Mapping of Flood Prone Area in Jakarta using Fuzzy C- Means

Author(s):  
Rahma Metrikasari ◽  
Andi Sulasikin ◽  
Yudhistira Nugraha ◽  
Farizah Rizka Rahmaniar ◽  
Andy Ernesto ◽  
...  
2021 ◽  
Author(s):  
Chinh Luu ◽  
Quynh Duy Bui ◽  
Romulus Costache ◽  
Luan Thanh Nguyen ◽  
Thu Thuy Nguyen ◽  
...  

2021 ◽  
Vol 226 ◽  
pp. 00030
Author(s):  
Muhamad Nurdin Yusuf ◽  
Agus Yuniawan Isyanto ◽  
Sudradjat Sudradjat

The research was carried out with the aim to find out the behavior of farmers towards risk and the factors that influence it. The research sample was 100 paddy farmers in flood-prone area paddy fields in Pangandaran District, West Java Province, Indonesia. Farmer’s behavior towards risk was analyzed using quadratic utility functions, while the factors that influence farmer’s behavior towards risk were analyzed using logistic regression. The results showed farmers 87 was risk neutral, while 13 farmer risk takers were farmers. Education, familys size and income significantly influence farmer’s behavior towards risk; while age, experience, land area, production risk, price risk, income risk and group did not significantly influence farmer’s behavior towards risk.


2016 ◽  
Vol 12 (1) ◽  
Author(s):  
Rahma Wayan Lestari ◽  
Indra Kanedi ◽  
Yode Arliando

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


2016 ◽  
Vol 28 (2) ◽  
pp. 91-101 ◽  
Author(s):  
TF Khan ◽  
MW Ullah ◽  
SM Imamul Huq

A study was carried out in 24 Upazillas of 12 districts of Bangladesh with a view to identifying risks, vulnerabilities and impacts of different types of natural disasters commonly occurring in Bangladesh with respect to agricultural production and sustainable agricultural development. Three districts were selected from each category of the disaster viz., drought, saline, river flood and flash flood prone. Two Upazillas were selected randomly from each district by considering the homogeneity (climate, agricultural practices, agricultural production, disaster risks, livelihood system, population, etc.) of the particular disaster affected areas. Of the four disasters, drought prone area covers the highest net cultivable area (NCA) measuring about 121 ha. Flash flood, salinity and river flood prone area covers approximately 115, 60 and 30 ha, respectively. The dominant cropping pattern in drought areas is Fallow-T.Aman-Wheat. In saline areas, it is Fallow-T. Aman- Pulse while in flood areas, it is Fallow-T.Aman- HYV Boro. Trend analysis shows that overall cropped area decreased by 1% in all disaster prone areas from 1984 - 2013. The highest decrease in cropped area was found for pulse in both drought (13%) and river flood (14%) areas. In saline and flash flood areas, it was for spice and potato, respectively. Among 72 farmers, 85% is vulnerable to drought, 90% to salinity, 69% to river flood and 95% to flash flood. Medium high land was found to be the most vulnerable for agricultural production in all disaster prone areas. Due to vulnerability to disasters, medium high land remains fallow in saline (83%), river flood (51%) and flash flood (31%) areas. In case of drought regions, medium low land (37%) remains fallow.Bangladesh J. Sci. Res. 28(2): 91-101, Dec-2015


2017 ◽  
Vol 46 (2) ◽  
pp. 150-158
Author(s):  
SMR Rahman ◽  
NR Sarker ◽  
MR Amin ◽  
M Kamruzzaman ◽  
MR Haque

