Optimization of adaptive neuro fuzzy inference system based urban growth model
© The Author(s) 2016
Received: 30 April 2016
Accepted: 28 July 2016
Published: 4 August 2016
Global urban population has increased from 22.9 % in 1985 to 47 % in 2010. In Iran, population living in urban areas has consistently increased from about 31 % in 1956 to 68.4 % in 2006. Urban growth as one of the results of rapid population growth, results lots of problems. Thus, monitoring and modelling of the urban expansion is necessary.
In this research, a novel Adaptive Neuro Fuzzy Inference System (ANFIS)-based methodology has been developed for urban growth modeling, as well as interpreting the relationship between the drivers of urbanization. Then, ANFIS results were compared with those achieved by both ANN and Logistic Regression (LR)-based methodologies using Percent Area Match quantity and Percent Area Match location to assess model goodness of fit.
The proposed ANFIS model which takes the advantages of using neural networks and fuzzy logic at the same time, had the best performances among the three implemented models. It was able to identify important factors in the development and their relationship and influence on the growth of the city.
The research aim is to find a computational based method which can effectively capture, analyse and model the complex nature of spatial phenomenon like urban growth. The proposed ANFIS method due to its structure is able to deals with nonlinear phenomenon. Integration of Remote sensing data, GIS tools and also, computational based method provide us an effective, reliable and also, scientific methods for monitoring, analysing and modeling of environmental phenomenon.
KeywordsUrban growth Modeling ANFIS
Unprecedented population growth in the cities has caused a number of problems such as improper planning of infrastructure and urban services, environmental pollution and human health problems. Urban growth as one of its results has created many environmental and socioeconomic problems during the last decades (Triantakonstantis 2012). In fact, urban growth can be considered as the transformation of the rural areas to cities and towns, which is coming along with costs (Deep and Saklani 2014). Urban growth as a complex system is affected by human and non-human based parameters. Spatio-temporal dynamics and incorporation of human drivers of land use changes have the most important impact on land use change (Veldkamp and Lambin 2001). Recognition of effective natural, social and spatiotemporal processes that affect urban growth can enhance the accuracy and reliability of the proposed modeling procedure (Foroutan et al. 2012).
Nowadays, due to the high value of land and natural resources and land use change affecting ecosystems and humans, land use change modeling is very important for the concerned urban executives, professionals and researchers. The aim of using urban growth model is to achieve two goals. First, implementation of techniques to understand the spatial relationship between urban growth driving factors (or proxies for them) and historical changes in urban land use. Second, projection of spatial changes in land use based on scenarios of changes in its drivers (Meiyappan et al. 2014). With analyzing historical spatiotemporal information, nature of spatiotemporal dynamics of land use changes resulted from different land use policies can be understood which can serve as a basis for developing possible growth scenarios essential for sustainable urban planning and development (Tayyebi et al. 2011).
Recently, a large number of urban expansion models have been implemented in so many researches. Among these models, artificial neural networks (ANN) and logistic regression (LR) have been so popular (Triantakonstantis and Mountrakis 2012). Logistic regression due to its simple and interpretable structure have been used in this field. On the other hand, ANN due to its fast and parallel processing and also, learning ability for obtaining the expansion patterns, have been used. These popular methods have some disadvantages, too. LR due to its linear structure is unable to deal with the nonlinear parts of the spatial phenomenon. On the other hand, lack of flexibility is one of the ANN disadvantages. Also, ANNs are unable to deal with qualitative uncertainty, too. In this condition, combinations of ANN and Fuzzy Inference Systems (FIS) obviate many of their shortcoming. In other words, by integrating ANNs and fuzzy systems, the capabilities of ANNs self-learning with the linguistic expression function of fuzzy inference can be fused (Pahlavani and Delavar 2014). Dickerson and Kosko (1996), Mitaim and Kosko (2011), Huang and Xing (2002), and Yan (2010) have had such studies that confirm the possibility of extracting fuzzy-rules from training data by integrating fuzzy systems with ANNs. Thus, ANFIS as an ANN based method which takes benefit of fuzzy inference system seems to be appropriate in spatial phenomenon like urban expansion.
