Penilaian Risiko Bencana di Sub DAS Amprong Menggunakan Pendekatan GIS

2020 ◽  
Vol 5 (2) ◽  
pp. 107-121
Author(s):  
Listyo Yudha Irawan ◽  
Nabila Nabila ◽  
Damar Panoto ◽  
Agung Chandra Darmansyah ◽  
Annisa Nur Rasyidah ◽  
...  
Keyword(s):  
Information System ◽  
Geographic Information System ◽  
Irrigation System ◽  
Geographic Information ◽  
Accurate Solution ◽  
Disaster Mitigation ◽  
Landslide Risk ◽  
Residential Areas ◽  
Risk Levels ◽  
Risk Areas

Abstrak: Sub DAS Amprong secara administrasi masuk pada wilayah Kabupaten Malang dan Kota Malang. Meliputi lima Kecamatan yakni: Kedungkandang, Poncokusumo, Tumpang, Pakis dan Jabung. Risiko bencana longsor tergolong tinggi pada kawasan ini. Maka dari itu, penelitian ini bertujuan untuk melakukan pengurangan risiko bencana longsor mengunakan pendeketaan GIS (Geographic Information System). Menggunakan GIS distribusi tingkat risiko akan dapat diketahui dengan baik, sehingga mampu memberikan solusi yang lebih akurat. Penelitian ini meliputi empat tahapan: 1) pemetaan bahaya longsor, 2) pemetaan kerentanan bencana, 3) pemetaan kapasitas bencana, 4) pemetaan risiko bencana. Hasilnya diketahui bahwa kecamatan Jabung dan Poncokusumo merupakan wialayah dengan tingkat risiko longsor paling tinggi. Upaya yang dapat dilakukan untuk mengurangi tingkat risiko dapat dilakukan melalui mitigasi bencana secara struktural dan nonstruktural. Wilayah dengan risiko tinggi bukan merupakan kawasan pemukiman, namun memiliki aktivitas utama berupa pertanian. Oleh karena itu perlu adanya manajemen risiko bencana longsor dalam usaha longsor seperti: dengan cara: 1) pengaturan sistem irigasi dengan baik, 2) penerapan sistem terasering, dan 3) pemasangan bronjong pada kaki lereng. Abstract: Amprong watershed is administratively included in Malang Regency and Malang City. Includes five districts namely: Kedungkandang, Poncokusumo, Tumpang, Pakis and Jabung. The risk of landslides is classified high in this region. Therefore, this research aims to reduce the risk of landslides using GIS (Geographic Information System). Using GIS the distribution of risk levels will be well known, so as to provide a more accurate solution. This research includes four stages: 1) mapping of landslide hazards, 2) mapping of disaster vulnerability, 3) mapping of disaster capacity, 4) mapping of disaster risk. The results are known that the Jabung and Poncokusumo sub-districts are areas with the highest risk of landslides. Efforts that can be made to reduce the level of risk can be done through structural and nonstructural disaster mitigation. High risk areas are not residential areas, but have major activities in the form of agriculture. Therefore, it is necessary to have landslide risk management, such as: by: 1) regulating the irrigation system properly, 2) applying the terracing system, and 3) installing gabions at the foot of the slope.

scholarly journals Geographic information system based malaria risk analysis and mapping in Erer District eastern Ethiopia

2019 ◽  
Author(s):  
Maereg Teklay A Amare ◽  
Gebrehiwot Gebretsadik kassa ◽  
Esie G/wahid Gebre ◽  
Abadi Abay ◽  
Mekonen yimer ◽  
...  
Keyword(s):  
Information System ◽  
Geographic Information System ◽  
Analytic Hierarchy Process ◽  
Geographic Information ◽  
Malaria Risk ◽  
Analytic Hierarchy ◽  
Risk Areas ◽  
Hazard And Risk ◽  
Temporal And Spatial ◽  
Hierarchy Process

Abstract Background: Erer is one of the districts in Ethiopia where the first malaria transmission season occurs. Although the focus on malaria research has increasingly gained ground, little emphasis has been given to develop quantitative methods for assessing malaria hazard and risk in a temporal and spatial perspective. Objective: To characterize and examine the temporal and spatial malaria trend. The research also aims at producing a predictive model of malaria hazard and risk in Erer district. Methods: In this study a cross sectional research design was used. It was carried out through the collection of both quantitative and qualitative data about the nature of malaria and household’s response towards it. A multi-stage sampling method was used and 136 sample size was determined from the sampling frame of 6203 households. Simple descriptive analysis technique was used to determine the malaria trend of the district. Integration of Geographic information system and analytic hierarchy process was used to determine the weight of each factor pair wise comparison and weighted linear combination was used to aggregate and produce the hazard and malaria risk maps. Results: Results have shown that 19.92%, 27.96%, 32.35%, 18.93% and 0.82% of the district was very high, high, moderate, low and very low malaria risk areas respectively. The malaria trend of the area was found to be variable across time with 2014 the peak year while the minimum case observed was in 2016. Conclusion: It is possible to conclude that risk maps are important for estimating the scale of the risk, and enable detection of high risk areas, thus facilitating decision making and policy formulation for enhanced malaria control interventions. Key words: Analytic Hierarchy Process; Malaria risk; Malaria trend; Weighted overlay

scholarly journals An Interactive Web-Gis Development for Risk Tsunami Hazard Potential Information In Pangandaran Village West Java

