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• Sunbelt countries can develop reliable solar-dominant grids in the absence of wind. • Policymakers should be highly sceptical of low solar technical potential estimates. • Zero energy emissions Malaysia requires about 0.7 TW solar, 2 TWh pumped hydro. • Sarawak-Peninsular Malaysia interconnection reduces storage needs, but not costs. • Inclusion of about 0.4% hydrogen or synthetic methane reduces storage needs and cost. Three-quarters of the global population lives in the sunbelt region (±35o latitude) with excellent access to solar resources. Wind resources at the equator are very limited, while supply chain and permitting delays are slowing wind deployment at a global scale. This paper investigates the potential for solar photovoltaic-dominant 100% renewable grids to be developed in sunbelt countries through a case study of Malaysia. It was expected that the technical potential for solar photovoltaics and off-river pumped hydro would be far in excess of what is required to decarbonise Malaysia's electricity grids. A Geographic Information System analysis determined that Malaysia has the potential to deploy approximately 8.5 Terawatts of terrestrial photovoltaics and 25 Terawatts of marine floating solar on calm oceans. There are roughly 450 Terawatt-hours of energy storage available in the form of off-river (closed-loop) pumped hydro energy storage. A least-cost, 16-year hourly energy balance model was used to determine that an affluent, fully electrified, highly interconnected Malaysia could greatly reduce emissions through the deployment of 660 Gigawatts of photovoltaics and 2200 Gigawatt-hours of pumped hydro, supported by small amounts of hydrogen-based gas peaking. All-in costs for such a fully decarbonised energy system were modelled to be in the range of 95–103 USD per Megawatt-hour. A Sarawak-Peninsular Malaysia interconnected grid could produce a similar electricity cost to two separate grids while reducing the long-term energy storage needs by approximately 33%. Inclusion of about 3 Terawatt-hours (0.1% of average annual generation at 20 Megawatt-hours/capita/year demand) of hydrogen, synthetic methane, or legacy fossil fuels could further reduce long-term energy storage needs by about 30% by helping to ride through occasional prolonged cloudy periods. Policymakers in sunbelt countries should be highly sceptical of technical potential estimates that suggest limited area for solar photovoltaics. Sunbelt countries can comfortably proceed with the development of cheap, reliable solar-dominant grids primarily supported by pumped hydro energy storage and inter-regional transmission. [ABSTRACT FROM AUTHOR]

Abstract: Landslide-susceptibility mapping is one of the most critical issues in Malaysia. These landslides can be systematically assessed and mapped through a traditional mapping framework that uses geoinformation technologies (GIT). The main purpose of this paper is to investigate the possible application of an artificial neural network model and its cross-application of weights at three study areas in Malaysia, Penang Island, Cameron Highland and Selangor. Landslide locations were identified in the study areas from the interpretation of aerial photographs, field surveys and inventory reports. A landslide-related spatial database was constructed from topographic, soil, geology, and land-cover maps. For the calculation of the relative weight and importance of each factor to a particular landslide occurrence, an artificial neural network (ANN) method was applied. Landslide susceptibility was analyzed using the landslide occurrence factors provided by the artificial neural network model. Then, the landslide-susceptibility analysis results were validated and cross-validated using the landslide locations as study areas. Different training sites were randomly selected to train the neural network, and nine sets of landslide-susceptibility maps were prepared. The paper then illustrates the verification of those maps using an “area under the curve” (AUC) method. The verification results show that the case of the weight using the same test area showed slightly higher accuracy than the weight used for the cross-applied area. Among the three studied areas, the verification results showed similar accuracy trends while using the weight for the study area itself. Cameron showed the best accuracy and Penang showed the worst accuracy. Generally, the verification results showed satisfactory agreement between the susceptibility map and the existing data on the landslide location. [Copyright &y& Elsevier]