Future studies perspective
Asselt et al. (2010), define three different categories of futures studies and how they handle uncertainty, although, different labels are used to categorise futures studies in (Armstrong and Fildes 2006; IPTS-JRC, 2008). Following Asselt et al. (2010), the three proposed categories of future studies include: forecasting, foresight and normative future studies. Forecasting, shows one relatively certain image of the future. The future can be seen as the logical result of the past (Veenman 2013). In fact, Forecasting is a short-, medium- or long-term estimation of future in a specific research area by means of scientific methodology (Cuhls 2003). For this approach, past-based scientific knowledge and models based on these assumptions are considered a reliable basis for making statements about the future (Veenman 2013). In the other words, Forecasting extends past and present patterns and trends into the future, implying a smooth transition between the past, present and the future (Nowotny 2008). Foresight is the second of Asselt’s categories for futures studies that more strongly emphasizes cognitive uncertainty is foresight which deals with multiple possible and plausible future (Veenman 2013). Foresight draws conclusions for the present and is therefore a broad range policy instrument that can serve various objectives (Cuhls 2000). In fact, Foresight is presented in a scenario study as a rich detailed portrait of a plausible future world, or as future states of a system (Berrogi 1997). A scenario is not a forecast but a plausible description of what might occur (Enserink et al. 2010). In foresight studies, future images are given with two or more scenarios (Schwartz 1991; Goodwin and Wright 2010). It is uncertain which trends develop, continue or stop, and which unexpected events might happen, since multiple, alternative futures are possible in foresight analysis (Veenman 2013). Normative, is the third category of future studies of Asselt et al. (2010). In contrast to forecasting and foresight studies, normative futures studies favor normativeness instead of trying to be ‘neutral’ (Veenman 2013). The normative studies include two branches: backcasting and critical futures studies. Backcasting is concerned with how desirable futures can be created, rather than what futures are likely to occur. In backcasting, one envisions a desired future endpoint, and then works backward to determine what policy measures would be required to achieve such a future. Critical future studies emphasizes that images of possible futures are not neutral but represent particular desires, values, cultural assumptions and world views (Asselt et al. 2010). Such future studies sketch a future that is considered ideal, for example, a situation of peace and tolerance, or a situation where the environmental burden is minimised. These types of future studies do not attempt to imagine one or more possible images of the future or one or more possible images of development without a statement being made about the desirability of it. According to Asselt’s category, this paper implemented the first category, forecasting.
Cognition based perspective
Several theoretical perspectives or frameworks have been developed in the study of cognition to organize research, and provide competing and cooperating explanations for cognitive phenomena (Montello and Freundschuh 2005). There are seven major perspectives which provide ample theoretical and conceptual raw material for interpreting past research on cognitive issues in geographic information science, and also, for providing directions for future research (Montello and Freundschuh 2005). They include: constructivism perspective, ecological perspective, information-processing perspective, connectionist perspective, linguistic perspective, situated cognition perspective and evolutionary perspective (Montello and Freundschuh 2005).
In this paper, the research methodology use the information-processing perspective and connectionist perspective categories. Information-processing perspective emphasis on the roles of strategies and metacognition (cognition about cognition) that control the use of cognitive structures when reasoning about particular problems (Montello and Freundschuh 2005). An example is a person using a particular set of rules to perform a GIS procedure on several data layers. The information-processing approach is inspired by traditional rule-based digital computing and is represented by work in formal/computational modeling and symbolic AI. In this study, the data processing section utilized this cognition. For example, Landsat imageries classification in this paper which done using ENVI Software is inspired by information-processing cognition.
Also, this paper in the modeling section (ANFIS and ANN models) utilized the Connectionist perspective cognition. Connectionist perspective cognition suggests that, cognition operates by the activation of complexly interconnected networks of simple neuron-like nodes. The output of a network is determined by the patterns of interconnecting links, and weights on these links, that affect output from one node to another (Rumelhart and McClelland 1986). It is claimed to be a model of cognition that explicitly ties mental activity to the operation of the brain and nervous system, or at least a neurologically plausible model of the nervous system (Montello and Freundschuh 2005). Thus, urban growth as a complex system needs different cognition perspectives to be considered in order to model the nature of urban expansion in a better and more accurate way.
