Damage Prediction Using Heavy Rain Risk Assessment: (1) Estimation of Heavy Rain Damage Risk Index

2017 ◽  
Vol 17 (2) ◽  
pp. 361-370 ◽  
Author(s):  
Jongsung Kim ◽  
◽  
Changhyun Choi ◽  
Jongso Lee ◽  
Hung Soo Kim ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Risk Index ◽  
Heavy Rain ◽  
Damage Prediction ◽  
Damage Risk

scholarly journals Reliability of Existing Climate Indices in Assessing the Freeze-Thaw Damage Risk of Internally Insulated Masonry Walls

Buildings ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 482
Author(s):  
Sahar Sahyoun ◽  
Hua Ge ◽  
Michael A. Lacasse ◽  
Maurice Defo
Keyword(s):  
Risk Assessment ◽  
Risk Index ◽  
Climate Scenario ◽  
Evaluation Methodology ◽  
Climate Indices ◽  
Moisture Index ◽  
Climatic Index ◽  
Actual Performance ◽  
Freeze Thaw ◽  
Damage Risk

This paper evaluates the reliability of the currently used climate-based indices in selecting a moisture reference year (MRY) for the freeze-thaw (FT) damage risk assessment of internally insulated solid brick walls. The evaluation methodology compares the ranking of the years determined by the climate-based indices and response-based indices from simulations, regarded as actual performance. The hygrothermal response of an old brick masonry wall assembly, before and after retrofit, was investigated in two Canadian cities under historical and projected future climates. Results indicated that climate-based indices failed to represent the actual performance. However, among the response-based indices, the freeze-thaw damage risk index (FTDR) showed a better correlation with the climate-based indices. Additionally, results indicated a better correlation between the climatic index (CI), the moisture index (MI), and FTDR in Ottawa; however, in Vancouver, a better fit was found between MI and FTDR. Moreover, the risk of freeze-thaw increased considerably after interior insulation was added under both historical and projected future climates. The risk of FT damage would increase for Ottawa but decrease for Vancouver under a warming climate projected in the future, based on the climate scenario used in this study. Further research is needed to develop a more reliable method for the ranking and the selection of MRYs on the basis of climate-based indices that is suitable for freeze-thaw damage risk assessment.

scholarly journals Determining the Risk Level of Heavy Rain Damage by Region in South Korea

Water ◽  
2022 ◽  
Vol 14 (2) ◽  
pp. 219
Author(s):  
Jongsung Kim ◽  
Donghyun Kim ◽  
Myungjin Lee ◽  
Heechan Han ◽  
Hung Soo Kim
Keyword(s):  
Risk Assessment ◽  
South Korea ◽  
Risk Index ◽  
National Level ◽  
Heavy Rain ◽  
Quantitative Risk Assessment ◽  
Risk Level ◽  
Disaster Prevention ◽  
Qualitative Risk Assessment ◽  
Regional Risk

For risk assessment, two methods, quantitative risk assessment and qualitative risk assessment, are used. In this study, we identified the regional risk level for a disaster-prevention plan for an overall area at the national level using qualitative risk assessment. To overcome the limitations of previous studies, a heavy rain damage risk index (HDRI) was proposed by clarifying the framework and using the indicator selection principle. Using historical damage data, we also carried out hierarchical cluster analysis to identify the major damage types that were not considered in previous risk-assessment studies. The result of the risk-level analysis revealed that risk levels are relatively high in some cities in South Korea where heavy rain damage occurs frequently or is severe. Five causes of damage were derived from this study—A: landslides, B: river inundation, C: poor drainage in arable areas, D: rapid water velocity, and E: inundation in urban lowlands. Finally, a prevention project was proposed considering regional risk level and damage type in this study. Our results can be used when macroscopically planning mid- to long-term disaster prevention projects.

Damage Prediction Using Heavy Rain Risk Assessment: (2) Development of Heavy Rain Damage Prediction Function

2017 ◽  
Vol 17 (2) ◽  
pp. 371-379 ◽  
Author(s):  
Jongsung Kim ◽  
◽  
Changhyun Choi ◽  
Jongso Lee ◽  
Hung Soo Kim ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Heavy Rain ◽  
Damage Prediction ◽  
Prediction Function

Development of Heavy Rain Damage Risk Class and Prediction Function

2020 ◽  
Author(s):  
DongHyun Kim ◽  
Jongsung Kim ◽  
Hung Soo Kim
Keyword(s):  
Risk Assessment ◽  
Regression Analysis ◽  
Risk Index ◽  
Development Program ◽  
Principal Component ◽  
Heavy Rain ◽  
Prediction Performance ◽  
Ann Model ◽  
Risk Class ◽  
Prediction Function