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Juan Andrian ◽  
Arif Ismail ◽  
Iwan Setiawan ◽  
Shafira Himayah
Keyword(s):  
Information System ◽  
Geographic Information System ◽  
Geographic Information ◽  
Tsunami Hazard ◽  
Disaster Mitigation ◽  
Web Gis ◽  
River Networks ◽  
Tsunami Disaster ◽  
Public Facilities ◽  
Lack Of Information

<p class="TableParagraph"><em>In 2006, a tsunami disaster occurred on the coast of Pangandaran Regency which claimed up to 664 fatalities. A large number of people died due to lack of information in knowing areas that are prone to tsunami disasters. Therefore, a geographic information system for the tsunami disaster is needed to facilitate the Pangandaran community to find out areas that are prone to tsunami disasters. In making a geographic information system web tsunami disaster using GeoServer, PostgreSQL and LeafletJS. Making a geographic information system web is done in several ways, namely, entering shapefile data into a database and then displaying it on a map server. The results of creating a web of the tsunami geographic information system contain information on land use, public facilities, hamlet boundaries, road networks, river networks and tsunami disaster mitigation.</em></p>

Reducing the Risk of Flood Disasters in Lamongan Regency Using the Geographic Information System (GIS)

UKaRsT ◽  
2021 ◽  
Vol 5 (2) ◽  
pp. 268
Author(s):  
Salwa Nabilah ◽  
Nur Azizah Affandy ◽  
N. Anwar ◽  
M. A. Maulana ◽  
N. Nurwatik
Keyword(s):  
Information System ◽  
Geographic Information System ◽  
Land Cover ◽  
Local Governments ◽  
Wide Distribution ◽  
Geographic Information ◽  
Disaster Mitigation ◽  
Flood Disasters ◽  
High Flood ◽  
The Impact

Flood disasters cause negative impacts, such as damage to facilities to the onset of fatalities. Reducing the risk of flooding needs to be done to reduce the impact caused by this disaster. Lamongan Regency is one of the regencies in East Java affected by floods every year in most of its areas. This study aims to reduce the risk caused by flooding by using GIS (Geographic Information System). Mitigation is done by determining areas with a high potential risk of being affected by flooding. The study used spatial analysis functions in ArcGIS. Supporting variables used rainfall, land cover, slope, soil texture, and watershed area, and it becomes important in determining flood-prone areas. From the results, the largest soil classification is the Kpl soil type. Litosol Gray Grumosol, The wide distribution of rainfall from 1500-1750 mm has the widest distribution is 66,67 ha. The slope of 0-8% has the widest distribution of 92,257 ha, making Lamongan a very vulnerable high flood area. Laren District is the District with the greatest flood potential, and Irrigated Field is the dominant land cover type affected by the flood. With the flood disaster map generated from this research, local governments can seek prevention in areas with high flood potential. They can carry out socialization based on disaster mitigation, especially for districts with potential flooding.

scholarly journals Multivariate spatio-temporal approach to identify vulnerable localities in dengue risk areas using Geographic Information System (GIS)

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Gayan P. Withanage ◽  
Malika Gunawardana ◽  
Sameera D. Viswakula ◽  
Krishantha Samaraweera ◽  
Nilmini S. Gunawardena ◽  
...  
Keyword(s):  
Information System ◽  
Geographic Information System ◽  
High Risk ◽  
Sri Lanka ◽  
Risk Model ◽  
Early Warning Systems ◽  
Geographic Information ◽  
Distance Functions ◽  
Public Places ◽  
Risk Areas

AbstractDengue is one of the most important vector-borne infection in Sri Lanka currently leading to vast economic and social burden. Neither a vaccine nor drug is still not being practiced, vector controlling is the best approach to control disease transmission in the country. Therefore, early warning systems are imminent requirement. The aim of the study was to develop Geographic Information System (GIS)-based multivariate analysis model to detect risk hotspots of dengue in the Gampaha District, Sri Lanka to control diseases transmission. A risk model and spatial Poisson point process model were developed using separate layers for patient incidence locations, positive breeding containers, roads, total buildings, public places, land use maps and elevation in four high risk areas in the district. Spatial correlations of each study layer with patient incidences was identified using Kernel density and Euclidean distance functions with minimum allowed distance parameter. Output files of risk model indicate that high risk localities are in close proximity to roads and coincide with vegetation coverage while the Poisson model highlighted the proximity of high intensity localities to public places and possibility of artificial reservoirs of dengue. The latter model further indicate that clustering of dengue cases in a radius of approximately 150 m in high risk areas indicating areas need intensive attention in future vector surveillances.

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