@article{Saengtabtim2026,

title = {TTDI-based longitudinal assessment of COVID-19 disruptions in Thailand and Southeast Asian tourism},

author = {Kumpol Saengtabtim and Natt Leelawat and Jing Tang and Anawat Suppasri},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105030929204&doi=10.1016%2fj.rineng.2026.109595&partnerID=40&md5=1677118ae2a0b66c123f3457bde59b56},

doi = {10.1016/j.rineng.2026.109595},

year  = {2026},

date = {2026-01-01},

journal = {Results in Engineering},

volume = {29},

note = {Cited by: 0; All Open Access, Gold Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Saengtabtim2025651,

title = {Harnessing generative AI for enhanced disaster management: a systematic review},

author = {Kumpol Saengtabtim and Natt Leelawat and Rujapa Aumnoysombat and Mahsa Adelifar and Natcha Saengwongwattana and Gritaya Suktavornprasit and Anawat Suppasri and Fumihiko Imamura and Jing Tang},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105009402638&doi=10.1080%2f20964471.2025.2521157&partnerID=40&md5=7500c1038f929a77445bccd4d9f5abfe},

doi = {10.1080/20964471.2025.2521157},

year  = {2025},

date = {2025-01-01},

journal = {Big Earth Data},

volume = {9},

number = {4},

pages = {651 – 675},

note = {Cited by: 5; All Open Access, Gold Open Access, Green Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Yudha2025,

title = {A systematic review and bibliometric analysis of the impacts of COVID-19 on economy and mobility from the geospatial data perspective},

author = {Alfan Kurnia Yudha and Natt Leelawat and Jing Tang},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105005182205&doi=10.1016%2fj.rineng.2025.105282&partnerID=40&md5=b0da3f68fe3fad4c74a18654be3ac488},

doi = {10.1016/j.rineng.2025.105282},

year  = {2025},

date = {2025-01-01},

journal = {Results in Engineering},

volume = {26},

publisher = {Elsevier B.V.},

abstract = {The COVID-19 pandemic has greatly impacted the global economy, human health, and daily life. The World Health Organization declared it a pandemic on March 11, 2020. By May 2023, it had caused over seven million deaths. Until now, its economic and social effects are still felt. This systematic review and bibliometric analysis focuses on how COVID-19 has affected the economy and mobility using geospatial data. Geospatial data from sensors, social media, mobile apps, cars, and remote sensing give us near-real-time insights into people's behaviors and perceptions during the pandemic. The study examines how COVID-19 spread over time and space to help understand and reduce its impact. Even with challenges in combining different datasets, spatial analysis shows patterns of how humans and the pandemic interact. The finding answers key questions: How did COVID-19 affect economic activities and mobility? What common patterns do geospatial data show during the pandemic? By identifying common geospatial datasets and analyzing research trends, the study provides insights for policymakers and researchers to better prepare for future pandemics. This review helps understand the complex systems of pandemics and their effects on society using geospatial big data. Notably, the findings show that nighttime light intensity and mobile phone mobility data were the most consistently used indicators to monitor pandemic-driven disruptions and recovery, captured shifts in behavior and compliance with public health measures, offering a critical data source for real-time health surveillance, enabling health experts to better understand population responses, and adaptive policy interventions. © 2025},

note = {Cited by: 2; All Open Access, Gold Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Laosunthara2025,

title = {Initial observations and immediate lessons learned from Thailand's response to the 2025 Mandalay earthquake},

author = {Ampan Laosunthara and Kodchakorn Krutphong and Natt Leelawat and Wongsa Wararuksajja and Naruethep Sukulthanasorn and Anawat Suppasri and Ratchaneekorn Thongthip and Chatpan Chintanapakdee},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105010414313&doi=10.1016%2fj.ijdrr.2025.105675&partnerID=40&md5=0fc5e58eef5451c6ec4fbe6f61886754},

doi = {10.1016/j.ijdrr.2025.105675},

year  = {2025},

date = {2025-01-01},

journal = {International Journal of Disaster Risk Reduction},

volume = {127},

publisher = {Elsevier Ltd},

abstract = {On March 28, 2025, a magnitude 7.7 earthquake struck central Myanmar, with tremors widely felt across Thailand. Bangkok, in particular, experienced severe disruption due to long-period ground motion (LPGM), highlighting the city's vulnerability to distant seismic events. This earthquake represents the most wide-reaching seismic disruption to affect Thailand since the 2004 Aceh Tsunami. This study presents preliminary observations of Thailand's response during the critical first 72 h, focusing on structural damage, emergency coordination, evacuation challenges, and public risk communication. In Bangkok, the collapse of a 30-story construction site resulted in 19 fatalities and 78 missing persons. Vulnerable groups, including people with limited mobility, faced heightened risks due to inaccessible infrastructure. Hospitals struggled to maintain operations while evacuating patients, and misinformation on social media intensified public confusion. This research identifies key policy implications: enhancing building standards to address non-structural elements, institutionalizing regular evacuation drills for high-rise buildings, and accelerating the deployment of nationwide cell broadcast alert systems. The 2025 Mandalay earthquake revealed that seismic events beyond national borders can cascade into multi-dimensional urban crises. The findings underscore the urgency of integrated risk governance frameworks and strengthened regional collaboration across Southeast Asia to prepare for and mitigate cross-border disaster impacts. © 2025 Elsevier Ltd},

