RISK-LEVEL ASSESSMENT SYSTEM ON BENGAWAN SOLO’S FLOOD PRONE AREAS USING AHP AND WEB GIS ICT-ASIA 2015 25-26 May 2015 SEARCA Los Banos Laguna Philippines HARIS RAHADIANTO ARNA FARIZA JAUARI AKHMAD NUR HASIM DEPARTMENT OF INFORMA TICS AND COM PUTER ENGINEERING ELECTRONICS ENGINEERING POLYTECHNIC INSTITUTE OF SURABAYA [email protected]
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The Impact of FloodFloods are considered to be the most common natural disasterworldwide during the last decades. Their consequences are notonly environmental but economic as well, since they may causedamages to urban areas and agricultural lands and may even
ProblemsThe national government has created a de-centralized structure toprepare and respond to disasters and climate change. However, thesestructures are often lacking and funding is frequently diverted frompreparation and mitigation to emergency response, and many lack the
organizational capability necessary to mitigate disasters.The increase in floods and their destructive results worldwide requirean ongoing improvement on identification and mapping of floodhazard. (Kundzewicz and Kaczmare 2000; Ebert et al. 2009).
IdeaBuild web-based risk-level assessment system that comprehendhazards, vulnerabilities, and capacities summarized in risk analysis andintegrated with Geographical Information System so it can map theregion based on its risk level
Flood Risk AssessmentIndicators used for semi-quantitative risk analysis will be selected basedon the suitability and availability. The formula described above stillapply, but will contain an index value not real value. In analogy HumanDevelopment Index (HDI) of the UNDP, to make the index comparable
at least in the dimensions, the index used in the analysis wereconverted into a value between 0 and 1, where 0 is the minimum valueof the original indicator, and 1 is the maximum value.
In the case of the low numbers are many and varied in amountsometimes high, will be converted logarithmic (Log10) than linear
conversion.
The core of the risk mapping methodology for a tree structure indicator,where risk index forming the root end of the analysis.
Analytic Hierarchy ProcessIn the semi-quantitative analysis, particularly about the lack of information about factors sensitivity is compensated by aweighting factor. Best weighting factors obtained throughconsensus of expert opinion. A methodology appears to a
consensus such is the Analytic Hierarchy Process (AHP). Thismethodology has been developed by Thomas L. Saaty began in1970, and was originally intended as a tool for decision-making.AHP is a measurement methodology through comparison pair-wise and depend on the judgment of experts to gain scale
priority. This is the scale that measures the relative form.Comparisons are made with using the absolute rating scale,which represents how much the indicator dominating the otherwith respect to a particular disaster.
As explained earlier, the Risk map has been prepared based on an
index of hazard, vulnerability and capacity. Modifications shouldbe made to the above formula so it can be used in semi-quantitative. Multiplication with a capacity inverted (1-C)performed, rather than division with C, to avoid high value in thecase of extreme values of the low value of C, or error in terms of
the empty values of C. The result of multiplying the index must becorrected by showing the root of 1 / n, for regains its originaldimensions
Risk-Level DivisionFurthermore, the values that were derived from were groupedinto three risk levels. This classification was done by using theoptimization method of classes distribution natural breaks (Jenks1967).
The Jenks optimization method, also called the Jenks naturalbreaks classification method, is a data classification methoddesigned to determine the best arrangement of values intodifferent classes. This is done by seeking to minimize each class’average deviation from the class mean, while maximizing each
class’ deviation from the means of the other groups. In otherwords, the method seeks to reduce the variance within classesand maximize the variance between classes.
ReferencesIndonesia Data and Information, National Disaster Management Authority(BNPB)
Merz B, Kreibich H, Schwarze R, Thieken A (2010) Assessment of economicflood damage. Nat Hazards Earth Syst Sci 10:1679 –1724
Kundzewicz W, Kaczmare Z (2000) Coping with hydrological extremes. WaterInt 25:66 –75
Ebert A, Kerle N, Stein A (2009) Urban social vulnerability assessment withphysical proxies and spatial metrics derived from air- and spaceborneimagery and GIS data. Nat Hazards 48:275 –294
Jenks G (1967) The Data Model Concept in Statistical Mapping. Int Year bCartogr 7:186 –190
General Guidelines for Disaster Risk Assessment, National DisasterManagement Authority (BNPB)