Ahmad Rodzi Mahmud

An artificial neural network model for flood simulation using GIS: Johor River Basin, Malaysia

Flooding is one of the most destructive natural hazards that cause damage to both life and property every year, and therefore the development of flood model to determine inundation area in watersheds is important for decision makers. In recent years, data mining approaches such as artificial neural network (ANN) techniques are being increasingly used for flood modeling. Previously, this ANN method was frequently used for hydrological and flood modeling by taking rainfall as input and runoff data as output, usually without taking into consideration of other flood causative factors. The specific objective of this study is to develop a flood model using various flood causative factors using ANN techniques and geographic information system (GIS) to modeling and simulate flood-prone areas in the southern part of Peninsular Malaysia. The ANN model for this study was developed in MATLAB using seven flood causative factors. Relevant thematic layers (including rainfall, slope, elevation, flow accumulation, soil, land use, and geology) are generated using GIS, remote sensing data, and field surveys. In the context of objective weight assignments, the ANN is used to directly produce water levels and then the flood map is constructed in GIS. To measure the performance of the model, four criteria performances, including a coefficient of determination (R2), the sum squared error, the mean square error, and the root mean square error are used. The verification results showed satisfactory agreement between the predicted and the real hydrological records. The results of this study could be used to help local and national government plan for the future and develop appropriate (to the local environmental conditions) new infrastructure to protect the lives and property of the people of Johor.

Comprehensive planning and the role of SDSS in flood disaster management in Malaysia

Purpose – To present a comprehensive flood management plan for Malaysia, the various planning stages and the proponents of the plan. It is also to expound and highlight the importance of spatial information technology in the strategy and to outline the critical decision‐making at various levels of the plan.Design/methodology/approach – A review of flood disaster management aimed at providing an insight into the strategies for a comprehensive flood disaster management for Malaysia. Discussion of the framework of a spatial decision support system (SDSS) and its role in decision‐making in a comprehensive disaster management plan.Findings – Provides information about a proposed comprehensive disaster management program for Malaysia and highlight the role of SDSS in improving decision‐making. It recognizes the strength of SDSS in the collection and processing of information to speed up communication between the proponents of the disaster management program. A well‐design SDSS for flood disaster management shou...

Maximal service area problem for optimal siting of emergency facilities

Geographic information systems (GIS) have been integrated to many applications in facility location problems today. However, there are still some GIS capabilities yet to be explored thoroughly. This study utilizes the capability of GIS to generate service areas as the travel time zones in a facility location model called the maximal service area problem (MSAP). The model is addressed to emergency facilities for which accessibility is an important requirement. The objective of the MSAP is to maximize the total service area of a specified number of facilities. In the MSAP, continuous space is deemed as the demand area, thus the optimality was measured by how large the area could be served by a set of facilities. Fire stations in South Jakarta, Indonesia, were chosen as a case study. Three heuristics, genetic algorithm (GA), tabu search (TS) and simulated annealing (SA), were applied to solve the optimization problem of the MSAP. The final output of the study shows that the three heuristics managed to provide better coverage than the existing coverage with the same number of fire stations within the same travel time. GA reached 82.95% coverage in 50.60 min, TS did 83.20% in 3.73 min, and SA did 80.17% in 52.42 min, while the existing coverage only reaches 73.82%.

GIS‐grid‐based and multi‐criteria analysis for identifying and mapping peat swamp forest fire hazard in Pahang, Malaysia

Peat swamp forest fire hazard areas were identified and mapped by integrating GIS‐grid‐based and multi‐criteria analysis to provide valuable information about the areas most likely to be affected by fire in the Pekan District, south of Pahang, Malaysia. A spatially weighted index model was implemented to develop the fire hazard assessment model used in this study. Fire‐causing factors such as land use, road network, slope, aspect and elevation data were used in this application. A two‐mosaic Landsat TM scene was used to extract land use parameters of the study area. A triangle irregular network was generated from the digitized topographic map to produce a slope risk map, an aspect risk map and an elevation risk map. Spatial analysis was applied to reclassify and overlay all grid hazard maps to produce a final peat swamp forest fire hazard map. To validate the model, the actual fire occurrence map was compared with the fire hazard zone area derived from the model. The model can be used only for specific areas, and other criteria should be considered if the model is used for other areas. The results show that most of the actual fire spots are located in very high and high fire risk zones identified by the model.

Predictive modeling and mapping of Malayan sun bear (Helarctos malayanus) distribution using maximum entropy

One of the available tools for mapping the geographical distribution and potential suitable habitats is species distribution models. These techniques are very helpful for finding poorly known distributions of species in poorly sampled areas, such as the tropics. Maximum Entropy (MaxEnt) is a recently developed modeling method that can be successfully calibrated using a relatively small number of records. In this research, the MaxEnt model was applied to describe the distribution and identify the key factors shaping the potential distribution of the vulnerable Malayan Sun Bear (Helarctos malayanus) in one of the main remaining habitats in Peninsular Malaysia. MaxEnt results showed that even though Malaysian sun bear habitat is tied with tropical evergreen forests, it lives in a marginal threshold of bio-climatic variables. On the other hand, current protected area networks within Peninsular Malaysia do not cover most of the sun bears potential suitable habitats. Assuming that the predicted suitability map covers sun bears actual distribution, future climate change, forest degradation and illegal hunting could potentially severely affect the sun bear’s population.

