Nevin Manimala Statistics

Prediction and analysis of atmospheric visibility in five terrain types with artificial intelligence

Heliyon. 2023 Aug 19;9(8):e19281. doi: 10.1016/j.heliyon.2023.e19281. eCollection 2023 Aug.


Scattering visiometers are widely used to measure atmospheric visibility; however, visibility is difficult to measure accurately because the extinction coefficient decays exponentially with visual range according to the Koschmid’s law. Moreover, models for predicting visibility are lacking due to the lack of accurate visibility observations to verify. This study formulated an artificial intelligence method for measuring atmospheric visibility in five topographical regions: hills, basins, plains, alluvial plains, and rift valleys. Four air pollution factors and five meteorological factors were selected as independent variables for predicting visibility by using three artificial intelligence models, namely a support vector machine (SVM) model, a multilayer perceptron (MLP) model, and an extreme gradient boosting (XGBoost) model. The GridSearchCV function was used to automatically tune model hyperparameters to determine the optimal parameter values of the three models for the five target areas. The predictions of the aforementioned three models underwent considerable considerably scale shrinking relative to observed values. The inappropriately low predicted visibility values might have been caused by the use of inaccurate observations for training. To solve this problem, formulas of scale ratio and downshift were used to adjust the predicted values. Statistical measurements of model performance measures by five quantitative methods (e.g., correlation coefficient, mean absolute error) showed that adjusted predictions were in strong agreement with the observation data for the five target areas. Therefore, the adjusted prediction has high reliability. Because of obvious differences in the topography, weather, and air quality of the five target areas, different models provided optimal predictions for different areas. In densely populated western Taiwan, the MLP model is most suitable for predicting visibility on hills whereas the XGBoost model is most suitable for predicting visibility on basins and plains. In eastern Taiwan, the SVM model is most suitable for predicting visibility on alluvial plains and rift valleys. Thus, the optimal prediction model should be identified according to the conditions in each area. These results can inform decision-making processes or improve visibility predicting in specific areas.

PMID:37664727 | PMC:PMC10469964 | DOI:10.1016/j.heliyon.2023.e19281

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