ORCID Profile
0000-0002-7899-4469
Current Organisation
Universiti Putra Malaysia
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Publisher: Springer Science and Business Media LLC
Date: 16-04-2018
Publisher: Springer Singapore
Date: 13-05-2019
Publisher: Copernicus GmbH
Date: 30-03-2015
Abstract: Abstract. Modelling and classification difficulties are fundamental issues in natural hazard assessment. A geographic information system (GIS) is a domain that requires users to use various tools to perform different types of spatial modelling. Bivariate statistical analysis (BSA) assists in hazard modelling. To perform this analysis, several calculations are required and the user has to transfer data from one format to another. Most researchers perform these calculations manually by using Microsoft Excel or other programs. This process is time-consuming and carries a degree of uncertainty. The lack of proper tools to implement BSA in a GIS environment prompted this study. In this paper, a user-friendly tool, bivariate statistical modeler (BSM), for BSA technique is proposed. Three popular BSA techniques, such as frequency ratio, weight-of-evidence (WoE), and evidential belief function (EBF) models, are applied in the newly proposed ArcMAP tool. This tool is programmed in Python and created by a simple graphical user interface (GUI), which facilitates the improvement of model performance. The proposed tool implements BSA automatically, thus allowing numerous variables to be examined. To validate the capability and accuracy of this program, a pilot test area in Malaysia is selected and all three models are tested by using the proposed program. Area under curve (AUC) is used to measure the success rate and prediction rate. Results demonstrate that the proposed program executes BSA with reasonable accuracy. The proposed BSA tool can be used in numerous applications, such as natural hazard, mineral potential, hydrological, and other engineering and environmental applications.
Publisher: Springer Science and Business Media LLC
Date: 08-05-2015
Publisher: IEEE
Date: 12-2012
Publisher: MDPI AG
Date: 16-07-2017
DOI: 10.3390/APP7070730
Publisher: MDPI AG
Date: 07-08-2019
DOI: 10.3390/S19163451
Abstract: Cameron Highland is a popular tourist hub in the mountainous area of Peninsular Malaysia. Most communities in this area suffer frequent incidence of debris flow, especially during monsoon seasons. Despite the loss of lives and properties recorded annually from debris flow, most studies in the region concentrate on landslides and flood susceptibilities. In this study, debris-flow susceptibility prediction was carried out using two data mining techniques Multivariate Adaptive Regression Splines (MARS) and Support Vector Regression (SVR) models. The existing inventory of debris-flow events (640 points) were selected for training 70% (448) and validation 30% (192). Twelve conditioning factors namely elevation, plan-curvature, slope angle, total curvature, slope aspect, Stream Transport Index (STI), profile curvature, roughness index, Stream Catchment Area (SCA), Stream Power Index (SPI), Topographic Wetness Index (TWI) and Topographic Position Index (TPI) were selected from Light Detection and Ranging (LiDAR)-derived Digital Elevation Model (DEM) data. Multi-collinearity was checked using Information Factor, Cramer’s V, and Gini Index to identify the relative importance of conditioning factors. The susceptibility models were produced and categorized into five classes not-susceptible, low, moderate, high and very-high classes. Models performances were evaluated using success and prediction rates where the area under the curve (AUC) showed a higher performance of MARS (93% and 83%) over SVR (76% and 72%). The result of this study will be important in contingency hazards and risks management plans to reduce the loss of lives and properties in the area.
Publisher: Trans Tech Publications, Ltd.
Date: 12-2013
DOI: 10.4028/WWW.SCIENTIFIC.NET/AMR.610-613.2100
Abstract: Sediments are principal carriers of the trace elements in the hydrosphere. Properties of the sediment (such as grain size, specific surface area and pore volume) decide the concentration level of the pollutant contain in water. The properties of sediment differed in each lake according to the normal geological phenomenon and source of discharge wastewater. The purpose of this study is to investigate the properties and contamination level of the sediment collected from lakes in Universiti Putra Malaysia (UPM). Sediment s les are taken from three different lakes Lake IT, Lake ENG and Lake PK. These locations are selected due to the type of wastewater has been discharged into the lakes which are from colleges and academic buildings. The sediments were tested in terms of physical, chemical properties and contaminant concentration (Pb, P and Cu). Using the contaminant concentration results the sediment concentration level of the pollutant of each lakes were referred to the Consensus- Based Sediment Quality Guidelines (CBSQG-2003). The dominant grain sizes of the sediments were found in the range of silt/clay with the size fraction in the range 12.74% to 12.83%. The specific surface areas of sediments were in the range of 16.3 to 22.5 m2/g with a pore size distribution in the range of 20 to 29 mm3/g. The chemical properties show that the pH values are in normal range pH 7, TOC values in the range of 10.84 to 12.39% and salinity values in the range of 0.05 to 0.06 dS/m. The contaminant concentrations show that the main heavy metal presents in Lake IT, Lake ENG and Lake PK as Lead (Pb) with 0.033 mg/l, 0.036 mg/L and 0.038 mg/L, respectively. According to the CBSQG-2003, due to the concentration of Lead presents in lakes sediment in UPM area, the sediments were categorised as non-polluted.
Publisher: Informa UK Limited
Date: 23-11-2017
Publisher: MDPI AG
Date: 07-05-2019
DOI: 10.3390/S19092107
Abstract: In some parts of tropical Africa, termite mound locations are traditionally used to site groundwater structures mainly in the form of hand-dug wells with high success rates. However, the scientific rationale behind the use of mounds as prospective sites for locating groundwater structures has not been thoroughly investigated. In this paper, locations and structural features of termite mounds were mapped with the aim of determining the aquifer potential beneath termite mounds and comparing the same with adjacent areas, 10 m away. Soil and species s ling, field surveys and laboratory analyses to obtain data on physical, hydraulic and geo-electrical parameters from termite mounds and adjacent control areas followed. The physical and hydraulic measurements demonstrated relatively higher infiltration rates and lower soil water content on mound soils compared with the surrounding areas. To assess the aquifer potential, vertical electrical soundings were conducted on 28 termite mounds sites and adjacent control areas. Three (3) important parameters were assessed to compute potential weights for each Vertical Electrical Sounding (VES) point: Depth to bedrock, aquifer layer resistivity and fresh/fractured bedrock resistivity. These weights were then compared between those of termite mound sites and those from control areas. The result revealed that about 43% of mound sites have greater aquifer potential compared to the surrounding areas, whereas 28.5% of mounds have equal and lower potentials compared with the surrounding areas. The study concludes that termite mounds locations are suitable spots for groundwater prospecting owing to the deeper regolith layer beneath them which suggests that termites either have the ability to locate places with a deeper weathering horizon or are themselves agents of biological weathering. Further studies to check how representative our study area is of other areas with similar termite activities are recommended.
No related grants have been discovered for zainuddin md yusoff.