The main goal of this study is to investigate the application of the probabilistic-based frequency ratio (FR) model in groundwater potential mapping at Langat basin in Malaysia using geographical information system. So far, the approach of probabilistic frequency ratio model has not yet been used to delineate groundwater potential in Malaysia. Moreover, this study includes the analysis of the spatial relationships between groundwater yield and various hydrological conditioning factors such as elevation, slope, curvature, river, lineament, geology, soil, and land use for this region. Eight groundwater-related factors were collected and extracted from topographic data, geological data, satellite imagery, and published maps. About 68 groundwater data with high potential yield values of ≥11 m3/h were randomly selected using statistical software of SPSS. Then, the groundwater data were randomly split into a training dataset 70 % (48 borehole data) for training the model and the remaining 30 % (20 borehole data) was used for validation purpose. Finally, the frequency ratio coefficients of the hydrological factors were used to generate the groundwater potential map. The validation dataset which was not used during the FR modeling process was used to validate the groundwater potential map using the prediction rate method. The validation results showed that the area under the curve for frequency model is 84.78 %. As far as the performance of the FR approach is concerned, the results appeared to be quite satisfactory, i.e., the zones determined on the map being zones of relative groundwater potential. This information could be used by government agencies as well as private sectors as a guide for groundwater exploration and assessment in Malaysia.

he purpose of the present study is to compare the prediction performances of three different approaches such as decision tree (DT), support vector machine (SVM) and adaptive neurofuzzy inference system (ANFIS) for landslide susceptibility mapping at Penang Hill area, Malaysia. The necessary input parameters for the landslide susceptibility assessments were obtained from various sources. At first, landslide locations were identified by aerial photographs and field surveys and a total of 113 landslide locations were constructed. The study area contains 340,608 pixels while total 8403 pixels include landslides. The landslide inventory was randomly partitioned into two subsets: (1) part 1 that contains 50% (4000 landslide grid cells) was used in the training phase of the models; (2) part 2 is a validation dataset 50% (4000 landslide grid cells) for validation of three models and to confirm its accuracy. The digitally processed images of input parameters were combined in GIS. Finally, landslide susceptibility maps were produced, and the performances were assessed and discussed. Total fifteen landslide susceptibility maps were produced using DT, SVM and ANFIS based models, and the resultant maps were validated using the landslide locations. Prediction performances of these maps were checked by receiver operating characteristics (ROC) by using both success rate curve and prediction rate curve. The validation results showed that, area under the ROC curve for the fifteen models produced using DT, SVM and ANFIS varied from 0.8204 to 0.9421 for success rate curve and 0.7580 to 0.8307 for prediction rate curves, respectively. Moreover, the prediction curves revealed that model 5 of DT has slightly higher prediction performance (83.07), whereas the success rate showed that model 5 of ANFIS has better prediction (94.21) capability among all models. The results of this study showed that landslide susceptibility mapping in the Penang Hill area using the three approaches (e.g., DT, SVM and ANFIS) is viable. As far as the performance of the models are concerned, the results appeared to be quite satisfactory, i.e., the zones determined on the map being zones of relative susceptibility.

Abstract:The aim of this study is to test and compare two probabilistic based models (frequency ratio and weightsof-evidence) with regard to regional gold potential mapping at Kelantan, Malaysia. Until now these models have not been used for the purpose of mapping gold potential areas in Malaysia. This study analyzed the spatial relationship between gold deposits and geological factors such as lithology, faults, geochemical and geophysical data in geographical information system (GIS) software. About eight (8) gold deposits and five (5) related factors are identified and quantified for their spatial relationships. Then, all factors were combined to generate a predictive gold potential map. The predictive maps were then validated by comparing them with known gold deposits using receiver operating characteristics (ROC) and "area under the curve" (AUC) graphs. The results of validation showed accuracies of 80% for the frequency ratio and 74% for the weightsof-evidence model, respectively. The results demonstrated the usefulness of frequency ratio and weights-of-evidence modeling techniques in mineral exploration work to discover unknown gold deposits in Kelantan, Malaysia.

Purpose-The primary aim of this research is to investigate the application of open source geographic information system software, geographical resources analysis support system (GRASS) for landslide hazard assessment. Design/methodology/approach-Five parameters affecting landslide occurrence derived from topographical, geological and land use maps of Cameron highland were used for the assessment. Findings-The results showed that about 93 percent of the study area falls under zone II that is of low hazard, with less than 7 percent on zone III with moderate hazard and only less than 1 percent falls under zone IV, which is of high hazard. Research limitations/implications-The accuracy of the landslide hazard map needs to be assessed by cross-correlation with landslide occurrence in the field. Practical implications-The map produced showed the potential application of GRASS as a tool for producing landslide hazard assessment map. Originality/value-The major outcome of this research is the possible use of open source GIS software in the application of landslide hazard assessment. The capability of GRASS in performing such environmental assessment will certainly attract many researchers and organizations with limited budgets, especially in developing countries such as Malaysia.

PurposeThe purpose of this paper is to utilise the interactive view capability of the geographical information system (GIS) for the geological interpretation in Klang Valley, Malaysia.Design/methodology/approachTopographical map scale of 1:10 000 was used to generate digital elevation model (DEM). The geological map was draped over the DEM to create a 3D perspective view. The geological interpretation was undertaken using the 3D capability of the GIS software.FindingsFrom the study, five lineaments which could possibly be the newly identified faults and one lithological boundary have been delineated.Research limitations/implicationsAlthough these findings need to be rechecked in the field, they show the capability of the DEM application in structural geology interpretation.Practical implicationsThe results obtained from this study demonstrate the capability of utilising a geological map draped over DEM for structural geological interpretation. Thus the technique may increase the interpretation accuracy.Originality/valueThe major outcome of this research is the possible use of DEM in the application of geological study.