scholarly journals Aplikasi SIG Untuk Pemetaan Zona Tingkat Bahaya Dan Keterpaparan Pemukiman Terhadap Tsunami Kota Denpasar

2020 ◽  
Vol 1 (2) ◽  
pp. 80-88
Author(s):  
Pegita Urmala Dewi ◽  
Oktaviana ◽  
Mila Wahdini ◽  
Naira Prasiamratri ◽  
Muhammad Budzar Alghifarry ◽  
...  
Keyword(s):  
At Risk ◽  
Element At Risk

Denpasar adalah ibukota provinsi Bali termasuk dalam wilayah terindikasi rawan tsunami karena berada didekat pinggir pantai serta memiliki ketinggian dan kemiringan yang relative kecil kepermukaan laut. Pemukiman merupakan aset wilayah yang harus dijaga. Sehingga zona keterpaparan pemukiman terhadap bahaya tsunami dibutuhkan guna memberikan informasi pada penduduk yang tinggal di kota tersebut. Tingkatan keterpaparan dilihat berdasarkan hazard dan element at risk. hazard  ini dilihat berdasarkan jarakdari garis pantai, ketinggian, jarak dari sungai, dan juga kemiringan lereng dari daerah Denpasar ini, sedangkan Element at risk atau elemen yang terkena bencana tsunami nya ialah pemukiman. Pemetaan menggunakan teknik overlay, metode skoring dan pembobotan menggunakan software ArcMap 10.3 sebagai pendekatan berbasis Sistem Informasi Geografis (SIG). Hasil penelitian menunjukan bahaya didominasi  tingkat sedang dan tinggi. tingkat keterpaparan tsunami pada pemukiman di kota Denpasar ini didominasi oleh tingkat keterpaparan sedang hingga tinggi dengan luas 3,46 km2 atau 27,21% dari luas Kota Denpasar dan terpusat di Kec. Denpasar Timur (25,14 km2) dan Kec. Denpasar Barat (15,02 km2). Selain itu,  pemukiman dengan keterpaparan tinggi yakni di Kec. Denpasar Selatan dengan luas 16,80 km2. Dikarenakan wilayah ketinggian yang rendah dan jarak dengan pantai  dekat.

scholarly journals Definitions and Concepts for Quantitative Rockfall Hazard and Risk Analysis

Geosciences ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 158
Author(s):  
Didier Hantz ◽  
Jordi Corominas ◽  
Giovanni B. Crosta ◽  
Michel Jaboyedoff
Keyword(s):  
Risk Assessment ◽  
At Risk ◽  
Failure Probability ◽  
Rockfall Hazard ◽  
Process Failure ◽  
Failure Propagation ◽  
Element At Risk ◽  
Hazard And Risk ◽  
Hazard And Risk Assessment ◽  
Definition Of

There is an increasing need for quantitative rockfall hazard and risk assessment that requires a precise definition of the terms and concepts used for this particular type of landslide. This paper suggests using terms that appear to be the most logic and explicit as possible and describes methods to derive some of the main hazards and risk descriptors. The terms and concepts presented concern the rockfall process (failure, propagation, fragmentation, modelling) and the hazard and risk descriptors, distinguishing the cases of localized and diffuse hazards. For a localized hazard, the failure probability of the considered rock compartment in a given period of time has to be assessed, and the probability for a given element at risk to be impacted with a given energy must be derived combining the failure probability, the reach probability, and the exposure of the element. For a diffuse hazard that is characterized by a failure frequency, the number of rockfalls reaching the element at risk per unit of time and with a given energy (passage frequency) can be derived. This frequency is relevant for risk assessment when the element at risk can be damaged several times. If it is not replaced, the probability that it is impacted by at least one rockfall is more relevant.

scholarly journals ANALISIS KERAWANAN BANJIR MENGGUNAKAN PENDEKATAN GEOMORFOLOGI DI DKI JAKARTA

2018 ◽  
Vol 2 (1) ◽  
pp. 1
Author(s):  
Siti Dahlia ◽  
Tricahyono Nurharsono ◽  
Wira Fazri Rosyidin
Keyword(s):  
At Risk ◽  
Central Government ◽  
Flood Hazard ◽  
Research Area ◽  
Capital City ◽  
Capital Region ◽  
Low Area ◽  
Flood Susceptibility ◽  
Element At Risk ◽  
Result Analysis