<p>This study conducted risk assessment and risk classification on heavy rain damage in the region, then developed the prediction function for heavy rain damage by the risk class. That is to say, the risk index of heavy rain damage by using PSR and DPSIR models was developed for the risk assessment and the risk classes (Red Zone, Orange Zone, Yellow Zone, Green Zone) obtained according to the index. Multiple regression analysis, principal component regression analysis, and artificial neural network(ANN) were applied to develop the prediction function of heavy rain damage. In order to evaluate the prediction performance of the prediction function, we divided heavy rain damage data into the learning section from 2005 to 2012 and the evaluation section from 2013 to 2016. As the results, the ANN using the DPSIR model showed the best prediction performance which has NRMSE of 8.65%. Therefore, the ANN model using the DPSIR was selected as the prediction function in this study. If we can predict the heavy rain damage based on the prediction function, it could be very helpful for disaster preparedness and management.</p><div> <p>This research was supported by a grant(2018-MOIS31-009) from Fundamental Technology Development Program for Extreme Disaster Response funded by Korean Ministry of Interior and Safety(MOIS).</p> <div> </div> </div>

scholarly journals Environmental Risk Assessment of Petroleum Activities in Surface Sediments, Suez Gulf, Egypt

2021 ◽  
Vol 9 (5) ◽  
pp. 473
Author(s):  
Magda M. Abou El-Safa ◽  
Mohamed Gad ◽  
Ebrahem M. Eid ◽  
Ashwaq M. Alnemari ◽  
Mohammed H. Almarshadi ◽  
...  
Keyword(s):  
Heavy Metals ◽  
Risk Assessment ◽  
Heavy Metal ◽  
Surface Sediment ◽  
Surface Sediments ◽  
Surf Zone ◽  
Risk Index ◽  
Negative Relationship ◽  
Anthropogenic Sources ◽  
Suez Gulf

The present study focuses on the risk assessment of heavy metal contamination in aquatic ecosystems by evaluating the current situation of heavy metals in seven locations (North Amer El Bahry, Amer, Bakr, Ras Gharib, July Water Floud, Ras Shokeir, and El Marageen) along the Suez Gulf coast that are well-known representative sites for petroleum activities in Egypt. One hundred and forty-six samples of surface sediments were carefully collected from twenty-seven profiles in the intertidal and surf zone. The hydrochemical parameters, such as pH and salinity (S‰), were measured during sample collection. The mineralogy study was carried out by an X-ray diffractometer (XRD), and the concentrations of Al, Mn, Fe, Cr, Cu, Co, Zn, Cd, and Pb were determined using inductively coupled plasma mass spectra (ICP-MS). The ecological risks of heavy metals were assessed by applying the contamination factor (CF), enrichment factor (EF), geoaccumulation index (Igeo), pollution load index (PLI), and potential ecological risk index (RI). The mineralogical composition mainly comprised quartz, dolomites, calcite, and feldspars. The average concentrations of the detected heavy metals, in descending order, were Al > Fe > Mn > Cr > Pb > Cu > Zn > Ni > Co > Cd. A non-significant or negative relationship between the heavy metal concentration in the samples and their textural grain size characteristics was observed. The coastal surface sediment samples of the Suez Gulf contained lower concentrations of heavy metals than those published for other regions in the world with petroleum activities, except for Al, Mn, and Cr. The results for the CF, EF, and Igeo showed that Cd and Pb have severe enrichment in surface sediment and are derived from anthropogenic sources, while Al, Mn, Fe, Cr, Co, Ni, Cu, and Zn originate from natural sources. By comparison, the PLI and RI results indicate that the North Amer El Bahry and July Water Floud are considered polluted areas due to their petroleum activities. The continuous monitoring and assessment of pollutants in the Suez Gulf will aid in the protection of the environment and the sustainability of resources.

scholarly journals Reforestation Impacted Soil Heavy Metal Fractionation and Related Risk Assessment in the Karst Area, Southwest China

Forests ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 891
Author(s):  
Qian Zhang ◽  
Guilin Han ◽  
Xingliang Xu
Keyword(s):  
Heavy Metals ◽  
Risk Assessment ◽  
Ecological Risk ◽  
Southwest China ◽  
Risk Index ◽  
Contamination Factor ◽  
Karst Area ◽  
Risk Assessment Code ◽  
Bcr Sequential Extraction ◽  
Soil Heavy Metals

