Rapid urbanization and increased human activity have negatively impacted the microclimate of cities, leading to unfavorable conditions for human thermal comfort, particularly in outdoor spaces. Thermal comfort can be improved through various means, such as adjusting the height of urban buildings, the aspect ratio of road widths, and the placement of street trees. This study employed the ENVI-met software V5.5.1 to simulate the microclimate based on aspect ratio (H/W = 1.5) and street tree spacing (6 m) similar to actual conditions with different aspect ratios (H/W = 0.5, 1.0, and 2.0) and street tree spacing (2 m) in Seoul, Republic of Korea. Thermal comfort was assessed through a comprehensive predicted mean vote (PMV) evaluation, considering the gender (male and female) and age (8, 35, and 80 years) of residents in the target area, to determine the optimal urban canyon scenario. The results of the study indicated that the height of the building and the percentage of trees had a significant impact on the temperature and PMV results. When comparing PMV results, women have higher thermal vulnerability than men, and based on age, older adults have higher thermal vulnerability. The aspect ratio of 1.5 and tree spacing of 2 m resulted in the lowest temperature of 35.91 °C at 12:00 p.m. at 0° wind direction and 36.09 °C at 90° wind direction, lower than the actual input value of 36.9 °C. The PMV values were also under the same conditions, with an average PMV by gender of 3.87 at 0° and 4.21 at 90° and an average PMV by age of 3.86 at 0° and 4.19 at 90°. This finding is significant because it can inform the development of planned cities that prioritize urban thermal comfort during summer. This can be achieved through the strategic design of urban canyons and incorporation of street trees in both new and existing cities.

In this study, we propose a novel approach for precipitation measurement based on rainfall acoustics, utilizing an effective rainfall acoustic collection device with low-cost IoT sensors housed in waterproof enclosure. Here, rainfall acoustics refer to the sound generated when raindrops fall and collide with surfaces such as the ground or canopy. Even at the same rainfall intensity, depending on the medium with which raindrops collide, acoustics with different frequency characteristics may occur. In this research, an acoustic collection device, combining a Raspberry Pi and a condenser microphone, was inserted into a waterproof enclosure and deployed in a rainfall environment to collect rainfall acoustics. This approach not only controls the medium of rainfall acoustics but also effectively blocks ambient noise and water, ensuring consistent characteristics of rainfall acoustics regardless of the installation environment. The collected rainfall acoustics were segmented into 10-second intervals, and spectrograms in the frequency domain were extracted by applying Short-Time Fourier Transform for each segment. Finally, using the extracted spectrogram as input data, a rainfall intensity estimation model based on a convolutional neural network was developed and other precision rainfall observation instruments (e.g., PARSIVEL, Pluvio², etc.) were considered collectively for the validation of the developed rainfall intensity estimation model. Acoustic-based rainfall observation enables the establishment of a dense observation network using low-cost devices. Leveraging the high temporal resolution of acoustic data, extremely short observation periods for rainfall can be achieved. This methodology presents an opportunity for cost-effective and high-spatiotemporal-resolution rainfall observation, overcoming the limitations of traditional methods.Keywords: Acoustic Sensing, Rainfall Acoustics, Precipitation, Convolutional Neural NetworkAcknowledgementThis work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. RS-2023-00250239) and in part by the Korea Meteorological Administration Research and Development Program under Grant RS-2023-00243008.This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No.NRF-2022R1A4A3032838).

The crisis in cities with high vulnerability are interlinked and cannot be addressed in isolation. The consideration of integrated approach to disaster management is essential to urban for resilience. This study aims to investigate the role of urban forests on reducing any potential risks from compound disasters in urban areas, represented by floods, air pollution, and human thermal comfort. At first, types of urban forests are investigated in a perspective of nature-based solutions (NbS) with various design criteria appropriate for urban rivers, potentially threatened by river flooding. Here, different kinds of scenarios are considered to figure out optimal design conditions as one of mitigating measures for individual disaster in urban areas adjacent to the river. From comparative analysis on hydrodynamic modelling with microclimate analysis and prediction, this study also evaluates main differences of changes in critical indicators for compound urban crises, such as water level, runoff, PM10, predicted mean vote (PMV), etc. Finally, it suggests how to maximize major functions of urban forests in the multi-criteria decision making context, towards a greener and more resilient city.AcknowledgementThis work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (2020R1G1A1013624) and in part by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. NRF-2022R1A4A3032838).

Recently, the frequency and severity of droughts have gradually increased due to extreme weather events and global warming. As the demand for drought management increases, field surveys and water supply are actively conducted in many countries. Given that such drought assessment and support require the consumption of labor and financial resources, the prioritization of essential agricultural areas has become a major topic for efficient decision-making in drought relief. In this study, we proposed a Principal Component Analysis (PCA) for selecting rural specialization districts across the 162 administrative regions of South Korea. Additionally, we aimed to investigate real cases of agricultural drought occurred in these regions by utilizing the survey of water supply measures derived from Ministry of Agriculture, Food and Rural Affairs. The research data comprised seven agricultural specialization factors, exemplified by agricultural workforce and infrastructure. First, we implemented singular decomposition method included in PCA process to represent the comprehensive trends of the agricultural specialization factors with maximum reflection. High value of principal component scores (PCS) estimated from PCA was interpreted as regions with high agricultural relevance. Lastly, the PCS were classified into different levels, defining top-ranking regions as rural specialization districts. Based on agricultural drought case studies from 2018 to 2021, it is expected that finding relative damage-prone areas and establishing appropriate drought responses will be feasible. Keywords: Principal Component Analysis, Rural Specialization Districts, Agricultural Specialization Factors, Principal Components Score Acknowledgement This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. RS-2023-00250239) and this research was supported by Korea Environment Industry & Technology Institute (KEITI) through Water Management Innovation Program for Drought (RS-2022-KE002032) funded by Korea Ministry of Environment. This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No.NRF-2022R1A4A3032838).