Evaluation of nine machine learning methods for estimating daily land surface radiation budget from MODIS satellite data

Li, Shaopeng; Jiang, Bo; Liang, Shunlin; Peng, Jianghai; Liang, Hui; Han, Jing; Yin, Xiuwan; Yao, Yunjun; Zhang, Xiaolin; Cheng, Jie; Zhao, Xiang; Liu, Qiang; Jia, Kun

doi:10.1080/17538947.2022.2130460

Cited by 5 publications

(3 citation statements)

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“…After each weak learning cycle, it increases the weights of misclassified samples, forcing the weak learners to focus on challenging instances. Eventually, it amalgamates all weak learners through weighted aggregation to build a strong learner [35]. It is celebrated for its swift convergence and ease of implementation.…”

Section: Independent Variablesmentioning

confidence: 99%

“…It also introduces regularization terms to reduce model prediction variability and improve resilience against overfitting. XGBoost offers benefits like rapid computation, superior performance, easy parameter tuning, and efficient handling of large datasets [35]. LightGBM is another member of the boosting framework.…”

Section: Independent Variablesmentioning

confidence: 99%

“…This mitigates sensitivity to the quality of training samples and overfitting, thereby enhancing the accuracy and applicability in handling complex feature datasets and limited training samples. While gradient boosting algorithms such as XGBoost and LightGBM typically demonstrate exceptional predictive capabilities when handling high-dimensional and large-scale datasets [35,36], the relatively small sample size in this study may limit the models' ability to fully capture the underlying patterns and complex relationships between features, thus limiting its predictive accuracy.…”

Section: Effectiveness Of Rf Modelmentioning

confidence: 99%

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County-Level Poverty Evaluation Using Machine Learning, Nighttime Light, and Geospatial Data

Zheng,

Zhang,

Deng

et al. 2024

Remote Sensing

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The accurate and timely acquisition of poverty information within a specific region is crucial for formulating effective development policies. Nighttime light (NL) remote sensing data and geospatial information provide the means for conducting precise and timely evaluations of poverty levels. However, current assessment methods predominantly rely on NL data, and the potential of combining multi-source geospatial data for poverty identification remains underexplored. Therefore, we propose an approach that assesses poverty based on both NL and geospatial data using machine learning models. This study uses the multidimensional poverty index (MPI), derived from county-level statistical data with social, economic, and environmental dimensions, as an indicator to assess poverty levels. We extracted a total of 17 independent variables from NL and geospatial data. Machine learning models (random forest (RF), support vector machine (SVM), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM)) and traditional linear regression (LR) were used to model the relationship between the MPI and independent variables. The results indicate that the RF model achieved significantly higher accuracy, with a coefficient of determination (R2) of 0.928, a mean absolute error (MAE) of 0.030, and a root mean square error (RMSE) of 0.037. The top five most important variables comprise two (NL_MAX and NL_MIN) from the NL data and three (POI_Ed, POI_Me, and POI_Ca) from the geographical spatial data, highlighting the significant roles of NL data and geographical data in MPI modeling. The MPI map that was generated by the RF model depicted the detailed spatial distribution of poverty in Fujian province. This study presents an approach to county-level poverty evaluation that integrates NL and geospatial data using a machine learning model, which can contribute to a more reliable and efficient estimate of poverty.

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Section: Independent Variablesmentioning

confidence: 99%

Section: Independent Variablesmentioning

confidence: 99%

Section: Effectiveness Of Rf Modelmentioning

confidence: 99%

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