Human agricultural activities have resulted in widespread land degradation and soil contamination in the karst areas. However, the effects of reforestation after agricultural abandonment on the mobility risks and contamination of heavy metals have been rarely reported. In the present study, six soil profiles were selected from cropland and abandoned cropland with reforestation in the Puding karst regions of Southwest China. The Community Bureau of Reference (BCR) sequential extraction method was used to evaluate the compositions of different chemical fractions of soil heavy metals, including Fe, Mn, Cr, Zn, Ni, and Cd. The total contents of Cr, Ni, Zn, Cd, and Mn in the croplands were significantly higher than those in the abandoned croplands. For all soils, Cr, Ni, Zn, and Fe were mainly concentrated in the residual fractions (>85%), whereas Mn and Cd were mostly observed in the non-residual fractions (>65%). The non-residual fractions of Cd, Cr, Ni, and Zn in the croplands were higher than those in the abandoned croplands. These results indicated that the content and mobility of soil heavy metals decreased after reforestation. The individual contamination factor (ICF) and risk assessment code (RAC) showed that Cd contributed to considerable contamination of karst soils. The global contamination factor (GCF) and potential ecological risk index (RI) suggested low contamination and ecological risk of the investigated heavy metals in the croplands, moreover they can be further reduced after reforestation.

scholarly journals Speciation Variation and Comprehensive Risk Assessment of Metal(loid)s in Surface Sediments of Intertidal Zones

2018 ◽  
Vol 15 (10) ◽  
pp. 2125 ◽  
Author(s):  
Baocui Liang ◽  
Xiao Qian ◽  
Shitao Peng ◽  
Xinhui Liu ◽  
Lili Bai ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Surface Sediments ◽  
Yellow River ◽  
Biological Effects ◽  
Risk Index ◽  
Sediment Quality ◽  
Residual Fraction ◽  
Intertidal Zones ◽  
Probable Effect Level ◽  
Effect Level

Speciation variation and comprehensive risk assessment of metal(loid)s (As, Cd, Cr, Cu, Mn, Ni, Pb and Zn) were investigated in surface sediments from the intertidal zones of the Yellow River Delta, China. Results showed that only the concentrations of As, Cd and Pb were significantly different between April and September (p < 0.01). In April, the residual fraction (F4) was predominant for As, Cr, Cu, Ni and Zn. However, the exchangeable and carbonate-associated fraction (F1) was dominant for Cd averaging 49.14% indicating a high environmental risk. In September, the F4 fraction was predominant and the F1 fraction was very low for most metal(loid)s except Cd and Mn. The geo-accumulation index (Igeo), the F1 fraction and potential ecological risk index (PERI) of most metal(loid)s were relatively low in surface sediments for both seasons. But Pb, As and Ni were between the threshold effect level (TEL)and the probable effect level (PEL) for 66.67%, 83.33% and 91.67% in April and As and Ni were between TEL and PEL for 41.67% and 91.67%, which indicated that the concentration of them was likely to occasionally exhibit adverse effects on the ecosystem. Although the Igeo, the F1 fraction or PERI of Cd in both seasons was higher at some sites, the results of sediment quality guidelines (SQGs) indicated that the biological effects of Cd were rarely observed in the studied area.

scholarly journals Development of Heavy Rain Damage Prediction Model Using Machine Learning Based on Big Data

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Changhyun Choi ◽  
Jeonghwan Kim ◽  
Jongsung Kim ◽  
Donghyun Kim ◽  
Younghye Bae ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Prediction Model ◽  
Prediction Models ◽  
Meteorological Data ◽  
Heavy Rain ◽  
Machine Learning Techniques ◽  
Damage Prediction ◽  
Explanatory Variables ◽  
The Republic

Prediction models of heavy rain damage using machine learning based on big data were developed for the Seoul Capital Area in the Republic of Korea. We used data on the occurrence of heavy rain damage from 1994 to 2015 as dependent variables and weather big data as explanatory variables. The model was developed by applying machine learning techniques such as decision trees, bagging, random forests, and boosting. As a result of evaluating the prediction performance of each model, the AUC value of the boosting model using meteorological data from the past 1 to 4 days was the highest at 95.87% and was selected as the final model. By using the prediction model developed in this study to predict the occurrence of heavy rain damage for each administrative region, we can greatly reduce the damage through proactive disaster management.

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