In this section, after introducing the study area (“Study area” section), data pre-processing step (“Data pre-processing” section) and proposed methods (“Artificial neural network specification”, “ANFIS specification”, “LR” sections), the training data are generated (“Creating the training data for the LR, ANN and ANFIS models” section). Then, the three proposed models trained using the training data and PAM quantity and location as the accuracy assessments have been used to determine the best solution between the three proposed models. Then by using the best learned model, we simulated the 2000 and 2006 maps and evaluated the results using the same accuracy assessment factors. Figure 1 presents the flow chart describing the main steps in urban growth modeling.
Classification accuracy (1987)
Classification accuracy (2000)
Classification accuracy (2006)
Artificial neural network specification
According to the recent researches, data driven inductive methods are popular. Because first, they have been extracted from the data and relation between them. Second, they tend to perform better in reproducing existing spatial patterns (Overmars et al. 2007; Koomen et al. 2015). Artificial neural networks with capacity of nonlinear, parallel and highly complex processing have been employed in many fields such as climate forecasting (Panagoulia 2006), agricultural land suitability assessment (Wang 1994), remote sensing (Morris et al. 2005) and land use change and urban growth modeling (Tayyebi et al. 2011; Pijanowski et al. 2002, 2009, 2014). Artificial neural network is a powerful tool in environmental modeling (Li and Yeh 2001). The ability to learn is the most important feature of this method. In the other words, the network uses data to identify patterns and relationships among the data. According to Almeida et al. (2008), Li and Yeh (2002), ANN method has the ability to capture the non-linear relationships presented in many geographic phenomena (Li and Yeh 2002; Li et al. 2003). Thus, it can be used due to this ability to compute the conversion probabilities for competing multiple land uses. There is a general consensus among researchers in the field of urban modeling that empiricism is a reasonable way to determine the most optimum and the best structure in neural net for a specific problem (Li and Yeh 2001; Yeh and Li 2003; Guan and Wang 2005; Almeida et al. 2008). Also, there is no certain rule for determining optimum number of hidden layer and also neurons in the hidden and output layers. In this study, an ANN structure with three layers has been used. The input layer includes eight neurons. Also, The output layer includes two neurons which is the number of classes (urban and non-urban). Tangent sigmoid in the hidden layer and (Purelin) linear function as the transfer function in the output layer have been used.
ANFIS was introduced first by Jang (1993). This method is developed through the integration of ANN and fuzzy logic models which enables us to integrate learning capability and human knowledge together in one method and at the same time to cover many of their shortcomings such as lack of flexibility in ANN and finding out the correct positions and shapes for membership functions in FIS (Mohammady et al. 2013). In this algorithm, during the learning process, membership functions in fuzzy structures change toward their optimal values (Mohammady et al. 2013). In this paper, an ANFIS structure which generated a Sugeno-type Fuzzy Inference System (FIS) structure using subtractive clustering method has been used. The subtractive clustering method has been chosen as the generating FIS method, because of high dimension of the input data. The rule extraction method first uses the subtractive clustering function to determine the number of rules and antecedent membership function and then, uses linear least squares estimation to determine each rules consequent equation (Chiu 1994). Gaussian membership function as the input membership function has been used. Since the dataset has 8 input variables and 1 output variable, sub cluster constructs a FIS with 8 inputs and 1 output. In this method, sub clustering identifies the number of membership function for each input and output which is as many as the number of clusters (Chiu 1994).
Creating the training data for the LR, ANN and ANFIS models
In this matrix, columns 1–8 are the input data and the last column indicates value of the cell obtained from the observed data of urban change. The value of 1 in the last column indicates that a non-urban cell changed to urban and 0 indicates no change to urban. RMSE values are generated for each cycle. Then, the RMSE values are plotted against the number of training cycles (epochs) to identify the best fitting model.