ABSTRACT  Special Capital Region of Jakarta Province is Capital City of Indonesia, which has various strategic functions, such as central government and economic and business center. Geographically DKI Jakarta Province is lowland, it caused Jakarta has high of flood hazard. This condition potentially result of high risk. Based on it, the aims of research is: 1). Making map of flood susceptibility in Special Capital Region of Jakarta area, based on geomorphology approach, and 2). Data inventory element at risk of flood. The method of data analysis used qualitative, based on interpretation satellite imagery data using elements of interpretation. Indicators used to result map of flood susceptibility are elevation, slope, and landform, using scoring and overlay technique. The result of research is flood susceptibility of low area is 13.613,40 ha, medium is 23.238,67 ha, and higt is 27.216,72 ha. Based on it, the majority of research area have hight of flood susceptibility. Based on spatial pattern, it showed that areas with high flood susceptibility are mostly located in the northern part of research area, and areas with the lowest flood susceptibility are majority in the southern part of researh area. The result analysis of element at risk, it showed that element at risk  affected by flood for high, medium, or low level is settlement. Key Words: Flood Susceptibility of Map, Exposure, Geomorphology, and Special Capital Region of Jakarta ABSTRAK Provinsi DKI Jakarta merupakan Ibu Kota negara Indonesia yang memiliki beragam fungsi startegis, seperti pusat pemerintahan, dan pusat ekonomi dan bisnis. Akan tetapi, kondisi geografis Provinsi DKI Jakarta yang merupakan dataran rendah, mengakibatkan wilayah Jakarta memiliki ancaman tinggi terhadap bahaya banjir. Hal ini dapat berpotensi menghasilkan tingginya risiko kerugian terhadap bencana. Berdasarkan hal tersebut, tujuan dalam penelitian ini yaitu: 1) Membuat peta kerawanan banjir Provinsi DKI Jakarta berdasarkan pendekatan geomorfologi, dan 2). Melakukan inventarisasi elemen berisiko yang berpotensi terpapar banjir. Metode analisis data dalam penelitian ini menggunakan analisis kualitatif, karena berdasarkan teknik interpretasi data citra secara kualitatif yaitu menggunakan unsur-unsur interpretasi. Parameter- parameter yang digunakan untuk menghasilkan peta kerawanan banjir yaitu elevasi, kemiringan lereng, dan bentuklahan, dengan menggunakan skoring dan tumpang susun. Hasil penelitian menunjukkan bahwa tingkat kerawanan banjir rendah seluas 13.613,40 ha, sedang seluas 23.238,67 ha, dan tinggi seluas 27.216,72 ha. Mayoritas wilayah penelitian terletak pada tingkat kerawanan banjir tinggi. Berdasarkan pola spasial menunjukkan bahwa daerah dengan tingkat kerawanan banjir tinggi mayoritas terletak di bagian utara wilayah penelitian, dan daerah dengan tingkat kerawanan banjir rendah mayoritas dibagian selatan wilayah penelitian. Hasil analisis keterpaparan elemen berisiko wilayah penelitian menunjukkan bahwa elemen berisiko yang berpotensi tertinggi terkena banjir baik tingkat tinggi, sedang, atau rendah yaitu pemukiman. Kata Kunci: Pemetaan Kerawanan Banjir, Keterpaparan, Gemorfologi, dan DKI Jakarta

scholarly journals Vulnerability curves vs. vulnerability indicators: application of an indicator-based methodology for debris-flow hazards

2016 ◽  
Vol 16 (8) ◽  
pp. 1771-1790 ◽  
Author(s):  
Maria Papathoma-Köhle
Keyword(s):  
At Risk ◽  
Debris Flow ◽  
Vulnerability Assessment ◽  
Structural Characteristics ◽  
Physical Vulnerability ◽  
Vulnerability Indicators ◽  
Element At Risk ◽  
Mountain Hazards ◽  
Methodological Approaches

Abstract. The assessment of the physical vulnerability of elements at risk as part of the risk analysis is an essential aspect for the development of strategies and structural measures for risk reduction. Understanding, analysing and, if possible, quantifying physical vulnerability is a prerequisite for designing strategies and adopting tools for its reduction. The most common methods for assessing physical vulnerability are vulnerability matrices, vulnerability curves and vulnerability indicators; however, in most of the cases, these methods are used in a conflicting way rather than in combination. The article focuses on two of these methods: vulnerability curves and vulnerability indicators. Vulnerability curves express physical vulnerability as a function of the intensity of the process and the degree of loss, considering, in individual cases only, some structural characteristics of the affected buildings. However, a considerable amount of studies argue that vulnerability assessment should focus on the identification of these variables that influence the vulnerability of an element at risk (vulnerability indicators). In this study, an indicator-based methodology (IBM) for mountain hazards including debris flow (Kappes et al., 2012) is applied to a case study for debris flows in South Tyrol, where in the past a vulnerability curve has been developed. The relatively "new" indicator-based method is being scrutinised and recommendations for its improvement are outlined. The comparison of the two methodological approaches and their results is challenging since both methodological approaches deal with vulnerability in a different way. However, it is still possible to highlight their weaknesses and strengths, show clearly that both methodologies are necessary for the assessment of physical vulnerability and provide a preliminary "holistic methodological framework" for physical vulnerability assessment showing how the two approaches may be used in combination in the future.

