Mapping of Flooded Areas in the Kudus District

2016 ◽  
Vol 4 (3) ◽  
pp. 670
Author(s):  
Rina Fiati ◽  
Anastasya Latubessy
Keyword(s):  
Information System ◽  
Expert System ◽  
Research Use ◽  
Solid Objects ◽  
Prone Area ◽  
Rule Method ◽  
Flood Prone Area ◽  
Flood Flow ◽  
Flooded Areas ◽  
Flood Prone Areas

<p>Flood is still an annual problem in the Kudus District. Based on the survey and interview with Regional Disaster Management Agency (BPBD – Badan Penanggulangan Bencana Daerah) data showed that in the Kudus District there are still many flood-prone areas. They also said that, there are six parameter that can be used to identify potential flood area such as: extensive inundation (km2, ha), depth or height of flood waters (meters), the flow velocity (m/s,km/h), the material washed away by flood flow (rocks, boulders, trees, and other solid objects), concentrations of water or silt thickness (meters, centimeters), and duration of inundation (hours, days, months). Therefor this research use six parameters are then analyzed and used as a benchmark model to identify flood-prone areas by using the production rule method, and as the material in constructing and designing flood-prone area identification systems based on expert system. Thus this research resulted a system to assist the identification of flood prone areas in the Kudus District by using expert system and geographic information system (GIS).</p>

SISTEM INFORMASI GEOGRAFIS (SIG) DAERAH RAWAN BANJIR DI KOTA BENGKULU MENGGUNAKAN ARCVIEW

2016 ◽  
Vol 12 (1) ◽  
Author(s):  
Rahma Wayan Lestari ◽  
Indra Kanedi ◽  
Yode Arliando
Keyword(s):  
Information System ◽  
Geographic Information System ◽  
Geographic Information ◽  
The Body ◽  
Microsoft Excel ◽  
Microsoft Word ◽  
Prone Area ◽  
Flood Prone Area ◽  
The City ◽  
Flood Prone Areas

The purpose of this research is to create a geographic information system Bengkulu city flood-prone areas using ArcView. Apply the knowledge obtained during the lecture, especially relating to the development of Geographic Information Systems. To be able to produce a system that is accurate and useful information for the community. Where the research was conducted in the city of Bengkulu BASARNAS. Bengkulu BASARNAS office specializing in Search and Rescue (SAR), is the body that manage the flood of data that is still done manually, using Microsoft Word and Microsoft Excel. Thus experiencing problems in delivering information directly to the office because the SAR agencies require a long time.Keywords: Geographic Information System, Flood Prone Area

scholarly journals Pemetaan Tingkat Kerawananan Banjir Di Kota Jambi Menggunakan Metode Scoring Dan Overlay Berbasis Sistem Informasi Geografis

2021 ◽  
Vol 5 (2) ◽  
pp. 132-141
Author(s):  
Lusiani Pryastuti ◽  
Keyword(s):  
Land Use ◽  
Information System ◽  
Spatial Distribution ◽  
Geographic Information System ◽  
Soil Type ◽  
Flood Vulnerability ◽  
Overlay Method ◽  
Prone Area ◽  
Flood Prone Area ◽  
Flood Prone Areas

This research is about flood vulnerability mapping in Jambi City based on Geographic Information System (GIS). This study is aiming to find out the flood vulnerability level, spatial distribution of flood, and flood prone areas in Jambi City. We used five parameters that affect flood vulnerability, including land slope, land level, land use, soil type, and rainfall during 2019. The method used is the scoring and overlay method with the help of ArcGis software. Flood vulnerability level was divided into three categories, namely quite vulnerable, vulnerable, and very vulnerable. The results obtained in this study are that most of Jambi City has a level of flood vulnerability in the vulnerable category, which is an area of 9254.82 ha (58%), while for the area that is dominated quite safe from flooding, Jambi Selatan sub-district, is 2849.14 ha (18%). This shows that more than half of the Jambi city area is a flood-prone area so it is very important to carry out structural and non-structural mitigation actions

scholarly journals Machine-Learning blends of geomorphic descriptors: value and limitations for flood hazard assessment across large floodplains

2021 ◽  
Author(s):  
Andrea Magnini ◽  
Michele Lombardi ◽  
Simone Persiano ◽  
Antonio Tirri ◽  
Francesco Lo Conti ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Recent Literature ◽  
Flood Hazard ◽  
Open Problems ◽  
Prone Area ◽  
Tree Models ◽  
Multivariate Approaches ◽  
Flood Prone Area ◽  
Flood Prone Areas

