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11 Using neural networks and GIS to forecast land use changes: a Land Transformation Model GIS : Bryan C. Pijanowski, Daniel G. Brown, Bradley A. Shellito, Gaurav A. Manik Slide 2 2 1. Introduction3. Methods5. Conclusions 4. Results and discussion 2. Background Slide 3 3 Introduction This paper illustrates how combining geographic information systems (GIS) and artificial neural networks (ANNs) can aid in the understanding the complex process of land use change. A GIS-based Land Transformation Model (LTM) to forecast land use change over large regions. Slide 4 4 Background ANNs(Artificial Neural Networks) ANNs were developed to model the brains interconnected system of neurons so that computers could be made to imitate the brains ability to sort patterns and learn from trial and error, thus observing relationships in data. Slide 5 5 Background A. simple perceptron B. The multi-layer perceptron (MLP) classifying linearly separable data and performing linear functions The MLP consists of three layers: input, hidden, and output Slide 6 Background GIS(Geographic Information System) Geographic Information System is an advanced computer software technology. It is a variety of spatial information collection, storage, analysis and visualization of information processing and management system. In the international study on land use change, mainly in support of GIS through remote sensing images of different periods or land-use diagram space Diejia operation, obtained the land use types during the transfer matrix, and then analyzes the status of land use change. Slide 7 Background Models of land use change Evaluating the influence of alternative policies and management regimes on land use exploring the various mechanisms by which land use change occurs and the social, economic, and spatial variables projecting potential future environmental and economic impacts of land use change 7 Slide 8 8 Background The LTM follows four sequential steps The LTM follows four sequential steps (1) processing/ coding of data to create spatial layers of predictor variables (2) the use of integrated artificial neural network predictors can be drawn from a "change in probability assessment " map (3) all the variables of time-based variable to distinguish between the possibility of a future model of land use (4) The models were tested Slide 9 Background LTM has many factors,such as political, administrative, economic, cultural, human behavior and the environment, small roads, residential streets, rivers, lakes and so on. LTM based on GIS technology is used to predict the large regional scale land use change. It uses a large number of socio-economic, political and environmental data and other information as the basis for the social, economic, political, ecological environment, land planners and resource managers to provide the necessary information. Slide 10 Background To recapitulate, LTM model in the following aspect is a powerful tool: (1)When the social, economic and spatial variables driving the land use change occurs, the detection of a variety of mechanisms. (2) To predict the future potential for land use change. (3) Assessment of the government management system and policies on land use and development patterns. Slide 11 Methods Study area and data sources Study area and data sources Michigans Grand Traverse Bay Watershed (GTBW) was selected as the test site for this project. The GTBW, located in the northwestern portion of Michigans Lower Peninsula, is one of the most rapid population growth and land use change regions in the USA. Michigans Grand Traverse Bay Watershed (GTBW) was selected as the test site for this project. The GTBW, located in the northwestern portion of Michigans Lower Peninsula, is one of the most rapid population growth and land use change regions in the USA. From 1970 to 1997, resident population in the watershed nearly doubled. Traverse City, with a resident population of approximately 18,000 (oftentimes having a seasonal tourist population exceeding 500,000) is the largest city in the watershed. From 1970 to 1997, resident population in the watershed nearly doubled. Traverse City, with a resident population of approximately 18,000 (oftentimes having a seasonal tourist population exceeding 500,000) is the largest city in the watershed. 11 Slide 12 Methods Map of Michigans Grand Traverse Bay Watershed counties and important locations within the watershed. Map of Michigans Grand Traverse Bay Watershed counties and important locations within the watershed. 12 Slide 13 Methods 13 Slide 14 Methods GIS-based predictor variables GIS-based predictor variables 14 Slide 15 Methods Maps of the 10-predictor variables used for the training exercise. Maps of the 10-predictor variables used for the training exercise. Ten predictor variables and the exclusion zones were compiled in Arc/Info Grid format using the LTM GIS Avenue interface. format (Table 1; Fig. 3) using the LTM GIS Avenue interface. format (Table 1; Fig. 3) using the LTM GIS Avenue interface. 15 Slide 16 Methods Maps of the 10-predictor variables used for the training exercise. Maps of the 10-predictor variables used for the training exercise. 16 Slide 17 Methods Maps of the 10-predictor variables used for the training exercise. Maps of the 10-predictor variables used for the training exercise. 17 Slide 18 Methods Maps of the 10-predictor variables used for the training exercise. Maps of the 10-predictor variables used for the training exercise. 18 Slide 19 Methods Maps of the 10-predictor variables used for the training exercise. Maps of the 10-predictor variables used for the training exercise. 