scholarly journals PENERAPAN FUZZY LOGIC DALAM PEMBUATAN PETA ELEMENT AT RISK BENCANA LUAPAN BANJIR SANGAI AIR BENGKULU KOTA BENGKULU

2019 ◽  
Vol 11 (2) ◽  
pp. 135-139
Author(s):  
Farouki Dinda Rassarandi ◽  
Bungaran Roy Satria Tambunan
Keyword(s):  
At Risk ◽  
Fuzzy Logic ◽  
Input Data ◽  
Element At Risk

Banjir merupakan suatu bencana yang dapat menimbulkan kerugian dan kerusakan di berbagai bidang, khususnya infrastruktur. Salah satu upaya untuk mencegah dan mengurangi dampak dari bencana banjir yaitu dengan pembuatan simulasi melalui pemodelan spasial dalam bentuk peta element at risk. Pada pembuatan peta element at risk, input data berupa peta yang diunduh dari Open Street Map yang berisikan kenampakan alam maupun infrastruktur dari simulasi bencana yang dibuat menggunakan logika Fuzzy. Penerapan logika Fuzzy digunakan untuk menginterpretasikan statemen yang samar dari persentase area bangunan yang terdampak pada setiap klasifikasi area luapan banjir menjadi sebuah pengertian yang logis dalam pengklasifikasian kerusakan “Berat’, “Sedang” dan “Ringan”. Berdasarkan hasil simulasi bencana banjir yang telah dilakukan, didapati bahwa jumlah bangunan yang terkena dampak bencana banjir luapan Sungai Air Bengkulu adalah sebanyak 37 bangunan “Rusak Berat’, 216 “Rusak Sedang’ dan 329 “Rusak Ringan’, dengan jumlah korban jiwa terdampak sebanyak 2.328 jiwa.

scholarly journals GIS and Remote Sensing for Malaria Risk Mapping, Ethiopia

2014 ◽  
Vol XL-8 ◽  
pp. 155-161 ◽  
Author(s):  
A. Ahmed
Keyword(s):  
At Risk ◽  
Land Use ◽  
Land Cover ◽  
Malaria Risk ◽  
Hazard Map ◽  
Land Use Land Cover ◽  
Risk Map ◽  
Risk Area ◽  
Element At Risk ◽  
Very High

Integrating malaria data into a decision support system (DSS) using Geographic Information System (GIS) and remote sensing tool can provide timely information and decision makers get prepared to make better and faster decisions which can reduce the damage and minimize the loss caused. This paper attempted to asses and produce maps of malaria prone areas including the most important natural factors. The input data were based on the geospatial factors including climatic, social and Topographic aspects from secondary data. The objective of study is to prepare malaria hazard, Vulnerability, and element at risk map which give the final output, malaria risk map. The malaria hazard analyses were computed using multi criteria evaluation (MCE) using environmental factors such as topographic factors (elevation, slope and flow distance to stream), land use/ land cover and Breeding site were developed and weighted, then weighted overlay technique were computed in ArcGIS software to generate malaria hazard map. The resulting malaria hazard map depicts that 19.2 %, 30.8 %, 25.1 %, 16.6 % and 8.3 % of the District were subjected to very high, high, moderate, low and very low malaria hazard areas respectively. For vulnerability analysis, health station location and speed constant in Spatial Analyst module were used to generate factor maps. For element at risk, land use land cover map were used to generate element at risk map. Finally malaria risk map of the District was generated. Land use land cover map which is the element at risk in the District, the vulnerability map and the hazard map were overlaid. The final output based on this approach is a malaria risk map, which is classified into 5 classes which is Very High-risk area, High-risk area, Moderate risk area, Low risk area and Very low risk area. The risk map produced from the overlay analysis showed that 20.5 %, 11.6 %, 23.8 %, 34.1 % and 26.4 % of the District were subjected to very high, high, moderate, low and very low malaria risk respectively. This help to plan valuable measures to be taken in early warning, monitor, control and prevent malaria epidemics.