An investigation was carried out with the objectives to identify the naturally occurring forage species, seasonal availability, production patterns under different climatic zones and production performance and methane emission from dairy cow under existing feeding systems. For this purpose, three different agro-climatic zones of Bangladesh, namely saline prone area (Satkhira), flood plain/river basin areas (Pabna), semi-arid/drought prone areas (Chapainobabgonj) were selected. To achieve the objectives, three Focus Group Discussions (FGD) were conducted in each location to obtain more information from different age groups of farmers. A total of 9 FDGs were conducted under three selected locations and twelve participants were attended in each FGD. During FDGs, information was collected through participatory discussions through check list and also discussion was recorded to verify the information gathered as per check list.  After collection of information in each side, all the data were checked and analyzed. The results indicated that in saline area, farmers reported that different types of local grass e.g. Tale Shapna,Durba,Nona Shapna, Khud Gate/ KhudKhachra, Shama, Full Paira, Bass Pata, Math Pora/KhataShak, GhimeeShak and Baksha etc were available round the year but according to their observation Nona Shapna, Tale Shapna and Baksha were more available compared to other species of the natural grasses and these three natural forages are more suitable in this area. In the drought prone area, different types of native grasses e.g. Durba,Shama, Mutha,Katla,Kausha/Kannar, Binna, Datuloka,Shanchi, Shunshue, Bash Batari, Ulo and Binna Pati were identified and utilized by the farmers in different seasons but Durba,Katla and Mutha were found more drought tolerant compared to other species. In flood prone area, Kolmi, Shanti, Baksha, Arail, Dubla, Bokma, Vadail and Bolenga etc were found and Kolmi, Baksha and Arail are more suitable in this area. Farmers were also reported that fodder tree like Dumur/khoksha also is survive in water logging situation and or flood prone area. The study revealed that calculated total DMI (Kg/h/day) was the highest (14.14±1.06) in flood prone  followed by drought (13.80±1.30) and saline areas (4.43±0.20),  respectively. Similarly, the milk production was also higher (12.06±1.19 litre/h/day) in flood prone area followed by drought (4.47±0.60 litre/h/day) and saline (1.83±0.11 litre/h/day) areas, respectively. The calculated total methane emission (g/h/d) was significantly higher in flood prone (478.31±36.36) and the lowest in saline (153.35±7.14) prone areas. Whereas, methane production per unit of milk yield, was the lowest in flood prone (46.55±6.78) and the highest (110.48±21.69) in drought prone area and the difference was statistically significant (p<0.05).  Therefore, it may be concluded that farmers’ rearing animals under climate vulnerable areas utilizing natural grasses are more prone to higher methane production compared to animals rearing better feed resources though their availability was varied with the seasons and locations.  Hence, further research is needed to explore more suitable natural grasses in addition to introduction of high yielding fodder with higher biomass and nutritive values based on the existing cropping systems in those climate vulnerable areas for higher milk production and low enteric methane emission in the country.Bang. J. Anim. Sci. 2017. 46 (2): 150-158


Water ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 2271
Author(s):  
Yoon Ha Lee ◽  
Hyun Il Kim ◽  
Kun Yeun Han ◽  
Won Hwa Hong

For flood risk assessment, it is necessary to quantify the uncertainty of spatiotemporal changes in floods by analyzing space and time simultaneously. This study designed and tested a methodology for the designation of evacuation routes that takes into account spatial and temporal inundation and tested the methodology by applying it to a flood-prone area of Seoul, Korea. For flood prediction, the non-linear auto-regressive with exogenous inputs neural network was utilized, and the geographic information system was utilized to classify evacuations by walking hazard level as well as to designate evacuation routes. The results of this study show that the artificial neural network can be used to shorten the flood prediction process. The results demonstrate that adaptability and safety have to be ensured in a flood by planning the evacuation route in a flexible manner based on the occurrence of, and change in, evacuation possibilities according to walking hazard regions.


2017 ◽  
Vol 12 (1) ◽  
pp. 147-157 ◽  
Author(s):  
Yukiko Tahira ◽  
◽  
Akiyuki Kawasaki ◽  

Poor and non-poor groups from two flood-prone villages in central Thailand were compared following the flood of 2011. The results showed that the damage/income ratio was higher among persons in the poor group living in old, high-pillared houses near the river. Although this group was not as well prepared and experienced less damage than the non-poor group, they had fewer resources for recovery. The study examined household history, networks, and socio-economic status, as well as the local history. The poor group’s socio-economic characteristics may limit their capacity to resettle, as they have lived in the flood-prone area for generations. Proposals to address this included improving dykes and early warning systems as well as offering compensation for lost earnings.


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