note = {Cited by: 3},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Laosunthara202587,

title = {Twitter-based Disaster Situation Analysis of the 2023 Turkey-Syria Earthquake},

author = {Ampan Laosunthara and Kumpol Saengtabtim and Takumi Ohashi and Jing Tang and Natt Leelawat},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105026382519&doi=10.4186%2fej.2025.29.9.87&partnerID=40&md5=a3a14808cd971792ea03854940f25ce5},

doi = {10.4186/ej.2025.29.9.87},

year  = {2025},

date = {2025-01-01},

journal = {Engineering Journal},

volume = {29},

number = {9},

pages = {87 – 96},

note = {Cited by: 0},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Chaudhary2024196969,

title = {Proximal Policy Optimization for Crowd Evacuation in Complex Environments - A Metaverse Approach at Krung Thep Aphiwat Central Terminal, Thailand},

author = {Sushank Chaudhary and Nitinun Sinpan and Pruk Sasithong and Sunita Khichar and Panithan La-Aiddee and Natt Leelawat and Amir Parnianifard and Suvit Poomrittigul and Lunchakorn Wuttisittikulkij},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211992072&doi=10.1109%2fACCESS.2024.3515153&partnerID=40&md5=1136034d905fd93d139dc7dcb4af0b0b},

doi = {10.1109/ACCESS.2024.3515153},

year  = {2024},

date = {2024-01-01},

journal = {IEEE Access},

volume = {12},

pages = {196969 – 196983},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {Efficient crowd evacuation from railway platforms is critical for passenger safety during emergencies. This study introduces a novel dynamic emergency evacuation route generator using the Proximal Policy Optimization (PPO) algorithm within a custom-built 3D simulation environment developed in Unity. We independently created a detailed digital twin of Krung Thep Aphiwat Central Terminal, Thailand's largest train station, and implemented all elements of the simulation, including the Social Force Model, to accurately replicate crowd behaviors and interactions during evacuation scenarios. Through extensive training over 3,000,000 episodes, our PPO-based model achieved significant improvements in evacuation efficiency. The results indicate that in a major emergency scenario, increasing the number of agents in the station reduced the number of remaining passengers from 111 to just 6, highlighting the model's effectiveness. Similarly, in a minor emergency scenario, the average number of remaining passengers dropped from 38 to 1 with the addition of more agents. These findings confirm the model's ability to adapt to different emergency conditions, offering a practical and scalable solution for enhancing evacuation strategies in high-density environments. Furthermore, increasing the agents' sight range also improved evacuation efficiency, with a 20-meter sight range yielding the best results. © 2013 IEEE.},

note = {Cited by: 7; All Open Access, Gold Open Access, Green Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Tang2024,

title = {Toward a more resilient Thailand: Developing a machine learning-powered forest fire warning system},

author = {Jing Tang and Manapat Weeramongkolkul and Supanida Suwankesawong and Kumpol Saengtabtim and Natt Leelawat and Kritchart Wongwailikhit},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197488130&doi=10.1016%2fj.heliyon.2024.e34021&partnerID=40&md5=12d1d4ba8bc243caa9519b05c8dda86b},

doi = {10.1016/j.heliyon.2024.e34021},

year  = {2024},

date = {2024-01-01},

journal = {Heliyon},

volume = {10},

number = {13},

publisher = {Elsevier Ltd},

abstract = {Forest fires in Thailand are a recurring and formidable challenge, inflicting widespread damage and ranking among the nation's most devastating natural disasters. Most detection methods are labor-intensive, lack speed for early detection, or result in high infrastructure costs. An essential approach to mitigating this issue involves establishing an efficient forest fire warning system based on amalgamating diverse available data sources and optimized algorithms. This research endeavors to develop a binary machine-learning classifier based on Thailand's forest fire occurrences from January 2019 to October 2022 using data acquired from satellite resources, including the Google Earth engine. We use four gas variables including carbon monoxide, sulfur dioxide, nitrogen dioxide, and ozone. The study explores a range of classification models, encompassing linear classifiers, gradient-boosting classifiers, and artificial neural networks. The XGBoost model is the top-performing option across various classification evaluation metrics. The model provides the accuracy of 99.6 % and ROC-AUC score of 0.939. These findings underscore the necessity for a comprehensive forest fire warning system that integrates gas measurement sensor devices and geospatial data. A feedback mechanism is also imperative to enable model retraining post-deployment, thereby diminishing reliance on geospatial attributes. Moreover, given that decision-tree-based algorithms consistently yield superior results, future research in machine learning for forest fire prediction should prioritize these approaches. © 2024 The Authors},

note = {Cited by: 8; All Open Access, Gold Open Access, Green Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}