scholarly journals A physical approach on flood risk vulnerability of buildings

2014 ◽  
Vol 11 (2) ◽  
pp. 1411-1460 ◽  
Author(s):  
B. Mazzorana ◽  
S. Simoni ◽  
C. Scherer ◽  
B. Gems ◽  
S. Fuchs ◽  
...  
Keyword(s):  
At Risk ◽  
Residential Buildings ◽  
Dynamic Assessment ◽  
Building Envelope ◽  
Economic Damage ◽  
Assessment Procedure ◽  
Element At Risk ◽  
Vulnerability Of Buildings ◽  
The Impact ◽  
Vulnerability Functions

Abstract. The design of efficient hydrological risk mitigation strategies and their subsequent implementation relies on a careful vulnerability analysis of the elements exposed. Recently, extensive research efforts were undertaken to develop and refine empirical relationships linking the structural vulnerability of buildings to the impact forces of the hazard processes. These empirical vulnerability functions allow estimating the expected direct losses as a result of the hazard scenario based on spatially explicit representation of the process patterns and the elements at risk classified into defined typological categories. However, due to the underlying empiricism of such vulnerability functions, the physics of the damage generating mechanisms for a well-defined element at risk with its peculiar geometry and structural characteristics remain unveiled, and, as such, the applicability of the empirical approach for planning hazard-proof residential buildings is limited. Therefore, we propose a conceptual assessment scheme to close this gap. This assessment scheme encompasses distinct analytical steps: modelling (a) the process intensity, (b) the impact on the element at risk exposed and (c) the physical response of the building envelope. Furthermore, these results provide the input data for the subsequent damage evaluation and economic damage valuation. This dynamic assessment supports all relevant planning activities with respect to a minimisation of losses, and can be implemented in the operational risk assessment procedure.

scholarly journals EXTRACTION OF ELEMENT AT RISK FOR LANDSLIDES USING REMOTE SENSING METHOD

2018 ◽  
Vol XLII-4/W9 ◽  
pp. 181-188
Author(s):  
R. C. Hasan ◽  
Q. A. Rosle ◽  
M. A. Asmadi ◽  
N. A. Mohd Kamal
Keyword(s):  
At Risk ◽  
Extraction Process ◽  
Automatic Process ◽  
Landslide Risk ◽  
Lidar Data ◽  
Large Area ◽  
Building Footprint ◽  
Element At Risk ◽  
Hazard And Risk ◽  
Study Sites

<p><strong>Abstract.</strong> One of the most critical steps towards landslide risk analysis is the determination of element at risk. Element at risk describes any object that could potentially fail or exposed to hazards during disaster. Without quantification of element at risk information, it is difficult to estimate risk. This paper aims at developing a methodology to extract and quantity element at risk from airborne Light Detection and Ranging (LiDAR) data. The element at risk map produced was then used to construct exposure map which describes the amount of hazard for each element at risk involved. This study presented two study sites at Kundasang and Kota Kinabalu in Sabah with both areas have experienced major earthquake in June 2015. The results show that not all the features can be automatically extracted from the LiDAR data. For example, automatic extraction process could be done for building footprint and building heights, but for others such as roads and vegetation areas, a manual digitization is still needed because of the difficulties to differentiate between these features. In addition to this, there were also difficulties in identifying attribute for each feature, for example to separate between federal roads with state and unpaved roads. Therefore, for large area hazard and risk mapping, the authors suggested that an automatic process should be investigated in the future to reduce time and cost to extract important features from LiDAR data.</p>

GIS-Based Quantitative Landslide Risk Assessment Approach for Property and Life at Bartin Hepler

2021 ◽  
pp. 1623-1636
Author(s):  
Arzu Erener ◽  
Gülcan Sarp ◽  
Şebnem Düzgün
Keyword(s):  
Risk Assessment ◽  
At Risk ◽  
Landslide Risk ◽  
Landslide Occurrence ◽  
Damage Probability ◽  
Landslide Risk Assessment ◽  
Assessment Approach ◽  
Damage Probability Matrix ◽  
Element At Risk ◽  
Work Done

In Turkey, landslides are the second most common natural disasters that cause damages in Turkey that follow the earthquakes. Thus, landslide risk assessment is of crucial importance in this area. Therefore in this study a quantitative approach for mapping landslide risk is developed for property and life at local scale. The approach is first based on the identification of existing elements at risk in the area by the developed algorithm. Then the vulnerability approach focuses on determination of quantitative vulnerability values for each element at risk by considering temporal and spatial impacts by adopting a “damage probability matrix“ approach. The loss estimation was combined with the hazard values which are based on former work done in Bartın Kumluca area where a detailed study of landslide occurrence and hazard in the recent past (last 30 years) was carried out. The final result risk maps for property ($/pixel/year) and life (life/pixel/year) shows all losses per pixel annually for each element at risk in Hepler village.

