ณัฏฐ์ ลีละวัฒน์ – Industrial Engineering Chula

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รศ. ดร.ณัฏฐ์ ลีละวัฒน์ รศ. ดร.ณัฏฐ์ ลีละวัฒน์

Overview

Dr. Natt Leelawat is an Associate Professor in Industrial Engineering at the Faculty of Engineering, Chulalongkorn University. He is also the Assistant Dean (International Affairs) of the Faculty of Engineering and the Head of the Center of Excellence in Disaster and Risk Management Information Systems (DRMIS). He is also a lecturer in the M.Sc. Program in Risk and Disaster Management and the M.Sc. Program in Innovative Engineering for Sustainability.

Dr. Leelawat is a Guest Associate Professor (Global) at the Graduate School of System Design and Management, Keio University, Japan. Previously, Dr. Leelawat has held various positions in academia and industry, including Director of the Risk and Disaster Management Program at the Graduate School of Chulalongkorn University, Assistant Professor at Tohoku University, Japan; Research Assistant at Tokyo Institute of Technology, Japan; and System Analyst at the Bank of Thailand. He is a member of several professional organizations, including the Association for Computing Machinery, the Association for Information Systems, the Business Continuity Institute (MBCI), IEEE (Senior Member and serving as Chair of IEEE R10 SAC), the Asia Oceania Geosciences Society (serving as Regional Advisory Committee and IG Session Secretary), the Earthquake Research Center of Thailand, and Thailand Network for Disaster Resilience.

Dr. Leelawat has received numerous awards and honors for his academic contributions. He was awarded the SakIntania’s Young Lecturer Award in 2018, the Tokyo Tech Alumni Association (Thailand Chapter)’s Young Outstanding Alumni Award in 2019, and the 2022 Samsenwittayalai Outstanding Alumni Award. He was also selected by Her Royal Highness Princess Maha Chakri Sirindhorn to be a representative from Thailand to join the 6th Global Young Scientists Summit in 2018. Dr. Leelawat has received Best Paper Awards from the 6th International Conference on Sustainable Future for Human Security in 2015, the 2nd International Symposium on Instrumentation, Control, Artificial Intelligence, and Robotics in 2021, the 40th Conference of Industrial Engineering Network in 2022, the 23rd International Symposium on Communications and Information Technologies in 2024, respective. Moreover, he and his team won the JDR Award for the Most Cited Paper in 2024.

His research areas include Disaster and Risk Management, Business Continuity Management, Management Information Systems, etc.

Personal Website : https://sites.google.com/site/nattleelawat

Education

D.Eng. Industrial Engineering and Management
Tokyo Institute of Technology, Japan, 2016

M.Eng. Industrial Engineering and Management
Tokyo Institute of Technology, Japan, 2013

B.Sc. (First Class Honours) Information Technology
Sirindhorn International Institute of Technology, Thammasat University, Thailand, 2007

Expertise

Information Systems

Strategic & Risk Management

Statistics & Data Analysis

Publications

117 entries « ‹ 12 of 12 › »

2015

Natt Leelawat, Anawat Suppasri, Ingrid Charvet, Takayuki Kimura, Daisuke Sugawara, Fumihiko Imamura

A study on influential factors on building damage in Kesennuma, Japan from the 2011 great East Japan Tsunami Journal Article

In: Engineering Journal, vol. 19, no. 3, pp. 105 – 115, 2015, (Cited by: 7; All Open Access, Bronze Open Access, Green Open Access).

Links | BibTeX

Natt Leelawat, Anawat Suppasri, Fumihiko Imamura

Disaster Recovery and Reconstruction Following the 2011 Great East Japan Earthquake and Tsunami: A Business Process Management Perspective Journal Article

In: International Journal of Disaster Risk Science, vol. 6, no. 3, pp. 310 – 314, 2015, (Cited by: 17; All Open Access, Gold Open Access).

Links | BibTeX

2014

Natt Leelawat, Anawat Suppasri, Ingrid Charvet, Fumihiko Imamura

Building damage from the 2011 Great East Japan tsunami: Quantitative assessment of influential factors: A new perspective on building damage analysis Journal Article

In: Natural Hazards, vol. 73, no. 2, pp. 449 – 471, 2014, (Cited by: 42; All Open Access, Hybrid Gold Open Access).

Abstract | Links | BibTeX

@article{Leelawat2014449,

title = {Building damage from the 2011 Great East Japan tsunami: Quantitative assessment of influential factors: A new perspective on building damage analysis},

author = {Natt Leelawat and Anawat Suppasri and Ingrid Charvet and Fumihiko Imamura},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84905381788&doi=10.1007%2fs11069-014-1081-z&partnerID=40&md5=60fb4a06b3c6255fd201b2aaccdb13c8},

doi = {10.1007/s11069-014-1081-z},

year  = {2014},

date = {2014-01-01},

journal = {Natural Hazards},

volume = {73},

number = {2},

pages = {449 – 471},

publisher = {Kluwer Academic Publishers},

abstract = {Based on the classification provided by the Ministry of Land, Infrastructure, Transport and Tourism (MLIT), the damage level of buildings impacted by the 2011 Great East Japan tsunami can be separated into six levels (from minor damage to washed away). The objective of this paper is to identify the significant predictor variables and the direction of their potential relationship to the damage level in order to create a predicting formula for damage level. This study used the detailed data of damaged buildings in Ishinomaki city, Miyagi prefecture, Japan, collected by MLIT. The explanatory variables tested included the inundation depth, number of floors, structural material, and function of the building. Ordinal regression was applied to model the relationship between the ordinal outcome variable (damage level) and the predictors. The findings indicated that inundation depth, structural material, and function of building were significantly associated with the damage level. In addition to this new type of model, this research provides a valuable insight into the relative influence of different factors on building damage and suggestions that may help to revise the classification of current standards. This study can contribute to academic tsunami research by assessing the contribution of different variables to the observed damage using new approaches based on statistical analysis and regression. Moreover, practical applications of these results include understanding of the predominant factors driving tsunami damage to structures, implementation of the relevant variables into the proposed, or alternative model in order to improve current damage predictions by taking into account not only inundation depth, but also variables such as structural material and function of building. © 2014 The Author(s).},

note = {Cited by: 42; All Open Access, Hybrid Gold Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}