Analysis of the association between environmental features and temperature using Decision Tree and Artificial Neural Network

Taiwan is located in a subtropical hot and humid climate, and there are many reasons for the Urban heat island(UHI) effect caused by the rising temperature in the city. For example, radiative heat, heat storage in buildings, and artificial heat dissipation affect microclimate changes to form a vicious cycle, which has a profound impact on climate and high-tempurature.

In this study, Taichung City was selected as the study area, and after excluding the high-altitude jurisdictions and suburban areas, the characteristic factors were introduced into the analysis software in each administrative area to obtain effective research results. Previous studies on the relationship between urban climatic characteristics and temperature have been conducted by monitoring networks, building model analysis, and environmental climate simulations, with emphasis on data collection and simulation. Because of the diversity and complexity of the environmental characteristic factors, and the difficulty of predicting the target by the past analysis,this study uses the data collected by detection instruments to analyze data in the form of Decision Trees, which can effectively reduce excessive information collection and enhance the research efficiency and reduce the cost of equipment construction. Among the environmental characteristics, the factors with higher correlation to the target values were selected for data standardization and then analyzed by Artificial Neural Network(ANN)-K Nearest Neighbor(KNN) to achieve the prediction of the target values, in order to meet the expected results of this study.

This study is dedicated to the analysis of the causes of UHI formation, and the analysis by Decision Tree research method reveals that the core area of Taichung City is affected by the surface factors of the high temperature area and the surrounding topographic conditions, resulting in microclimate hyperthermia. The results of the study showed that the percentages of the factors that affected the high temperature of the administrative area were as follows: Normalized Difference Vegetation Index (NVDI) was 24%, impervious area was 22%, building area density was 19%, and average building height and surface roughness were 16%. For the future application of the research results, an ANN model can be developed for the prediction of the selected high correlation factors, which can be used as a reference for the subsequent formulation of environmental regulations and the analysis of urban energy saving strategies.