The training data in this study area includes 25 % (50,000 cells) of all the data, which 36 % of these training data (18,000 cells) were entered (ANFIS, ANN and LR) to train and estimate the bias and the rest 64 % (32,000 cells) were used as the data check. The training data (50,000 cells) were 25 % of the 1987 image data which have been used for calibrating the models as training and checking for ANN and ANFIS models and calculating LR parameters. The training data have been chosen from all the image area in a random manner which guarantees there is no bias in the selection. Then, after the selection of the sample data, the models are calibrated.
LR correlation matrix
Distance to region centers
Distance to developed area
Distance to green space
Distance to main road
Distance to fault
Number of urban cell in a 3*3 neighborhood
Distance to region centers
Distance to developed area
Distance to green space
Distance to main road
Distance to fault
Number of urban cell in a 3*3 neighborhood
Simulation results for 2000 map with training and check data using ANFIS, ANN and LR
Area in 1987 (km2)
Area in 2000 (km2)
Predicted area in 2000 (km2)
Simulation results for 2000 map (testing data) using ANFIS
Area in 1987 (km2)
Area in 2000 (km2)
Predicted area in 2000 (km2)
Simulation results for 2006 map using ANFIS
Area in 2000 (km2)
Area in 2006 (km2)
Predicted area in 2006 (km2)
In Sanandaj city during the 1987–2006, the most significant growth has occurred in the southern part of the city. Although, in the north, east and west of city, urban growth has been occurred. However, exiting of the high elevation areas in the west, southwest, north and northeast played as a burrier role in the development of these regions. In addition, the areas in the south west have experienced more growth, as these areas are well served by the urban road network and had appropriate elevation and slope situation.
According to Table 5, distance to developed areas and distance to main roads due to their high values have been the most important factors in the expansion of Sanandaj city during 1987–2006. On the other side, slope and distance to fault had the least impact in expansion of the city due to their small values.
According to Table 6, the ANFIS model has the best goodness of fit among the three models. The predicted urban areas for 2000 map using training and checking data in ANFIS method has been so close to the real urban area for 2000 map than ANN and LR results. Also, the predicted urban area using ANFIS model for 2000 and 2006 maps (Tables 7, 8) have created PAM location and quantity values close to 1 which means the predicted area in both 2000 and 2006 maps have had acceptable agreement with the real ones.
As observed from the existing growth trends until 2006, urban growth policies have not paid serious attention to risk assessments factor such as distance to faults in development of the city which can be a major challenge at high dense urban areas.
Linguistic knowledge through fuzzy inference system can easily be used to model the urban development. So, knowledge and uncertainty about urban development can be easily incorporated into the modeling process. Needless to say that urban planners and urban managers need to provide rules or knowledge instead of exact mathematic expressions for spatial phenomena (Al‐Kheder et al. 2008).
The results of research using ANFIS approach have had better goodness of fit than those of ANN and LR approaches in modeling urban growth for Sanandaj city.
Recently, in Iran, policy makers and urban planners and managers have begun to use urban growth models, both locally and nationally and policies related to land use and urban growth to support efficient use of land and natural resources (Tayyebi et al. 2011). So, urban growth models are powerful tools for urban planners and decision makers to manage and analyse directions and volumes of expansion of cities.
Combination of remote sensing data, geospatial information systems and artificial intelligence can be a powerful and useful method to analyse and model environmental phenomena such as urban growth. This combination has the potential to support such models by providing data and analytical tools for the study of urban planning. In fact, GIS and RS are considered as new reliable ways providing the necessary information and intelligence for planning proposals and can be used as monitoring tools during the implementation of plans.
The result of using more membership functions in ANFIS algorithm is that more accuracy can be achieved in the fewer epochs. On the other hand, use of more membership functions means that the network architecture needs more memory and also more time to reach the predefined error threshold value.
Neither of the considered methods, has limitation on the input data, evaluation and sensitive analysis consideration. They support all kind of input data such as socioeconomic and biophysical data. In a number of commonly used methods such as SLEUTH, the method cannot support socioeconomic data such as population. In addition, in the software based methods like SLEUTH, there is no way for considering sensitive analysis. Increasing the number of input parameters in ANFIS structure could be done with the least change in structure and program. Like other method with an ANN component, in ANFIS the weights and bias of each neuron could not be elaborate separately. This issue is one of the most important ANN’s drawbacks. Thus, in this aspect LR model is simple, clear and has an interpretable structure that could easily determine the weights and importance of each input.