Abstract. Recent literature shows several examples of simplified approaches that perform flood hazard (FH) assessment and mapping across large geographical areas on the basis of fast-computing geomorphic descriptors. These approaches may consider a single index (univariate) or use a set of indices simultaneously (multivariate). What is the potential and accuracy of multivariate approaches relative to univariate ones? Can we effectively use these methods for extrapolation purposes, i.e. FH assessment outside the region used for setting up the model? Our study addresses these open problems by considering two separate issues: (1) mapping flood-prone areas, and (2) predicting the expected water depth for a given inundation scenario. We blend seven geomorphic descriptors through Decision Tree models trained on target FH maps, referring to a large study area (≈105 km2). We discuss the potential of multivariate approaches relative to the performance of a selected univariate model and on the basis of multiple extrapolation experiments, where models are tested outside their training region. Our results show that multivariate approaches may (a) significantly enhance flood-prone area delineation (overall accuracy: 93 %) relative to univariate ones (overall accuracy: 84 %), (b) provide accurate predictions of expected inundation depths (determination coefficient ≈0.7), and (c) produce encouraging results in extrapolation.

GIS Based Information System to Identify Flood Prone Area in Bangladesh

2020 ◽  
Author(s):  
Md. Sa-Ad Ibne Jamal ◽  
Md. Nafiz Imtiaz Saimon ◽  
Sabrina Afrin ◽  
Sabrin Islam ◽  
Sumaiya Afroz Mila ◽  
...  
Keyword(s):  
Information System ◽  
Prone Area ◽  
Flood Prone Area

scholarly journals ANALISIS KERENTANAN KAWASAN PERMUKIMAN PADA KAWASAN RAWAN BANJIR DI BAGIAN HILIR SUNGAI SARIO

2017 ◽  
Vol 13 (3A) ◽  
pp. 119
Author(s):  
Ivone Paula Bungkolu ◽  
Grace A. J. Rumagit ◽  
Rine ., Kaunang
Keyword(s):  
Sampling Method ◽  
Social Ties ◽  
Influencing Factor ◽  
Analytical Technique ◽  
Susceptibility Factor ◽  
Susceptibility Factors ◽  
The Social ◽  
Prone Area ◽  
Flood Prone Area ◽  
Flood Prone Areas

The objectives of this research are: (1) Analyzing any susceptibility factors that can influence people downstream of Sario River to settle or not stay in settlements located in flood prone areas, (2) Reviewing the policy of flood-prone settlement management what is most effective in overcoming the existing flood problems at the site. This research was conducted in September until November 2017. Sampling method used was purposive sampling method. Analytical technique used in this research using quantitative method that is using logistic regression. The results of this study indicate that (1) the most influencing factor of the person staying in the flood prone area downstream of the Sario River is the social susceptibility factor precisely the social ties, ie the close kinship between the members of the community in the location. (2) Furthermore, the most appropriate government program for dealing with settlements of the lower flood prone areas of the Sario River is through the creation of a retaining river basin or river dredging.

scholarly journals Flood-Prone Area Delineation in Urban Subbasins Based on Stream Ordering: Culiacan Urban Basin as a Study Case

2021 ◽  
Vol 13 (24) ◽  
pp. 13513
Author(s):  
Antonio J. Sanhouse-García ◽  
Jesús Gabriel Rangel-Peraza ◽  
Sergio A. Rentería-Guevara ◽  
Yaneth A. Bustos-Terrones ◽  
Zuriel D. Mora-Félix ◽  
...  
Keyword(s):  
Urban Development ◽  
Hydrological Cycle ◽  
Flow Direction ◽  
Global Navigation Satellite Systems ◽  
Stream Order ◽  
Satellite Systems ◽  
Prone Area ◽  
Flood Prone Area ◽  
Global Navigation Satellite ◽  
Flood Prone Areas

Urban development decreases infiltration, increases the runoff velocity, and reduces the concentration times. This situation increases the flood risk in urban watersheds, which represent a management challenge for urban communities and authorities. To increase the resilience of communities due to modifications of the hydrological cycle produced by climate change and urban development, a methodology is proposed to delineate flood-prone areas in urban basins. This methodology is implemented in an urban subbasin of Culiacan, Mexico, and is based on stream order. A high-resolution digital elevation model was used, which was validated independently through a photogrammetric flight with an unmanned aerial vehicle and ground control points obtained with GNSS (global navigation satellite systems) receivers. Morphometric parameters related to geometry, shape, relief, and drainage network aspects of the subbasin were determined and analyzed. Then, flood-prone area zonation was carried out based on stream-order classification and flow direction. Fieldwork was also carried out for the inspection of the sewage network conditions. This methodology simplifies the identification of the flood-prone areas in urban subbasins without carrying out complex hydraulic calculations.

scholarly journals Impact and Mitigation Strategies for Flash Floods Occurrence towards Vehicle Instabilities

