Ercan A.,University of California at Davis |
Bin Mohamad M.F.,National Hydraulic Research Institute of Malaysia |
Kavvas M.L.,University of California at Davis
Hydrological Processes | Year: 2013
The sea level change along the Peninsular Malaysia and Sabah-Sarawak coastlines for the 21st century is investigated along the coastal areas of Peninsular Malaysia and Sabah-Sarawak because of the expected climate change during the 21st century. The spatial variation of the sea level change is estimated by assimilating the global mean sea level projections from the Atmosphere-Ocean coupled Global Climate Model/General Circulation Model (AOGCM) simulations to the satellite altimeter observations along the subject coastlines. Using the assimilated AOGCM projections, the sea level around the Peninsular Malaysia coastline is projected to rise with a mean in the range of 0.066 to 0.141m in 2040 and 0.253m to 0.517m in 2100. Using the assimilated AOGCM projections, the sea level around Sabah-Sarawak coastlines is projected to rise with a mean in the range of 0.115m to 0.291m in 2040 and 0.432m to 1.064m in 2100. The highest sea level rise occurs at the northeast and northwest regions in Peninsular Malaysia and at north and east sectors of Sabah in Sabah-Sarawak coastline. © 2012 John Wiley & Sons, Ltd..
Karimi-Googhari S.,Shahid Bahonar University of Kerman |
Feng H.Y.,National Hydraulic Research Institute of Malaysia |
Ghazali A.H.B.,University Putra Malaysia |
Shui L.T.,University Putra Malaysia
Pertanika Journal of Science and Technology | Year: 2010
Proper integrated management of a dam reservoir requires that all components of the water resource system be known. One of these components is the daily reservoir inflow which is the subject matter of this study, i.e. to establish predictions of what is coming in the next rainfall-runoff process over a catchment. The transformation of rainfall into runoff is an extremely complex, dynamic, and more of a non-linear process. The available six-year average daily rainfall data across the Sembrong dam catchment were computed using the well-known Theissen's polygon method. Daily reservoir inflow data were extracted by applying the water balance model to the Sembrong dam reservoir. Modelling of relationship between rainfall and reservoir inflow data was done using feed-forward back-propagation neural networks. The final selected model has one hidden layer with 11 neurons in the hidden layer. The selected model was applied for an independent data series testing. Results in relation to specific climatic and hydrologic properties of a small tropical catchment suggested that the model is suitable to be used in forecasting the next day's reservoir inflow. The efficiencies of the model Abtained indicated the validity of using the neural network for modelling reservoir inflow series. © Universiti Putra Malaysia Press.
Abdullah M.F.,National Hydraulic Research Institute of Malaysia |
Ahmad K.,National University of Malaysia
Proceedings - 5th International Conference on Electrical Engineering and Informatics: Bridging the Knowledge between Academic, Industry, and Community, ICEEI 2015 | Year: 2015
Business Intelligence plays an important role in the organization for collecting, integrating, analyzing and transforming data to be useful for effective decision making process. Nowadays, organizations are flooded with various kinds of unstructured data such as e-mail, images, reports, maps, charts, publications. An effective and efficient business model of these data could help in decision making. Currently, there is no study done on the business intelligence model for managing unstructured data that can fulfil the organization needs. Therefore, the purpose of this paper is to improve the organization's business intelligence process through the exploitation of unstructured data that is owned by the organization. In this study, unstructured data are classified, enriched and complemented with diversity of data through the process of creating metadata for each unstructured data. Four main processes are proposed to transform unstructured data to structured data which are extraction, classification, storage and mapping of data classes. Each process and its activities are combined to produce an effective and efficient business intelligence model for unstructured data management. This model helps in generating new data and information that is more comprehensive and collective to help business intelligence through advanced analysis, decision-making process and planning new research areas. Output from this study is to make unstructured data as renewable assets that is easily accessible and used as a reference and foundation in business intelligence and decision making process. © 2015 IEEE.
Sharip Z.,University of Western Australia |
Sharip Z.,National Hydraulic Research Institute of Malaysia |
Hipsey M.R.,University of Western Australia |
Schooler S.S.,University of Wisconsin-Superior |
Hobbs R.J.,University of Western Australia
International Journal of Design and Nature and Ecodynamics | Year: 2012
This paper examines the spatial patterns of water exchange based on water temperature variation between littoral and pelagic zones and compares the patterns in a series of shallow lakes at different water levels. Exchange patterns were assessed by developing isotherms along the transects and estimating the surface energy budget using the vertical temperature profi le and time-series measurements. Our results indicate the presence of density-driven fl ow induced by the differential temperature gradient between littoral areas, which are dominated by either fl oating-leaved or submerged vegetation, and the open pelagic region. Persistent stratifi cation was noted in the narrower lakes, which was thought to be due to the presence of dense submerged vegetation that attenuate wind-driven turbulence. In addition, variation of thermal stratifi cation and mixing dynamics between these lakes at different water levels has corresponding effects on the biological and chemical regimes. The circulation contributes to increased transport of the phosphate that could favour submerged species and subsequently induce shifts of macrophyte community composition. The results of this study have implications for the rehabilitation and management of lake ecosystems. © 2012 WIT Press.
Abd Manaf S.,University Putra Malaysia |
Mustapha N.,University Putra Malaysia |
Sulaiman M.N.,University Putra Malaysia |
Husin N.A.,University Putra Malaysia |
Abdul Hamid M.R.,National Hydraulic Research Institute of Malaysia
ACRS 2015 - 36th Asian Conference on Remote Sensing: Fostering Resilient Growth in Asia, Proceedings | Year: 2015
Shoreline extraction is important to identify exact land and water boundary of a country. However, it is difficult and time consuming for a large region when using traditional ground survey techniques. Alternatively, by using remote sensing for extracting shoreline is rapid and highly accurate thus minimizing the mapping errors. Although Google Map and Google Earth are open freely for public, they are not suitable to extract shoreline due to some important spectral information have been remove out. The problem in shoreline detection is the difficulty of extraction according to hydrodynamic condition of coastal area such as tides, current, etc. that leads to low accuracy rate. In extracting shoreline, remotely sensed images could be analyzed by using satellite image processing techniques. By using fusion of multispectral and SAR images, shoreline could be extracted with a higher accuracy rate.