Community preparedness for tsunami disaster: a case study

Purpose – The main objective of this study is to develop a tsunami emergency response plan for a coastal community by adopting a community‐based disaster preparedness approach.Design/methodology/approach – A multi‐strategy research design utilizing both quantitative and qualitative methods was used. The weaknesses and strengths of the different agencies involved in responding to the 2004 tsunami disaster were identified through a focus group discussion. A survey was used to assess the preparedness of the community. Tsunami awareness and education were imparted through lectures, sermons, radio talk shows, informal briefings, workshops and printed materials. Tsunami evacuation routes, safe zones, warning protocols and evacuation plans were finalized through a consultation process with the community. A tsunami evacuation plan was verified during a table‐top exercise and was tested through a drill.Findings – It is evident from the study that a community‐based approach (where the local community is taken as th...

Spatial information technology in flood early warning systems: an overview of theory, application and latest developments in Malaysia

Malaysia experiences a major flood event every three years due to the adverse effects of two monsoon seasons a year. Floods have thus become the most significant natural disaster in the country in terms of the population affected, frequency, aerial extent, financial cost and the disruption to socio‐economic activities. Many previous flood control measures have had different levels of success but have generally had little effect in reducing the problem. However, it is now understood that it is neither possible nor desirable to control floods completely. Spatial information technology is thus being increasingly recognized as the most effective approach to flood disaster management. This paper reviews the spatial information technology in flood disaster management and its application in Malaysia. Some flood forecasting systems are discussed, along with their shortcomings. The paper discusses the framework of a proposed flood early warning system for the Langat river basin that operationally couples real‐time NOAA‐AVHRR data for quantitative precipitation forecasting with hydrologically oriented GIS and a MIKE11 hydrodynamic model.

Modelling rainfall intensity from NOAA AVHRR data for operational flood forecasting in Malaysia

Many empirical studies in numerical weather prediction have been carried out that establish the relationship between top‐of‐the‐cloud brightness temperature and rainfall particularly in tropical and equatorial regions of the world. Malaysia is a tropical country that lies along the path of the north‐east and south‐west monsoon rainfall, which sometimes causes extensive flood disasters. Observations have generally shown that heavy cumulonimbus cloud formation and thunderstorms precede the usual heavy monsoon rains that cause flood disasters in the region. In this study, a model has been developed to process National Oceanic & Atmospheric Administration Advanced Very High Resolution Radiometer (AVHRR) satellite data for rainfall intensity in an attempt to improve quantitative precipitation forecasting (QPF) as input to operational hydro‐meteorological flood early warning. The thermal bands in the multispectral AVHRR data were processed for brightness temperature. Data were further processed to determine cloud height and classification performed to delineate clouds in three broad classes of low, middle, and high. A rainfall intensity of 3–12 mm h−1 was assigned to the 1‐D cloud model to determine the maximum rain rate as a function of maximum cloud height and minimum cloud model temperature at a threshold level of 235 K. The result of establishing the rainfall intensity based on top of the cloud brightness temperature was very promising. It also showed a good areal coverage that delineated areas likely to receive intense rainfall on a regional scale. With a spatial resolution of 1.1 km, data are course but provide a good coverage for an average river catchment/basin. This raises the opportunity of simulating rainfall runoff for the river catchment through the coupling of a suitable hydro‐dynamic model and GIS to provide early warning prior to the actual rainfall event.

Online 3D Terrain Visualization: A Comparison of Three Different GIS Software

In recent times, the online 3D terrain visualization has created much interest in many applications such as land information system, military, environmental, tourism and mapping science. Many systems such as CityGML, Terraserver, Google Earth and Spaceye3D have successfully adopted this technology in their system for different applications. The aim of this paper is to compare the effectiveness of developing online 3D terrain visualization by using three popular GIS Software; R2V, Arc View 3.2 and Arc GIS 9.2. In this paper, we implement the system based on the Digital Elevation Model (DEM) and Triangulated Irregular Network (TIN) for the terrain data and then overlay with satellite image. Finally, the effectiveness of online 3D terrain rendering in terms of terrain visualization quality, file size, and loading time are made to compare the quality of the systems. The results help the developers on finding the right software to be used for their online 3D terrain visualization.

Mesoscale grid rainfall estimation from AVHRR and GMS data.

Areal rainfall averages derived from rain-gauge observations suffer from limitations not only due to sampling but also because gauges are usually distributed with a spatial bias towards populated areas and against areas with high elevation and slope. For a large river basin, however, heavy rainfall in the mountain upstream can result in severe flooding downstream. In this study, cloud-indexing and cloud model-based techniques were applied to Advanced Very High Resolution Radiometer (AVHRR) and Geostationary Meteorological Satellite (GMS) imager data based on the cloud-top brightness temperature (T B) and processed for estimating mesoscale grid rainfall. This study aims to improve and refine rainfall estimation in Malaysian monsoons based on cloud model techniques for operational pre-flood forecasting using readily available near-real-time satellite data such as the National Oceanic and Atmospheric Administration (NOAA)-AVHRR and GMS imager. Rain rates between 3 and 12 mm h−1 were assigned to cloud pixels of hourly coverage AVHRR or GMS data over the Langat Basin area for the duration of the monsoon rainfall event of 27 September to 8 October 2000 in Malaysia. The observed rainfall and quantitative precipitation forecast (QPF) showed an R 2 value of 0.9028, while the observed rainfall run-off (RR; recorded) and its simulated data had an R 2 value of 0.9263 and the QPF run-off and its simulated data had an R 2 value of 0.815. The rainfall estimate was used to simulate the flood event of the catchment. The estimated rainfall over the catchment showed similar flood area coverage to the observed flood event.