FLOOD SUSCEPTIBILITY MODELING USING ANALYTIC NETWORK PROCESS AND GEOSPATIAL TECHNOLOGY

• Main Author: LAWAL, UMAR DANO
• Format: Thesis
• Language: English
• Published: 2014
• Subjects: TA Engineering (General). Civil engineering (General)
• Online Access: http://utpedia.utp.edu.my/id/eprint/22477/1/2013%20-CIVIL%20-%20FLOOD%20SUSCEPTIBILITY%20MODELING%20USING%20ANALYTIC%20NETWORK%20PROCESS%20%26%20GEOSPATIAL%20TECHNOLOGY%20-%20LAWAL%20UMAR%20DANO.pdf; http://utpedia.utp.edu.my/id/eprint/22477/

Flood forecasting involves the use of spatial physical information and information on decision maker's preferences. This research integrates Geographic Information System (GIS) with appropriate Multi-Criteria Decision Analysis (MCDA) technique known as Analytic Network Process (ANP) model and remote sensing to produce a robust spatial flood forecasting model and to overcomes the drawbacks of Analytic Hierarchy Process (AHP) model, which was criticized on the issue of its rank reversal and for assuming criteria and alternatives to be independent which rarely occurs in real life situation. Till date, there is a dearth of published reference materials that utilizes GIS-based ANP model and remote sensing in forecasting flood susceptible zones so as to give modelers a reliable flood forecasting tool. This research bridges this gap by developing a hybrid of GIS-based ANP and remote sensing for flood forecasting. The ANP mathematical model was used to calculate weights for the various flood influencing factors/criteria. This involved the elicitation of experts' preferences via ANP survey questionnaires. The outcomes from this process were integrated into the GIS environment using a loose coupling approach. The flood susceptible zones were subsequently simulated using the ArcGIS spatial analyst functionalities. Results from the ANP model revealed the Very Highly Susceptible to Flooding (VHSF) areas to fanned 38.4% (30924.612ha) of the total area. The results were further verified using One At a Time (OAT) sensitivity analysis in order to check its stability; where six out of the twenty two scenarios correlated with original simulated spatial flood forecasting model produced.