scholarly journals Conservation Design and Scenario for Flood Mitigation on Arui Watershed, Indonesia

2019 ◽  
Vol 51 (3) ◽  
pp. 261
Author(s):  
Mahmud Mahmud ◽  
Ambar Kusumandari ◽  
Sudarmadji Sudarmadji ◽  
Nunuk Supriyatno
Keyword(s):  
Natural Disaster ◽  
Current Velocity ◽  
Flood Mitigation ◽  
Tukey Test ◽  
Disaster Data ◽  
Flooding Disaster ◽  
River Current ◽  
Conservation Design

Flooding has been natural disaster in Indonesia and elsewhere. This research is designed to create scenarios and designs conservation to mitigate flooding disaster.  Data potential ,vulnerability, and duplicated river covering 0.25% of the targeted flooding area were collected and analysed. Five disain of conservation, natural river as control, river normalization, normalization with gabion stone, river straigtening, and straigtening with gabion stone, are proposed, and main targeted responses of these five scenarios are river current velocity. Effectiveness scenarios were analysed using Anova and Tukey test. The results showed that alignment with gabion stone was the most effective scenario for flooding mitigation since this was the most effective in increasing river current velocity. This could prevent riverbank occurrence of avalanche, accelerate river current, overcome flooding, and prevent future flooding. Other scenarios likes dead clicth ended-hallway, canalization, and riparian reclamation are also possible implemented.

scholarly journals Visualization System for Natural Disaster Data

2018 ◽  
Vol 24 (3) ◽  
pp. 21-31 ◽  
Author(s):  
kim jong yong ◽  
SANGHUN PARK ◽  
Joonyoung Cho ◽  
Dongwook Kim ◽  
정석철 ◽  
...  
Keyword(s):  
Natural Disaster ◽  
Visualization System ◽  
Disaster Data

Fatal Work Injuries Involving Natural Disasters, 1992–2006

2009 ◽  
Vol 3 (4) ◽  
pp. 201-209 ◽  
Author(s):  
Gregory M. Fayard
Keyword(s):  
Natural Disasters ◽  
Natural Disaster ◽  
Disaster Preparedness ◽  
Large Scale ◽  
Expert Knowledge ◽  
Occupational Injuries ◽  
Motor Vehicles ◽  
Disaster Data ◽  
The Government ◽  
Classification Structure

ABSTRACTObjective: Although a goal of disaster preparedness is to protect vulnerable populations from hazards, little research has explored the types of risks that workers face in their encounters with natural disasters. This study examines how workers are fatally injured in severe natural events.Methods: A classification structure was created that identified the physical component of the disaster that led to the death and the pursuit of the worker as it relates to the disaster. Data on natural disasters from the Census of Fatal Occupational Injuries for the years 1992 through 2006 were analyzed.Results: A total of 307 natural disaster deaths to workers were identified in 1992–2006. Most fatal occupational injuries were related to wildfires (80 fatalities), hurricanes (72 fatalities), and floods (62 fatalities). Compared with fatal occupational injuries in general, natural disaster fatalities involved more workers who were white and more workers who were working for the government. Most wildfire fatalities stemmed directly from exposure to fire and gases and occurred to those engaged in firefighting, whereas hurricane fatalities tended to occur more independently of disaster-produced hazards and to workers engaged in cleanup and reconstruction. Those deaths related to the 2005 hurricanes occurred a median of 36.5 days after landfall of the associated storm. Nearly half of the flood deaths occurred to passengers in motor vehicles. Other disasters included tornadoes (33 fatalities), landslides (17), avalanches (16), ice storms (14), and blizzards (9).Conclusions: Despite an increasing social emphasis on disaster preparation and response, there has been little increase in expert knowledge about how people actually perish in these large-scale events. Using a 2-way classification structure, this study identifies areas of emphasis in preventing occupational deaths from various natural disasters. (Disaster Med Public Health Preparedness. 2009;3:201–209)

scholarly journals Classifying Informatory Tweets during Disaster Using Deep Learning

