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177 results on '"Park, Hyuck-Jin"'

17. Uncertainty Reduction in Flood Susceptibility Mapping Using Random Forest and eXtreme Gradient Boosting Algorithms in Two Tropical Desert Cities, Shibam and Marib, Yemen.

Author

Al-Aizari, Ali R., Alzahrani, Hassan, AlThuwaynee, Omar F., Al-Masnay, Yousef A., Ullah, Kashif, Park, Hyuck-Jin, Al-Areeq, Nabil M., Rahman, Mahfuzur, Hazaea, Bashar Y., and Liu, Xingpeng

Subjects
BOOSTING algorithms, CITIES & towns, FLOOD warning systems, RANDOM forest algorithms, RECEIVER operating characteristic curves, NATURAL disasters, FLOODS
Abstract

Flooding is a natural disaster that coexists with human beings and causes severe loss of life and property worldwide. Although numerous studies for flood susceptibility modelling have been introduced, a notable gap has been the overlooked or reduced consideration of the uncertainty in the accuracy of the produced maps. Challenges such as limited data, uncertainty due to confidence bounds, and the overfitting problem are critical areas for improving accurate models. We focus on the uncertainty in susceptibility mapping, mainly when there is a significant variation in the predictive relevance of the predictor factors. It is also noted that the receiver operating characteristic (ROC) curve may not accurately depict the sensitivity of the resulting susceptibility map to overfitting. Therefore, reducing the overfitting problem was targeted to increase accuracy and improve processing time in flood prediction. This study created a spatial repository to test the models, containing data from historical flooding and twelve topographic and geo-environmental flood conditioning variables. Then, we applied random forest (RF) and extreme gradient boosting (XGB) algorithms to map flood susceptibility, incorporating a variable drop-off in the empirical loop function. The results showed that the drop-off loop function was a crucial method to resolve the model uncertainty associated with the conditioning factors of the susceptibility modelling and methods. The results showed that approximately 8.42% to 9.89% of Marib City and 9.93% to 15.69% of Shibam City areas were highly vulnerable to floods. Furthermore, this study significantly contributes to worldwide endeavors focused on reducing the hazards linked to natural disasters. The approaches used in this study can offer valuable insights and strategies for reducing natural disaster risks, particularly in Yemen. [ABSTRACT FROM AUTHOR]

Published
2024
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23. Spatial identification and temporal prediction of air pollution sources using conditional bivariate probability function and time series signature

Author

Althuwaynee, Omar F., Pokharel, Badal, Aydda, Ali, Balogun, Abdul-Lateef, Kim, Sang-Wan, and Park, Hyuck-Jin

Abstract

Accurate identification of distant, large, and frequent sources of emission in cities is a complex procedure due to the presence of large-sized pollutants and the existence of many land use types. This study aims to simplify and optimize the visualization mechanism of long time-series of air pollution data, particularly for urban areas, which is naturally correlated in time and spatially complicated to analyze. Also, we elaborate different sources of pollution that were hitherto undetectable using ordinary plot models by leveraging recent advances in ensemble statistical approaches. The high performing conditional bivariate probability function (CBPF) and time-series signature were integrated within the R programming environment to facilitate the study’s analysis. Hourly air pollution data for the period between 2007 to 2016 is collected using four air quality stations, (ca0016, ca0058, ca0054, and ca0025), situated in highly urbanized locations that are characterized by complex land use and high pollution emitting activities. A conditional bivariate probability function (CBPF) was used to analyze the data, utilizing pollutant concentration values such as Sulfur dioxide (SO2), Nitrogen oxides (NO2), Carbon monoxide (CO) and Particulate Matter (PM10) as a third variable plotted on the radial axis, with wind direction and wind speed variables. Generalized linear model (GLM) and sensitivity analysis are applied to verify and visualize the relationship between Air Pollution Index (API) of PM10 and other significant pollutants of GML outputs based on quantile values. To address potential future challenges, we forecast 3 months PM10 values using a Time Series Signature statistical algorithm with time functions and validated the outcome in the 4 stations. Analysis of results reveals that sources emitting PM10 have similar activities producing other pollutants (SO2, CO, and NO2). Therefore, these pollutants can be detected by cross selection between the pollution sources in the affected city. The directional results of CBPF plot indicate that ca0058 and ca0054 enable easier detection of pollutants’ sources in comparison to ca0016 and ca0025 due to being located on the edge of industrial areas. This study’s CBPF technique and time series signature analysis’ outcomes are promising, successfully elaborating different sources of pollution that were hitherto undetectable using ordinary plot models and thus contribute to existing air quality assessment and enhancement mechanisms.

Published
2024
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32. DEWS: a QGIS tool pack for the automatic selection of reference rain gauges for landslide-triggering rainfall thresholds

Author

Althuwaynee, Omar F., primary, Melillo, Massimo, additional, Gariano, Stefano Luigi, additional, Lambardo, Luigi, additional, Park, Hyuck-Jin, additional, Kim, Sang-Wan, additional, Hader, Paulo, additional, Mohajane, Meriame, additional, Quevedo, Renata Pacheco, additional, Catani, Filippo, additional, and Aydda, Ali, additional

Published
2022
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