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This study aimed to improve the uncertainty in spatial data of risk assessment through a Fuzzy inference system (FIS) as a way to conduct an environmental risk map of air pollution in Taiwan. In modeling, the feature inputs of FIS included the geographic coordinates and time, while the outputs are the pollutant concentrations. The outputs are supplements to the concentration contour on the map in comparison with Kriging interpolation. In our model, the FIS was designed using the official open data of air pollutants, including Pb and PM 2.5 that were collected from the monitoring stations in mid-southern Taiwan. The model involved data filtration and imputation in the preliminary scheme to extract the historical data for analysis. We used the data of Pb (2001–2013) and PM 2.5 (2006–2013) for the training process, and then used the data from 2014 to 2015 for validation. Our model was able to compute the smaller errors of inferred and measured values of Pb and PM 2.5 than the conventional method. The approach was applied to deduce the exposure of PM 2.5 distributed over the Taiwan Island in accordance with the governmental open data of seventy-three stations during 2006–2016 in order to produce our risk map. The designed model upon Fuzzy inference accesses potential risks of spatiotemporal exposures in the unmeasured locations with feasibility and adaptability for environmental management. • The study of environmental management needs reliable data of pollutant exposure location for risk assessment. • An inference model based on Fuzzy theory exports the risk map through GIS. • Spatiotemporal open data of PM 2.5 and Pb in mid-southern Taiwan are used for the modeling process. • The approach expands to infer the risk map of PM 2.5 on the main island of Taiwan for further application. • The model can quickly scan pollutant measurement to assess environmental exposure for possible risk. [ABSTRACT FROM AUTHOR]

Cost–benefit analysis is widely used to elucidate the association between foraging group size and resource size. Despite advances in the development of theoretical frameworks, however, the empirical systems used for testing are hindered by the vagaries of field surveys and incomplete data. This study developed the three approaches to data imputation based on machine learning (ML) algorithms with the aim of rescuing valuable field data. Using 163 host spider webs (132 complete data and 31 incomplete data), our results indicated that the data imputation based on random forest algorithm outperformed classification and regression trees, the k-nearest neighbor, and other conventional approaches (Wilcoxon signed-rank test and correlation difference have p-value from < 0.001–0.030). We then used rescued data based on a natural system involving kleptoparasitic spiders from Taiwan and Vietnam (Argyrodes miniaceus, Theridiidae) to test the occurrence and group size of kleptoparasites in natural populations. Our partial least-squares path modelling (PLS-PM) results demonstrated that the size of the host web (T = 6.890, p = 0.000) is a significant feature affecting group size. The resource size (T = 2.590, p = 0.010) and the microclimate (T = 3.230, p = 0.001) are significant features affecting the presence of kleptoparasites. The test of conformation of group size distribution to the ideal free distribution (IFD) model revealed that predictions pertaining to per-capita resource size were underestimated (bootstrap resampling mean slopes