The process of development control involves a technique for the systematic compilation of expert quantitative analysis and qualitative assessment of project land use and property development viability, including its effect on the surrounding area, and the presentation of results in a way that enables the importance of the predicted results, and the scope of modifying or mitigating them to be properly evaluated by the relevant decision making body before a planning permission is rendered. Taking the local authorities in Malaysia as an example, this paper will discuss and demonstrate the development of a GIS database and its integration and application for development and building control. The first part of the paper examines the functions of local authorities particularly in the context of development control. In this study, the procedure involved in granting a planning permission as well as enforcement becomes the main focus because it determines the bulk of the system design. The study identifies seven sub-systems namely planning permission, building control, planning enforcement, geospatial database, information kiosk, document processing and presentation sub-systems. The system is developed using a combination of office automation, CAD, GIS, multimedia and other software packages. This paper will focus on the development of the GIS for the purpose of development control. The components of the GIS database include land information, buildings, existing developments, planning information, development plans, utilities, community facilities, transportation, environment and the socio-economy database. The GIS database will be used at every stage of development control, for example, in initial discussions, registration, invitation of objections, the development control process, the consideration by Technical Committee and the consideration by Planning Committee. It is anticipated that the GIS database could be used by many parties involved in the process as a reference point in evaluating a planning submission. Having access to the database will provide flexibility in assessing a proposed development and deciding on the overall urban growth management programme in the most cost-effective manner. The system will also provide transparency and consistency in the development control procedure. [Copyright &y& Elsevier]

• The electric bus energy consumption is calculated by using spatial–temporal model. • The varying energy losses among the grid and electric bus charging are considered. • The proposed framework can be straightforwardly applied on various bus networks. • The validation of this study is based on Bus-Rapid Kuala Lumpur data. • A tracking system is used to obtain the speed measurement of the driving mode. This work proposes a generic framework to estimate the energy consumption and investigate the penetration impact of a distributed network of battery electric buses (BEBs). The core of this work builds on a novel framework to determine the energy demand of BEBs and their potential as a replacement for diesel-powered buses in transportation networks. This paper uses data mapping technology from the Geographical Information System to cover the potential analysis of BEB penetration for large-scale bus networks. State-of-the-art methods have previously estimated the energy consumption of a BEB by using simulator models for each driving cycle, but these studies have not considered the actual elevation data of a local bus route. In fact, the elevation of the bus route is considered the main factor that varies the energy consumption of BEBs. This study developed a longitudinal dynamic model with a spatial version of a digital elevation model to determine the energy demand of a large-scale BEB network. Additionally, this work assessed two charging protocols—opportunity charging and overnight charging—according to the operating environments of electric buses. The application of the framework is validated in a case study to electrify the entire Rapid Kuala Lumpur bus (Bus-Rapid KL) network in Malaysia. The proposed model used real-world data, which are typically available only to bus transit administration operators. In this paper, the data for bus route lines, bus station locations, and the number of passengers riding the Bus-Rapid KL were considered to formulate the forecasting longitudinal and temporal model by using passenger information system data. The results showed a penetration impact of the BEB charging demand during daytime and nighttime in an urban area in Kuala Lumpur. The proposed forecasting paradigm may permit power network operators to predict the optimal electric bus charging demand based on actual BEB consumption through the bus paths. [ABSTRACT FROM AUTHOR]

This paper aims to determine the suitable places for a radioactive waste repository in Peninsular Malaysia to implement a permanent disposal facility for disposal sealed radioactive sources (DSRS). The sole Malaysia interim waste storage that stores a large proportion of DSRS was expected to be occupied by 2025 fully. Currently, the nation's licensing board promoted the borehole disposal facility to resolve the full capacity dilemma. The potential sites were determined by the geographical information system (GIS) based Weight Linear Combination (WLC) analysis, integrating the improved Analysis Hierarchical Process (improved AHP) and the technique for order of preference by similarity to ideal solution (TOPSIS) approaches. The sensitivity analysis was applied to identify the main contributing factors for corresponding Peninsular Malaysia selection. Ten parametric criteria and three exclusion criteria are applied to assess the potential areas and screen out the unfavorable regions, respectively. Results indicated the final composite map presents 435 areas of very high suitability class with 17.5% coverage from the total suitability class. The top 5 largest candidates with high potentiality are Jerantut & Ulu Lepar, Bandar Tun Razak, Pertang & Simpang, Pertang, Tanjung Ipoh, and Labis, where come from three provinces: Pahang, Negeri Sembilan and Johor. By TOPSIS model, the ranks for these five candidates are Jerantut & Ulu Lepar, Bandar Tun Razak, Tanjung Ipoh, Labis and Simpang Pertang. The model revealed that the suitability ranks is proportional to its area sizes in the approximate values of criteria. The three main contributing factors in Peninsular Malaysia are hydrogeology, seismicity and lithology, which are strongly recommended to involve in other corresponding site selection project. • Top five candidates for radioactive waste disposal site in Malaysia are Jerantut & Ulu Lepar, Bandar Tun Razak, Tanjung Ipoh, Labis and Simpang Pertang. • Three main contributing factors for site selection in Malaysia are hydrogeology, seismicity and lithology. • The suitability index rank is proportional to the area sizes in the consideration of area factor. [ABSTRACT FROM AUTHOR]

