The most devastating flood event in Kota Bharu was recorded in December 2014, which affected several properties worth millions of dollars and thousands of homes. Damage to physical properties, especially buildings, is identified as a significant contributor to flood disasters in Malaysia. Therefore, it is essential to address physical flood vulnerability by developing an integrated approach for modeling buildings’ flood vulnerability to decrease the flood consequences. This study aims at developing a flood vulnerability assessment approach using an indicator-based model (IBM) for individual buildings in Kota Bahru, Kelantan, Malaysia. An intensive literature review and expert opinions were used to determine suitable indicators that contribute to the physical flood vulnerability of buildings. The indicators were grouped into three components, i.e., flood hazard intensity (I), building characteristics (C), and effect of the surrounding environment (E). The indicators were further refined based on expert opinions and Relative Importance Index (RII) analysis. Based on their contribution to the Malaysia local building flood vulnerability, priority weight is assigned by the experts to each of the selected indicators using the participatory Analytical Hierarchy Process (AHP). A spatial database of buildings in Kota Bharu is developed through field surveys and manually digitizing building footprints from satellite imageries. The identified indicators and their weight are added to each building footprint. The Weighted Linear Combination (WLC) aggregation method combined the weight of indicators into a vulnerability index and maps. The results of a physical flood vulnerability were validated using building damage information obtained through interviews with the community that experienced previous flood in the study area. The result showed that about 98% of the study area’s buildings have either moderate or low vulnerability to flooding. The flood vulnerability map has an overall accuracy of 75.12% and 0.63 kappa statistics. In conclusion, the IBM approach has been used successfully to develop a physical flood vulnerability for buildings in Kota Bharu. The model contributes to support different structural and non-structural approaches in the flood mitigations process.

The advent of satellite rainfall products can provide a solution to the scarcity of observed rainfall data. The present study aims to evaluate the performance of high spatial-temporal resolution satellite rainfall products (SRPs) and rain gauge data in hydrological modelling and flood inundation mapping. Four SRPs, Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) - Early, - Late (IMERG-E, IMERG-L), Global Satellite Mapping of Precipitation-Near Real Time (GSMaP-NRT), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks- Cloud Classification System (PERSIANN-CCS) and rain gauge data were used as the primary input to a hydrological model, Rainfall-Runoff-Inundation (RRI) and the simulated flood level and runoff were compared with the observed data using statistical metrics. GSMaP showed the best performance in simulating hourly runoff with the lowest relative bias (RB) and the highest Nash-Sutcliffe efficiency (NSE) of 4.9% and 0.79, respectively. Meanwhile, the rain gauge data was able to produce runoff with −12.2% and 0.71 for RB and NSE, respectively. The other three SRPs showed acceptable results in daily discharge simulation (NSE value between 0.42 and 0.49, and RB value between −23.3% and −31.2%). The generated flood map also agreed with the published information. In general, the SRPs, particularly the GSMaP, showed their ability to support rapid flood forecasting required for early warning of floods.

URBAN FLOOD VULNERABILITY MAPPING OF LAGOS, NIGERIA

The current trends of floods event in many countries are alarming. Hence, managing flood and the associated risk are crucial in order to reduce the loss and to be well prepared for the combined impact of urbanization and climate changes. The best approach to manage flood activities is a risk-based approach, where the vulnerability of elements at risk is reduced to a minimum. There is a significant number of studies that use an indicator-based approach for flood vulnerability assessment with focus on the macro-scale. However, this paper assesses physical flood vulnerability of buildings at micro-scale using an indicator-based method in Kota Bharu, Malaysia. The region is one of the most flood affected regions in Malaysia. Micro-scale vulnerability assessment considers damages for individual buildings at risk, rather than in aggraded manner. In this study, the methodology adopted involve the use of 1D-2D SOBEK flood modelling, the selection and weightage of indicators, development of spatial based building index and, production of building vulnerability maps. The findings demonstrate the physical pattern of flood vulnerability of buildings at a micro-scale. The approach can assist in flood management planning and risk mitigation at a local scale

Vulnerability plays an important role in risk assessment. For flood vulnerability assessment, the map and characteristics of elements-at-risk at different scales are strongly required depending on the risk and vulnerability assessment requirements. This study proposes a methodology to classify urban structure type by combining object-based image classification and different high resolution remote sensing data. In this study, a high resolution satellite image and LiDAR have been acquired over Kota Bharu, Kelantan which consists of highly heterogeneous urban structure type (UST) classes. The first stage is data pre-processing that includes orthorectification and pansharpening of Geoeye satellite image, image resampling for normalised Digital Surface Model (nDSM) and followed by image segmentation for creating meaningful objects. The second stage comprises of derivation of image features, generation of training and testing datasets, and classification of UST. The classification was based on three types of machine learning classifiers, i.e. Random Forest (RF), Support Vector Machine (SVM) and Classification and Regression Tree (CART). The results obtained from the classification processes were compared using individual omission and commission error, overcall accuracy and Kappa coefficient. The results show that Random Forest classifier with all image features achieved the highest overall accuracy (93.5%) and Kappa coefficient (0.94). This is followed by CART classifier with overall accuracy of 93.7% and Kappa coefficient of 0.92. Finally, SVM classifier produced the lowest overall accuracy and Kappa coefficient with 88.6% and 0.86, respectively. The UST classification result can be further used to assist detailed building characterisation for large scale flood vulnerability assessment.