2020 ◽  
Author(s):  
Zahiraniza Mustaffa ◽  
Ebrahim Hamid Hussein Al-Qadami ◽  
Syed Muzzamil Hussain Shah ◽  
Khamaruzaman Wan Yusof
Keyword(s):  
Flash Floods ◽  
Mitigation Strategies ◽  
The Road ◽  
Road Users ◽  
Prone Area ◽  
Proper Estimation ◽  
Flood Prone Area ◽  
Flooded Area ◽  
Vehicle Information ◽  
Flooded Areas

This chapter presents a flood risk management system for vehicles at roadways, developed from extensive experimental and numerical studies on the impact of flash floods towards vehicle instabilities. The system, easily addressed as FLO-LOW, developed to contradict the assumptions that a vehicle would be able to protect the passengers from the flood impact. Herein the hydrodynamics of flows moving across these roads coupled with the conditions of a static car that would result in vehicle instabilities has been studied. In an attempt to prevent fatalities in commonly flooded areas, permanent structures are installed to warn users regarding water depth at the flooded areas. The existing flood monitoring system only focuses on water conditions in rivers or lake in order to determine risks associated with floods. Thus, there is a need for a better system to understand and quantify a mechanism to determine hydrodynamics instability of a vehicle in floodwaters. FLO-LOW enables the road users to input their vehicle information for a proper estimation of safety limits upon crossing the flood prone area. Preferably, the system enables road users to describe and quantify parameters that might cause their vehicles to become vulnerable to being washed away as they enter the flooded area.

scholarly journals OBSERVING THE INUNDATED AREA USING LANDSAT-8 MULTITEMPORAL IMAGES AND DETERMINATION OF FLOOD-PRONE AREA IN BANDUNG BASIN

2019 ◽  
Vol 15 (2) ◽  
pp. 131
Author(s):  
Fajar Yulianto ◽  
NFn Suwarsono ◽  
Sayidah Sulma ◽  
Muhammad Rokhis Khomarudin
Keyword(s):  
Land Surface ◽  
Land Use Changes ◽  
Image Data ◽  
Landsat 8 ◽  
Flood Events ◽  
Water Index ◽  
The Past ◽  
Prone Area ◽  
Flood Prone Area ◽  
Flood Prone Areas

Flood is the most frequent hydro-meteorological disaster in Indonesia. Flood disasters in the Bandung basin result from increasing population density, especially in the Citarum riverbank area, accompanied by land use changes in upstream of the Citarum catchment area which has disrupted the river’s function. One of the basic issues that need to be investigated is which areas of the Bandung basin are prone to flooding. This study offers an effective and efficient method of mapping flood-prone areas based on flood events that have occurred in the past through the use of historical remote sensing image data. In this research, Landsat-8 imagery was used to observe the inundated area in the Bandung basin in the past (2014–2018) using an improved algorithm, the modified normalized water index (MNDWI). The results of the study show that MNDWI is the appropriate parameter to be used to detect flooded areas in the Bandung basin area that have heterogeneous land surface conditions. The flood-prone area was determined based on flood events for 2014 to 2018, identified as inundated areas in the images. The estimation of the flood-prone area in the Bandung basin is 11,886.87 ha. Most of the flood-prone areas are in the subdistricts of Rancaekek, Bojongsoang, Solokan Jeruk, Ciparay, Cileunyi, Bale Endah and Cikancung. This area geographically or naturally is a water habitat area. Therefore, if the area will be used for residential, this will have consequences that flood will always be a threat to the area. 

scholarly journals Mapping of Dissaster-Prone Area for Residential Area Development: Evaluation in Barabai City, Hulu Sungai Tengah District, South Kalimantan

2016 ◽  
Vol 4 (1) ◽  
Author(s):  
Rosalina Kumalawati
Keyword(s):  
Sampling Technique ◽  
Research Area ◽  
Residential Development ◽  
Residential Area ◽  
Residential Areas ◽  
The People ◽  
Prone Area ◽  
Development Evaluation ◽  
Flood Prone Area ◽  
Flood Prone Areas

Flood disaster has some influences towards residential area. The effect of flood towards residential area needs to be investigated to determine the correct allocation of residential development space for the people who live in a flood-prone area. This research aims to develop a map of flood-prone areas for the sake of residential development evaluation in Baraba district, Hulu Sungai Tengah Regency, South Borneo.The purposive sampling technique was used to select the research area. The unit analysis is some districts and included in the flood-prone areas. To determine the sample based on flood-prone level, the stratified sampling was used.The result of the research shows that most of the investigated areas prone to flood. A secure location for resident should utilize the land in accordance with the criteria of safe and appropriate settlement layout. The low-prone and safe areas can be used for the space allocation of residential areas development in order to be safer from flooding.

Flood-prone area mapping using machine learning techniques: a case study of Quang Binh province, Vietnam

2021 ◽  
Author(s):  
Chinh Luu ◽  
Quynh Duy Bui ◽  
Romulus Costache ◽  
Luan Thanh Nguyen ◽  
Thu Thuy Nguyen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Prone Area ◽  
Flood Prone Area
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