No abstract

In recent years, several types of simulation and prediction models have been used within a GIS environment to determine a realistic future for urban growth patterns. These models include quantitative and spatio-temporal techniques that are implemented to monitor urban growth. The results derived through these techniques are used to create future policies that take into account sustainable development and the demands of future generations. The aim of this paper is to provide a basis for a literature review of urban Cellular Automata (CA) models to find the most suitable approach for a realistic simulation of land use changes. The general characteristics of simulation models of urban growth and urban CA models are described, and the different techniques used in the design of these models are classified. The strengths and weaknesses of the various models are identified based on the analysis and discussion of the characteristics of these models. The results of the review confirm that the CA model is one of the strongest models for simulating urban growth patterns owing to its structure, simplicity, and possibility of evolution. Limitations of the CA model, namely weaknesses in the quantitative aspect, and the inability to include the driving forces of urban growth in the simulation process, may be minimized by integrating it with other quantitative models, such as via the Analytic Hierarchy Process (AHP), Markov Chain and frequency ratio models. Realistic simulation can be achieved when socioeconomic factors and spatial and temporal dimensions are integrated in the simulation process.

The creation of an accurate simulation of future urban growth is considered one of the most important challenges in urban studies that involve spatial modeling. The purpose of this study is to improve the simulation capability of an integrated CA-Markov Chain (CA-MC) model using CA-MC based on the Analytical Hierarchy Process (AHP) and CA-MC based on Frequency Ratio (FR), both applied in Seremban, Malaysia, as well as to compare the performance and accuracy between the traditional and hybrid models. Various physical, socioeconomic , utilities, and environmental criteria were used as predictors, including elevation, slope, soil texture, population density, distance to commercial area, distance to educational area, distance to residential area, distance to industrial area, distance to roads, distance to highway, distance to railway, distance to power line, distance to stream, and land cover. For calibration, three models were applied to simulate urban growth trends in 2010; the actual data of 2010 were used for model validation utilizing the Relative Operating Characteristic (ROC) and Kappa coefficient methods Consequently, future urban growth maps of 2020 and 2030 were created. The validation findings confirm that the integration of the CA-MC model with the FR model and employing the significant driving force of urban growth in the simulation process have resulted in the improved simulation capability of the CA-MC model. This study has provided a novel approach for improving the CA-MC model based on FR, which will provide powerful support to planners and decision-makers in the development of future sustainable urban planning.

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.