Abstract: The objective of this paper is to exploit the potential application of an evidential belief function model to landslide susceptibility mapping at Kuala Lumpur city and surrounding areas using geographic information system (GIS). At first, a landslide inventory map was prepared using aerial photographs, high resolution satellite images and field survey. A total 220 landslides were mapped and an inventory map was prepared. Then the landslide inventory was randomly split into a testing dataset 70% (153 landslides) and remaining 30% (67 landslides) data was used for validation purpose. Fourteen landslide conditioning factors such as slope, aspect, curvature, altitude, surface roughness, lithology, distance from faults, ndvi (normalized difference vegetation index), land cover, distance from drainage, distance from road, spi (stream power index), soil type, precipitation, were used as thematic layers in the analysis. The Dempster–Shafer theory of evidence model was applied to prepare the landslide susceptibility maps. The validation of the resultant susceptibility maps were performed using receiver operating characteristics (ROC) and area under the curve (AUC). The validation results show that the area under the curve for the evidential belief function (the belief map) model is 0.82 (82%) with prediction accuracy 0.75 (75%). The results of this study indicated that the EBF model can be effectively used in preparation of landslide susceptibility maps. [Copyright &y& Elsevier]

Abstract: This paper presents landslide-susceptibility mapping using an adaptive neuro-fuzzy inference system (ANFIS) using a geographic information system (GIS) environment. In the first stage, landslide locations from the study area were identified by interpreting aerial photographs and supported by an extensive field survey. In the second stage, landslide-related conditioning factors such as altitude, slope angle, plan curvature, distance to drainage, distance to road, soil texture and stream power index (SPI) were extracted from the topographic and soil maps. Then, landslide-susceptible areas were analyzed by the ANFIS approach and mapped using landslide-conditioning factors. In particular, various membership functions (MFs) were applied for the landslide-susceptibility mapping and their results were compared with the field-verified landslide locations. Additionally, the receiver operating characteristics (ROC) curve for all landslide susceptibility maps were drawn and the areas under curve values were calculated. The ROC curve technique is based on the plotting of model sensitivity — true positive fraction values calculated for different threshold values, versus model specificity — true negative fraction values, on a graph. Landslide test locations that were not used during the ANFIS modeling purpose were used to validate the landslide susceptibility maps. The validation results revealed that the susceptibility maps constructed by the ANFIS predictive models using triangular, trapezoidal, generalized bell and polynomial MFs produced reasonable results (84.39%), which can be used for preliminary land-use planning. Finally, the authors concluded that ANFIS is a very useful and an effective tool in regional landslide susceptibility assessment. [Copyright &y& Elsevier]

Abstract: The purpose of the present paper is to manifest the results of the neuro-fuzzy model using remote sensing data and GIS for landslide susceptibility analysis in a part of the Klang Valley areas i Malaysia. Landslide locations in the study area were identified by interpreting aerial photographs and satellite images, supported by extensive field surveys. SPOT 5 satellite imagery was used to map vegetation index. Maps of topography, lineaments, NDVI and land cover were constructed from the spatial datasets. Seven landslide conditioning factors such as altitude, slope angle, plan curvature, distance from drainage, soil type, distance from faults and NDVI were extracted from the spatial database. These factors were analyzed using a neuro-fuzzy model (adaptive neuro-fuzzy inference system, ANFIS) to construct the landslide susceptibility maps. During the model development works, total 5 landslide susceptibility models were obtained by using ANFIS results. For verification, the results of the analyses were then compared with the field-verified landslide locations. Additionally, the ROC curves for all landslide susceptibility models were drawn and the area under curve values was calculated. Landslide locations were used to validate results of the landslide susceptibility map and the verification results showed 98% accuracy for the model 5 employing all parameters produced in the present study as the landslide conditioning factors. The validation results showed sufficient agreement between the obtained susceptibility map and the existing data on landslide areas. Qualitatively, the model yields reasonable results which can be used for preliminary landuse planning purposes. As a conclusion, the ANFIS is a very useful tool for regional landslide susceptibility assessments. [Copyright &y& Elsevier]