19 Slide 20 Methods ANN-based integration ANN-based integration ANNs were applied to the prediction of land use change in four phases: ANNs were applied to the prediction of land use change in four phases: 20 (1) Use of GIS spatial data layer as the input layer ANNs (2) The use of small areas of historical data as the sample data (3) The extended network, all of the study area using historical data (4) The use of information obtained predictions Slide 21 Methods LTM application of the model is broadly as follows: The re-classification of land types with the code, and information and data of the drawings after the available information regarding the transport network, rivers, lakes, coastline location, etc. As a combination of GIS- based LTM model input data. Slide 22 Methods In the establishment of GIS-based land use change model, based on past historical data of a large number of mass analysis, we can see land use change and population trends; then the impact of land use in the population, political, economic, transportation and other elements of graph Overlay analysis to determine the elements of the new integrated impact of land use. The conclusions of the data will be generated as the ANNs spatial data layer input data to predict. Slide 23 Methods An overlay of model predictions and observed changes in an area southwest of Traverse City in Grand Traverse County. An overlay of model predictions and observed changes in an area southwest of Traverse City in Grand Traverse County. 23 Slide 24 Results and discussion Watershed-scale land use projections Watershed-scale land use projections These projections illustrate how the ANN could be trained on relationships between urbanization and all of the predictor variables that occurred in Grand Traverse These projections illustrate how the ANN could be trained on relationships between urbanization and all of the predictor variables that occurred in Grand Traverse County and, through our approach, applied to the same predictor variables scaled to a larger region to provide reasonable results for these counties in the watershed. County and, through our approach, applied to the same predictor variables scaled to a larger region to provide reasonable results for these counties in the watershed. 24 Slide 25 Results and discussion This Fig. shows the results of this regional forecast of land use changes This Fig. shows the results of this regional forecast of land use changes 25 Slide 26 Results and discussion Land use change prediction is the use of many different periods to obtain source of information on their comprehensive analysis and comparison, based on the changes that change the type of region and it is a data- based learning and analysis process, which is in line with ANN technology features. ANN analysis of information processing capabilities of the GIS to make up for the lack of dynamic data analysis. It can be developed based on historical data to a certain variation of induction, and then to predict. Slide 27 Conclusions We made several assumptions in order to keep the model simple: We made several assumptions in order to keep the model simple: (1) the pattern of each predictor variable remained constant beyond 1990. (1) the pattern of each predictor variable remained constant beyond 1990. (2) spatial rules used to build the interactions between the predictor cells and potential locations for transition are assumed to be correct and remain constant over time. (2) spatial rules used to build the interactions between the predictor cells and potential locations for transition are assumed to be correct and remain constant over time. (3) the neural network itself was assumed to remain constant over time. (3) the neural network itself was assumed to remain constant over time. (4) the amount of urban per capita (4) the amount of urban per capita undergoing a transition is assumed to be fixed over time. undergoing a transition is assumed to be fixed over time. 27 Slide 28 Conclusions Changes using GIS and ANN to ANN forecasting method takes advantage of the high degree of complexity of mapping ability and Strong self-organizing and adaptive learning capacity ability to multi-source data fusion, detection accuracy and efficiency in a greater increase. Changes using GIS and ANN to ANN forecasting method takes advantage of the high degree of complexity of mapping ability and Strong self-organizing and adaptive learning capacity ability to multi-source data fusion, detection accuracy and efficiency in a greater increase. During the network training, the use of existing GIS data- aided training samples Selected to achieve the automation of sample points on the part of the selection of training samples can improve the efficiency of selection. During the network training, the use of existing GIS data- aided training samples Selected to achieve the automation of sample points on the part of the selection of training samples can improve the efficiency of selection. 28 Slide 29 Conclusions When using ANN predicted by the size and timing across the region-wide restrictions on the length, because some of the major impact Difficult to determine the characteristics of elements, so the prediction is not entirely accurate. When using ANN predicted by the size and timing across the region-wide restrictions on the length, because some of the major impact Difficult to determine the characteristics of elements, so the prediction is not entirely accurate. The need for the use of GIS data generated by way of a deeper level, We believe that with the continuous progress of science and technology will be more accurate forecasting results. The need for the use of GIS data generated by way of a deeper level, We believe that with the continuous progress of science and technology will be more accurate forecasting results. 29