Comparing Four Types Methods for Karst NDVI Prediction Based on Machine Learning
| Autor: | Liyuan Zuo, Qiang Liu, Jiangbo Gao, Yuju Ma, Lulu Liu |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Atmospheric Science
geography SVR Coefficient of determination geography.geographical_feature_category Mean squared error BPNN prediction comparison Vegetation Environmental Science (miscellaneous) Karst Normalized Difference Vegetation Index Random forest Support vector machine Mean absolute percentage error Meteorology. Climatology natural and anthropogenic factors Statistics RF QC851-999 karst NDVI RBFNN Mathematics |
| Zdroj: | Atmosphere, Vol 12, Iss 1341, p 1341 (2021) Atmosphere Volume 12 Issue 10 |
| ISSN: | 2073-4433 |
| DOI: | 10.3390/atmos12101341 |
| Popis: | 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 (R2), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE) of the obtained results were calculated, and the mean R2 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. |
| Databáze: | OpenAIRE |
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