Abstract: Data collection for landslide susceptibility modeling is often an inhibitive activity. This is one reason why for quite some time landslides have been described and modelled on the basis of spatially distributed values of landslide-related attributes. This paper presents landslide susceptibility analysis in the Klang Valley area, Malaysia, using back-propagation artificial neural network model. A landslide inventory map with a total of 398 landslide locations was constructed using the data from various sources. Out of 398 landslide locations, 318 (80%) of the data taken before the year 2004 was used for training the neural network model and the remaining 80 (20%) locations (post-2004 events) were used for the accuracy assessment purpose. Topographical, geological data and satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. Eleven landslide occurrence related factors were selected as: slope angle, slope aspect, curvature, altitude, distance to roads, distance to rivers, lithology, distance to faults, soil type, landcover and the normalized difference vegetation index value. For calculating the weight of the relative importance of each factor to the landslide occurrence, an artificial neural network method was developed. Each thematic layer''s weight was determined by the back-propagation training method and landslide susceptibility indices (LSI) were calculated using the trained back-propagation weights. To assess the factor effects, the weights were calculated three times, using all 11 factors in the first case, then recalculating after removal of those 4 factors that had the smallest weights, and thirdly after removal of the remaining 3 least influential factors. The effect of weights in landslide susceptibility was verified using the landslide location data. It is revealed that all factors have relatively positive effects on the landslide susceptibility maps in the study. The validation results showed sufficient agreement between the computed susceptibility maps and the existing data on landslide areas. The distribution of landslide susceptibility zones derived from ANN shows similar trends as those obtained by applying in GIS-based susceptibility procedures by the same authors (using the frequency ratio and logistic regression method) and indicates that ANN results are better than the earlier method. Among the three cases, the best accuracy (94%) was obtained in the case of the 7 factors weight, whereas 11 factors based weight showed the worst accuracy (91%). [Copyright &y& Elsevier]

Abstract: This paper presents the results of the cross-validation of a multivariate logistic regression model using remote sensing data and GIS for landslide hazard analysis on the Penang, Cameron, and Selangor areas in Malaysia. Landslide locations in the study areas were identified by interpreting aerial photographs and satellite images, supported by field surveys. SPOT 5 and Landsat TM satellite imagery were used to map landcover and vegetation index, respectively. Maps of topography, soil type, lineaments and land cover were constructed from the spatial datasets. Ten factors which influence landslide occurrence, i.e., slope, aspect, curvature, distance from drainage, lithology, distance from lineaments, soil type, landcover, rainfall precipitation, and normalized difference vegetation index (ndvi), were extracted from the spatial database and the logistic regression coefficient of each factor was computed. Then the landslide hazard was analysed using the multivariate logistic regression coefficients derived not only from the data for the respective area but also using the logistic regression coefficients calculated from each of the other two areas (nine hazard maps in all) as a cross-validation of the model. For verification of the model, the results of the analyses were then compared with the field-verified landslide locations. Among the three cases of the application of logistic regression coefficient in the same study area, the case of Selangor based on the Selangor logistic regression coefficients showed the highest accuracy (94%), where as Penang based on the Penang coefficients showed the lowest accuracy (86%). Similarly, among the six cases from the cross application of logistic regression coefficient in other two areas, the case of Selangor based on logistic coefficient of Cameron showed highest (90%) prediction accuracy where as the case of Penang based on the Selangor logistic regression coefficients showed the lowest accuracy (79%). Qualitatively, the cross application model yields reasonable results which can be used for preliminary landslide hazard mapping. [Copyright &y& Elsevier]

This paper presents a geographic information system (GIS)-based multi-criteria decision making approach for forest conservation planning at a landscape scale. This approach enables decision makers to evaluate the relative priorities of conserving forest areas based on a set of preferences, criteria and indicators for the area. Compromise programming techniques are used to integrate the forest conservation priority maps of decision groups where a separation distance is calculated. A clustering analysis was applied to identify potential conservation areas as the basis of delineating potential new protected areas. The study was conducted in the Kinabalu area, Sabah, Malaysia where two polygons neighboring the Kinabalu Park were delineated. A group of 11 polygons totaling 2050 ha has also been detected in the western part of Kinabalu Park. The study recommends the inclusion of a forest polygon (359 ha) neighboring Kinabalu Park and another (4361 ha) to the west of the park as new protected areas. A green corridor linking the potential new protected areas and Kinabalu Park should also be constructed to facilitate animal movement and interaction. This study reveals that riparian vegetation is an important aspect to forest conservation and the legislation to protect riparian zones should be strengthened. [Copyright &y& Elsevier]