In this study, ANFIS model had the benefits of using neural networks and fuzzy logic at the same time. It was able to identify important factors in the development and their relationship and influence on the growth of the city.
Uncertainty is an indispensable component of spatial phenomena. Urban expansion due to its spatio-temporal nature has greatly affected by uncertainty. ANFIS includes fuzzy inference which is able to deal with uncertainty.
Due to LR model’s simplicity and fast processing capability, it is a well-known method in urban growth and land use change modeling. However, it should be mentioned that this method in unable to model the nonlinear parts of the land use change phenomenon and the huge difference between the result of LR and ANFIS may be due to their structures. On the other hand, ANFIS is a well-known method in nonlinear problems, therefore; it has this ability to deal with complex problems such as urban growth.
Dealing with large data set is a traditional issue in environmental modeling like urban growth and land use change modeling. One of the solutions for the future researches could be clustering the input data and selecting the important ones to make the processing time shorter.
Using satellite imageries with high spatial resolution such as IKONOS, Quickbird and Orbveiw may enhance the classification accuracies. In this study, three Landsat imageries acquired at 1987, 2000 and 2006 have been used. But it should be mentioned that for a developing country like Iran which historical urban data and land use map is not stored properly or even existed, this research using free and reliable satellite imageries data which is the single source of data for these regions is a practical and scientific method for analyzing urban growth.
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- Al-Kheder S, Wanga J, Shana J (2008) Fuzzy inference guided cellular automata urban-growth modelling using multi-temporal satellite images. Int J Geogr Inf Sci 22(11–12):1271–1293View ArticleGoogle Scholar
- Almeida CM, Glerian JM, Castejon EF, Soares- Filhob BS (2008) Using neural networks and cellular automata for modeling intra-urban land-use dynamics. Int J Geogr Inf Sci 22(8–9):943–963View ArticleGoogle Scholar
- Anderson JR, Hardy EE, Roach JT, Witmer RE (1976) A land use and land cover classification system for use with remote sensor data. US Geol Surv Prof Pap 964:28Google Scholar
- Armstrong J, Fildes R (2006) Making progress in forecasting. Int J Forecast 22:433–444View ArticleGoogle Scholar
- Asselt MV, der Molen FV, Faas N, Veenman S (2010) Uit zicht: toekomstverkennen met beleid. Wetenschappelijke Raad voor het Regeringsbeleid, Den HaagGoogle Scholar
- Batty M, Howes D (2001) Predicting temporal patterns in urban development from remote imagery. In: Donnay JP, Barnsley MJ, Longley PA (eds) Remote Sensing and urban analysis. Taylor and Francis, London, pp 185–204View ArticleGoogle Scholar
- Berrogi G (1997) Decision modeling in policy management. Technol Forecast Soc Chang 72(2):161–173Google Scholar
- Chiu S (1994) Fuzzy model identification based on cluster estimation. J Intell Fuzzy Syst 2(3):267–278Google Scholar
- Christensen R (1997) Log-linear models and logistic regression, 3rd edn. Springer-Verlag, New YorkGoogle Scholar
- Clarke KC, Parks BO, Crane MP (2002) Geographic information systems and environmental modeling. Prentice Hall, New JerseyGoogle Scholar
- Cuhls K (2000) Opening up foresight processes participation and networking. In: E´conomies et Socie´te´s. Cahiers de L’Ismea, ParisGoogle Scholar
- Cuhls K (2003) From forecasting to foresight processes—new participative foresight activities in Germany. J Forecast 22:93–111View ArticleGoogle Scholar
