Five machine learning (ML) algorithms were employed for gap-filling surface fluxes of CO₂, water vapor, and sensible heat above three different ecosystems: grassland, rice paddy field, and forest. The performance and limitations of these ML models, which are support vector machine, random forest, multi-layer perception, deep neural network, and long short-term memory, were investigated. Firstly, the accuracy of gap-filling to time and hysteresis input factors of ML algorithms for different ecosystems is discussed. Secondly, the optimal ML model selected in the first stage is compared with the classic method—the Penman–Monteith (P–M) equation for water vapor flux gap-filling. Thirdly, with different gap lengths (from one hour to one week), we explored the data length required for an ML model to perform the optimal gap-filling. Our results demonstrate the following: (1) for ecosystems with a strong hysteresis between surface fluxes and net radiation, adding proceeding meteorological data into the model inputs could improve the model performance; (2) the five ML models gave similar gap-filling performance; (3) for gap-filling water vapor flux, the ML model is better than the P–M equation; and (4) for a gap with length of half day, one day, or one week, an ML model with training data length greater than 1300 h would provide a better gap-filling accuracy.

Accurate real-time forecasts of inundation depth and area during typhoon flooding is crucial to disaster emergency response. The development of an inundation forecasting model has been recognized as essential to manage disaster risk. In the past, most researchers used multiple single-point forecasts to obtain surface flooding depth forecasts with spatial interpolation. In this study, a forecasting model (QPF-RIF) integrating a hydrodynamic model (SOBEK), support vector machine–multi-step forecast (SVM-MSF), and a self-organizing map (SOM) were proposed. The task of this model was divided into four parts: hydrodynamic simulation, point forecasting, inundation database clustering, and spatial expansion. First, the SOBEK model was used in simulating inundation hydrodynamics to construct the flooding maps database. Second, the SVM-MSF yields water level (inundation volume) forecasted with a 1 to 72 h lead time. Third, the SOM clustered the previous flooding maps database into several groups representing different flooding characteristics. Finally, a spatial expansion module produced inundation maps based on forecasting information from forecasting flood volume and flood causative factors. To demonstrate the effectiveness of the proposed forecasting model, we presented an application to the Yilan River basin in Taiwan. Our forecasting results indicated that the proposed model yields accurate flood inundation maps (less than 1 cm error) for a 1 h lead time. For long-term forecasting (46 h to 72 h ahead), the model controlled the error of the forecast results within 7 cm. In the testing events, the model forecasted an average of 83% of the flooding area in the long term. This flood inundation forecasting model is expected to be useful in providing early flood warning information for disaster emergency response.

This study conducted field measurements to explore the effects of street spatial structure on micrometeorological condition and air quality on both hot and cool days in Taipei City. Six street canyons with an aspect ratio of one, but varied in street orientation, street width, sky view factor, and number of planting strips, were selected for observations. In this case study, it was observed that, as well as the meso-scale phenomena, the local and micro-scale (street canyon structure) had influences on street air temperature, mean radiant temperature, and wind velocity. However, the local and micro-scales only had minor effects on relative humidity for both hot and cool days. Shade effect on temperature by street trees was observed; this effect could cause 2 degrees drop on mean radiant temperature and about 0.5 degree drop on air temperature. Our analyses and field measurements also revealed that, in some cases, concentrations of PM_2.5 and PM₁₀ were found to be correlated with local street canopy structure; however, in some cases, the meso-scale process was found to be the dominant factor. We also found that concentrations of CO and O₃ were inversely correlated in the street canyon. The findings of this study provide introductory scientific data and guidelines for urban street designers to improve thermal comfort and air quality.