factors BPNN RBFNN RF SVR |
| url | https://www.mdpi.com/2073-4433/12/10/1341 |
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