2020 ◽  
Vol 32 ◽  
pp. 03025
Author(s):  
Pradip Bhere ◽  
Anand Upadhyay ◽  
Ketan Chaudhari ◽  
Tushar Ghorpade
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Natural Disaster ◽  
Feature Vector ◽  
Sound Image ◽  
Life Saving ◽  
Disaster Data ◽  
Educational Information

Micro blogging platforms like Twitter generate a wealth of information during a disaster. Data can be in the form of sound, image, text, video etc. by way of tweets. Tweets produced during a disaster are not always educational. Information tweets can provide useful information about affected people, infrastructure damage, civilized organizations etc. Studies show that when it comes to sharing emergency information during a natural disaster, time is everything. Research on Twitter use during hurricanes, floods and floods provide potentially life-saving data on how information is disseminated in emergencies. The proposed system outlines how to distinguish sensitive and non-useful tweets during a disaster. The proposed method is based on the use of Word2Vec and the Convolutional Neural Network (CNN). Word2vec provides a feature vector and CNN is used to classify tweets.

scholarly journals Pengelompokkan Data Bencana Alam Berdasarkan Wilayah, Waktu, Jumlah Korban dan Kerusakan Fasilitas Dengan Algoritma K-Means

2020 ◽  
Vol 4 (3) ◽  
pp. 744
Author(s):  
Murdiaty Murdiaty ◽  
Angela Angela ◽  
Chatrine Sylvia
Keyword(s):  
Data Mining ◽  
Natural Disasters ◽  
Natural Disaster ◽  
Data Clustering ◽  
Volcanic Eruptions ◽  
Marine Resources ◽  
Clustering Techniques ◽  
Data Mining Approach ◽  
Disaster Data ◽  
Property Losses

Indonesia has fertile soil, natural resources and abundant marine resources. However, Indonesia is also not immune to the risk of natural disasters which are a series of events that disturb and threaten life safety and cause material and non-material losses. Indonesia's strategic geological location causes Indonesia to be frequently hit by earthquakes, volcanic eruptions and other natural disasters. From the data collected, natural disasters that occurred in Indonesia consisted of several categories, namely earthquakes, volcanic eruptions, floods, landslides, tornados, and tsunamis. Many natural disasters in Indonesia have caused casualties, both fatalities and injuries, destroying the surrounding area and destroying infrastructure and causing property losses. The trend of increasing incidence of natural disasters needs to be further investigated to prevent the number of victims from increasing. This information can be obtained through a data mining approach given the large amount of data available. In relation to natural disaster data, clustering techniques in data mining are very useful for grouping natural disaster data based on the same characteristics so that the data can be adopted as a groundwork for predicting natural disaster events in the future. Thus, this research is supposed to group natural disaster data using clustering techniques using the k-means algorithm into several groups, in terms of natural disaster types, time of disaster, number of victims, and damage to various facilities as a result of natural disasters

scholarly journals A Study on the Cooperative Utilization of Disaster Databases and Statistical Databases Using GIS

2020 ◽  
Vol 20 (1) ◽  
pp. 373-381
Author(s):  
Gyeng-Bin Lee ◽  
Young-Kuu Kim ◽  
Jung-Hwan Yun ◽  
Jae-Nam Lee
Keyword(s):  
Natural Disaster ◽  
Disaster Management ◽  
Flood Management ◽  
Disaster Prevention ◽  
Prevention Measures ◽  
Disaster Data ◽  
Management Plans ◽  
The Status ◽  
Natural Disaster Management ◽  
The Given

Historical damage cases and data on damage statistics recorded in the disaster year do not take into account the status of storm and flood management and the ability to plan for the recovery of affected areas in establishing natural disaster prevention measures and restoration plans for the given areas; thus, this information is not used as basic data for practical responses to damages from storms and floods. In this regard, this study proposes a method for developing disaster integration information using GIS-based national statistical data and disaster data as a method of natural disaster management considering local statuses. It further suggests disaster management plans based on analyzing damage density and damage targets. It is expected that GIS-based disaster integration information constructed through the results of this study can be used to classify vulnerable areas and establish disaster prevention and recovery plans for vulnerable areas.