APLIKASI SIG UNTUK PEMETAAN ZONA KETERPAPARAN PERMUKIMAN TERHADAP TSUNAMI

2018 ◽  
Vol 2 ◽  
pp. 317
Author(s):  
Fakhri Hadi
Keyword(s):  
At Risk ◽  
Element At Risk ◽  
Gt Element

<p>Kota Pariaman merupakan salah satu kota di Indonesia yang terindikasi rawan terhadap bencana tsunami dikarenakan lokasinya yang berada di pinggir pantai serta berbatasan langsung dengan Samudera Hindia. Permukiman merupakan salah satu aset yang harus dijaga. Pemetaan zona keterpaparan permukiman terhadap tsunami diperlukan demi menjaga penduduk yang bertempat tinggal di kota tersebut serta sebagai acuan dalam mitigasi bencana dan meminimalkan kerugian akibat bencana tersebut. Pemetaan ini bertujuan untuk menaksir tingkat keterpaparan permukiman terhadap bencana tsunami. Tingkat keterpaparan dilihat dari dua komponen, yaitu tingkat bahaya (<em>hazard</em>), serta <em>element at risk. </em>Tingkat bahaya tsunami dilihat dari<em> </em>jarak dari garis pantai, ketinggian, wilayah lereng, serta jarak dari sungai sedangkan <em>Element at risk </em>atau elemen yang terkena bencana tsunami yaitu permukiman. Pemetaan keterpaparan ini menggunakan teknik <em>overlay</em>, metode skoring dan pembobotan dengan menggunakan <em>software ArcMap 10.1 </em>sebagai salah satu pendekatan berbasis Sistem Informasi Geografis (SIG). Hasil penelitian menunjukkan bahwa tingkat keterpaparan permukiman di Kota Pariaman terhadap tsunami didominasi oleh tingkat keterpaparan sedang dengan luas 963,07 hektar, kemudian disusul oleh tingkat keterpaparan tinggi dengan luas 572,60 hektar, serta tingkat keterpaparan rendah dengan luas 121,98 hektar. Permukiman di Kota Pariaman yang terpapar tinggi terhadap tsunami cenderung berada di wilayah yang landai, serta dekat dengan pantai.</p><p class="abstract"><strong>Kata Kunci:  </strong>Keterpaparan, Permukiman, SIG, Tsunami</p>

scholarly journals VULNERABILITY MAPPING AND ANALYSIS: AN IMPLEMENTATION IN GEOHAZARD AREAS IN SABAH

2018 ◽  
Vol XLII-4/W9 ◽  
pp. 189-200 ◽  
Author(s):  
Z. Mohamad ◽  
Z. Ramli ◽  
M. Z. Abd Rahman ◽  
M. R. Mohd Salleh ◽  
Z. Ismail ◽  
...  
Keyword(s):  
At Risk ◽  
Construction Materials ◽  
Weighted Average ◽  
Road Construction ◽  
Risk Mapping ◽  
Vulnerability Mapping ◽  
Natural Phenomenon ◽  
Landslide Risk ◽  
Specific Element ◽  
Element At Risk

<p><strong>Abstract.</strong> Vulnerability identifies the element-at-risk as well as the evaluation of their relationships with the hazard. The relationships relate the landslide potential damages over a specific element-at-risk. Vulnerability can be defined as the degree of loss to a given element-at-risk or set of elements at risk resulting from the occurrence of a natural phenomenon of a given magnitude and expressed on a scale from 0 (no damage) to 1 (total damage). In this study, the landslide vulnerability mapping and analysis were made on two element-at-risks namely buildings and roads. Based on field observations, building and road construction materials have been classified into 22 and 5 construction materials respectively. The field visits were made on specific areas based on candidate buildings and roads as chosen during the landslide exposure analysis and mapping. The vulnerability values for these element-at-risks were expressed using expert opinion. Four experts have been interviewed with separate sessions. The experts were also supplied with local information on the landslides occurrences and photos of element-at-risk in Kundasang and Kota Kinabalu. The vulnerability matrices were combined based on the weighted average approach, in which higher weight was assigned to panel with local expert (landslides and damage assessment), wide experience in landslide vulnerability analysis, hazard and risk mapping. Finally, the vulnerability maps were produced for Kundasang and Kota Kinabalu with spatial resolution of 25<span class="thinspace"></span>cm. These maps were used for the next step i.e. landslide risk mapping and analysis.</p>

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