- Dadras M, Shafri HZM, Ahmad N, Pradhan B, Safarpour S (2015) Spatio-temporal analysis of urban growth from remote sensing data in Bandar Abbas city, Iran. Egypt J Remote Sens Space Sci 18:35–52Google Scholar
- Deep S, Saklani A (2014) Urban sprawl modeling using cellular automata. Egypt J Remote Sens Space Sci 17:179–187Google Scholar
- Dickerson JA, Kosko B (1996) Fuzzy function approximation with ellipsoidal rules. IEEE Trans Syst Man Cybern Part B Cybern 26(4):542–560View ArticleGoogle Scholar
- Donnay JP, Barnsley MJ, Longle PA (2001) Remote sensing and urban analysis. In: Donnay JP, Barnsley MJ, Longley PA (eds) Remote sensing and urban analysis. Taylor and Francis, London, pp 3–18View ArticleGoogle Scholar
- Enserink B, Hermans L, Kwakkel J, Thissen W, Koppenjan J, Bots P (2010) Policy analysis of multi-actor systems. Lemma, Den HaagGoogle Scholar
- Foroutan E, Delavar MR, Araabi BN (2012) Integration of genetic algorithms and fuzzy logic for urban growth modelling. ISPRS Ann Photogramm Remote Sens Spat Inf Sci 1:69–74View ArticleGoogle Scholar
- Goodchild MF (2000) Spatial analysis: methods and problems in land use management. In: Hill MJ, Aspinall RJ (eds) Spatial information for land use management. Gordon and Breach Science Publishers, Singapore, pp 39–50Google Scholar
- Goodwin P, Wright G (2010) limits of forecasting methods in anticipating rare events. Technol Forecast Soc Chang 77:355–368View ArticleGoogle Scholar
- Guan Q, Wang L (2005) An artificial-neural-network-based constrained CA model for simulating urban growth and its application. In: Auto-curto conference, 18–23 March 2005, Las Vegas, NV. http://www.geog.ucsb.edu1-guanlpapedGuan-AutoCarto2005.pdf. Accessed 26 Dec 2005
- Herold M, Menz G, Clarke KC (2001) Remote sensing and urban growth model demands and perspectives. symposium on remote sensing of urban areas, Regensburger Geographische Schriften, Regensburg, Germany, June 2001, vol. 35, on supplement CD- ROMGoogle Scholar
- Herold M, Goldstein N, Clarke K (2003) The spatio-temporal form of urban growth: measurement, analysis and modeling. Remote Sens Environ 85:95–105Google Scholar
- Huang S, Huang Y (1991) Bounds on the number of hidden neurons in multilayer perceptrons. IEEE Trans Neural Networks 2(1):47–55View ArticleGoogle Scholar
- Huang SH, Xing H (2002) Extract intelligible and concise fuzzy rules from neural networks. Fuzzy Sets Syst 132(2):233–243View ArticleGoogle Scholar
- Im J, Jensen J, Tullis J (2008) Object-based change detection using correlation image analysis and image segmentation. Int J Remote Sens 29:399–423View ArticleGoogle Scholar
- IPTS-JRC (2008) On-line foresight guide. JRC-IPTS, SevillaGoogle Scholar
- Jang JR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685View ArticleGoogle Scholar
- Jensen JR, Cowen DC (1999) Remote sensing of urban/suburban infrastructure and socio-economic attributes. Photogramm Eng Remote Sens 65(5):611–622Google Scholar
- Koomen E, Diogo V, Dekkers J, Rietveld P (2015) A utility-based suitability framework for integrated local-scale land-use modelling. Comput Environ Urban Syst 50:1–14View ArticleGoogle Scholar
- Kumar DS, Arya DS, Vojinovic Z (2013) Modeling of urban growth dynamics and its impact on surface runoff characteristics. Comput Environ Urban Syst 41:124–135View ArticleGoogle Scholar
- Li X, Yeh AGO (2001) Calibration of cellular automata by using neural networks for the simulation of complex urban systems. Environ Plan A 33:1445–1462View ArticleGoogle Scholar
- Li X, Yeh AGO (2002) Urban simulation using principal components analysis and cellular automata for land-use planning. Photogramm Eng Remote Sens 68(4):341–352Google Scholar
- Li X, Yeh AGO (2004) Analyzing spatial restructuring of land use patterns in a fast growing region using remote sensing and GIS. Landsc Urban Plan 69:335–354View ArticleGoogle Scholar
