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Oil pollution is one of the most important issues affecting the marine coastal environments all over the world. There are a large number of organizations to propose plans for managing the problems that coastal areas face, including various methods to combat the oil spills. This project proposes to achieve a decision support system (DSS) to assist the users/managers to choose the most suitable method for combating oil spills, according to the coastal area sensitivity. In this regard and in order to build an appropriate DSS model, some relevant documents regarding oil spills and spills management strategies, GIS‐based modeling, and DSS planning were reviewed, some of which are referred to here.

This study integrates high spatial resolution remote sensor data with geographic information system (GIS) data and multi‐criteria analysis to develop a methodology to model disaster risk for flood risk management and in peat swamp forest fires in order to assist in providing decision support systems for emergency operations and disaster prevention. Landslides are the result of a wide variety of processes, including geological, geomorphological and meteorological factors. Spatial technology has the ability to assess and estimate regions of landslide hazard by creating thematic maps and overlapping them to produce a final hazard map which classifies regions according to three categories of risk.

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.

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.

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 should present a balance among capabilities of dialog, data and modeling.

The study has outlined the links and components of SDSS and not its development processes; this may limit the used of this paper in in‐depth study of the development if SDSS. Some source for detail study of the development of SDSS have, however, been cited.

This paper is a very useful source of information about the preparation of a comprehensive disaster management program. It also sheds light on the role of SDSS in improving and speeding up communication between the various proponents of the program. Researcher and students will fine, it provides general guidelines and framework for disaster planning and management.

This paper fulfills flood disaster study need for developing a comprehensive disaster management program. It presents the framework of SDSS, the interrelationship between their various components and how they play a role in decision‐making.

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.

Remote sensing data and GIS techniques have been used to create thematic maps for assessment and estimation of landslide hazards, in Pos Slim‐Cameron Highlands area, Peninsula Malaysia. The Landsat TM5 scene was used to extract land use parameter of the study area. The digital elevation model (DEM) was generated from digitised topographic maps to produce slope risk map, aspect risk map and height risk map. From these data, a simple algorithm is created to classify the area into different risk zones. By overlaying all hazard maps, a final hazard map is produced. The integration of GIS with remotely sensed data might greatly facilitate classifying landslide areas to three categories; low risk, medium risk and high risk.

The purpose of this paper is to show that satellite data applicability for landslides studies is given concentration in tropical regions, which have two limitations; regular cloud cover and thick vegetation.

Landslide studies have three categories: mapping, zonation, and monitoring. High spatial resolution images are convenient for mapping. Since the slope and slope materials are the dominant parameters for slide potential, a high resolution DEM produced from the above data with classification of multispectral data will be vital for zonation. Weather‐free and penetration are advantages that make radar images essential for monitoring.

A composition of satellite data with support of aerial photography, with its high spatial resolution, will give an excellent spatial database for these studies.

Satellite remote sensing data are applicable for landslides studies in non‐accessible mountainous tropical regions.

In GIS applications for a realistic representation of a terrain a great number of triangles are needed that ultimately increases the data size. For online GIS interactive programs it has become highly essential to reduce the number of triangles in order to save more storing space. Therefore, there is need to visualize terrains at different levels of detail, for example, a region of high interest should be in higher resolution than a region of low or no interest. Wavelet technology provides an efficient approach to achieve this. Using this technology, one can decompose a terrain data into hierarchy. On the other hand, the reduction of the number of triangles in subsequent levels should not be too small; otherwise leading to poor representation of terrain.

This paper proposes a new computational code (please see Appendix for the flow chart and pseudo code) for triangulated irregular network (TIN) using Delaunay triangulation methods. The algorithms have proved to be efficient tools in numerical methods such as finite element method and image processing. Further, second generation wavelet techniques popularly known as “lifting schemes” have been applied to compress the TIN data.

A new interpolation wavelet filter for TIN has been applied in two steps, namely splitting and elevation. In the splitting step, a triangle has been divided into several sub‐triangles and the elevation step has been used to “modify” the point values (point coordinates for geometry) after the splitting. Then, this data set is compressed at the desired locations by using second generation wavelets.

A new algorithm for second generation wavelet compression has been proposed for TIN data compression. The quality of geographical surface representation after using proposed technique is compared with the original terrain. The results show that this method can be used for significant reduction of data set.