Analisa Data Bencana Alam Untuk Prediksi Dampak Yang Ditimbulkan Dengan Algoritma J48 (Studi Kasus : Palang Merah Indonesia)

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Dewi Shintya Lumbansiantar
Keyword(s):  
Data Mining ◽  
Natural Disasters ◽  
Natural Disaster ◽  
Volcanic Eruptions ◽  
Red Cross ◽  
Natural Event ◽  
Disaster Data ◽  
Mining Methods ◽  
Using Data ◽  
The Impact

Natural disaster is a natural event that is difficult to avoid and difficult to estimate the exact impact of natural disasters that can be fatalities, social environment, propety, losses, even distrubance to the community even though it is very likely to occur. As for the disasters that often occur in Indonesia including floods, landslides, tsunamis, earthquakes and volcanic eruptions. The lack of relief supplies provided by the Indonesian Red Cross (PMI) was caused by the absence of data on the need for assistance provided. Therefore it is necessary to analyze natural disaster data that has happened before to be used to predict the impact caused by natural disasters. Prediction of the amount of assistance needed can be done using data mining techniques, therefore this study amis to analyzenatural disaster data using data mining methods using the J48 algorithm. To analyze natural disastr data for prediction of the impact can be used by rapidminer testing so that the results can be in the form of a decision tree.Keywords: Data Mining, Natural Disaster Data, J48 Algorithm

THE USE OF INDIGENOUS KNOWLEDGE IN FLOOD DISASTER FORECASTING FOR FLOOD DISASTER RISK REDUCTION IN NORTHERN KATSINA STATE

2021 ◽  
Vol 4 (4) ◽  
pp. 469-476
Author(s):  
Lawal Abdulrashid
Keyword(s):  
Indigenous Knowledge ◽  
Risk Reduction ◽  
Natural Disaster ◽  
Disaster Risk Reduction ◽  
Disaster Risk ◽  
Flood Disaster ◽  
Structured Interviews ◽  
Major Disaster ◽  
Disaster Data ◽  
Disaster Monitoring

This study investigates the use of indigenous knowledge by the communities of semi-arid areas of Katsina state in forecasting/predicting the risk of flood disaster. Data were collected through semi-structured interviews with purposefully selected respondents and focus group discussions. It was found that indigenous knowledge of disaster monitoring, prediction and early warning is based on the observation of behaviors of animals, birds, insects, shrubs, trees, wind, temperature, and cloud among others. The communities of northern Katsina state faces other natural disaster challenges, flood is among the major disaster risk experienced by the population and over the years they have evolved indigenous ways that helped them not only in predicting this natural disaster but also in devising techniques and mechanism of dealing with it. Documentation of disaster risk reduction information and development of disaster risk reduction policy was recommended to deal with the situation

When Mother Earth Rises Up

2013 ◽  
Vol 44 (4) ◽  
pp. 271-277 ◽  
Author(s):  
Simona Sacchi ◽  
Paolo Riva ◽  
Marco Brambilla
Keyword(s):  
Free Will ◽  
Natural Disasters ◽  
Natural Disaster ◽  
Mental States ◽  
Disaster Victims ◽  
Willingness To Help ◽  
Mother Earth

Anthropomorphization is the tendency to ascribe humanlike features and mental states, such as free will and consciousness, to nonhuman beings or inanimate agents. Two studies investigated the consequences of the anthropomorphization of nature on people’s willingness to help victims of natural disasters. Study 1 (N = 96) showed that the humanization of nature correlated negatively with willingness to help natural disaster victims. Study 2 (N = 52) tested for causality, showing that the anthropomorphization of nature reduced participants’ intentions to help the victims. Overall, our findings suggest that humanizing nature undermines the tendency to support victims of natural disasters.

The social and psychological consequences of a natural disaster: A longitudinal study of Hurricane Audrey.

1963 ◽  
Author(s):  
Frederick L. Bates ◽  
◽  
C. W. Fogleman ◽  
V. J. Parenton ◽  
R. H. Pittman ◽  
...  
Keyword(s):  
Longitudinal Study ◽  
Natural Disaster ◽  
Psychological Consequences ◽  
The Social
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