- Li L, Sato Y, Zhu H (2003) Simulating spatial urban expansion based on a physical process. Landsc Urban Plan 64:67–76View ArticleGoogle Scholar
- Meiyappan P, Dalton M, O’Neill C, Atul B, Jain AK (2014) Spatial modeling of agricultural land use change at global scale. Ecol Model 291:152–174View ArticleGoogle Scholar
- Mitaim S, Kosko B (2011) The shape of fuzzy sets in adaptive function approximation. IEEE Trans Fuzzy Syst 9(4):637–656View ArticleGoogle Scholar
- Mohammady S, Delavar MR, Pijanowski BC (2013) Urban growth modeling using ANFIS algorithm: a case study for Sanandaj city, Iran. Int Arch Photogramm Remote Sens Spatial Inf Sci 3:493–498. doi:10.5194/isprsarchives-XL-1-W3-493-2013 View ArticleGoogle Scholar
- Montello DR, Freundschuh SM (2005) Cognition of geographic information. In: McMaster RB, Usery EL (eds) A research agenda for geographic information science. CRC Press, Boca Raton, pp 61–91Google Scholar
- Morris J, Porter D, Neet M, Noble PA, Schmidt L, Lapine LA, Jensen JR (2005) Salt and brackish marsh characterization at North Inlet, SC using LIDAR- derived elevation data and land cover extracted from multispectral imagery using a neural network. Int J Remote Sens 26:5221–5234View ArticleGoogle Scholar
- Nowotny H (2008) Insatiable curiosity: innovation in a fragile future. Cambridge Mamit Press, CambridgeGoogle Scholar
- Overmars KP, Verburg PH, Veldkamp T (2007) Comparison of a deductive and an inductive approach to specify land suitability in a spatially explicit land use model. Land Use Policy 24(3):584–599View ArticleGoogle Scholar
- Pahlavani P, Delavar MR (2014) Multi-criteria route planning based on a driver’s preferences in multi-criteria route selection. Transp Res Part C 40:14–35View ArticleGoogle Scholar
- Panagoulia D (2006) Artificial neural networks and high and low flows in various climate regimes. Hydrol Sci J 51(4):563–587View ArticleGoogle Scholar
- Pijanowski BC, Shellito B, Pithadia S (2002) Using artificial neural networks, geographic information systems and remote sensing to model urban sprawl in coastal watersheds along eastern Lake Michigan. Lakes Reserv 7:271–285View ArticleGoogle Scholar
- Pijanowski BC, Pithadia S, Shellito BA, Alexandridis K (2005) Calibrating a neural network-based urban change model for two metropolitan areas of Upper Midwest of the United States. Int J Geogr Inf Sci 19:197–215View ArticleGoogle Scholar
- Pijanowski BC, Tayyebi A, Delavar MR, Yazdanpanah MJ (2009) Urban expansion simulation using geographic information systems and artificial neural networks. Int J Environ Res 3(4):493–502Google Scholar
- Pijanowski BC, Tayyebi A, Doucette J, Pekin BK, Braun D, Plourde J (2014) A big data urban growth simulation at a national scale: configuring the GIS and neural network based land transformation model to run in a high performance computing (HPC) environment. Environ Model Softw 51:250–268View ArticleGoogle Scholar
- Rumelhart DE, McClelland JL (eds) (1986) Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations. The MIT Press, CambridgeGoogle Scholar
- Schwartz P (1991) The Art of the long view: planning for the future in an uncertain world. Doubleday, New YorkGoogle Scholar
- Sousa S, Caeiro S, Painho M (2002) Assessment of map similarity of categorical maps using kappa statistics: the case of sado estuary. Paper presented at the ESIG 2002, Tagus Park, OeirasGoogle Scholar
- Tayyebi A, Pijanowski BC, Tayyebi AH (2011) An urban growth boundary model using neural networks, GIS and radial parameterization: an application to Tehran, Iran. Landsc Urban Plan 100:35–44View ArticleGoogle Scholar
- Triantakonstantis DP (2012) Urban growth modeling using determinism and stochasticity in a touristic village in western Greece. Open J Civil Eng 2:42–48View ArticleGoogle Scholar
- Triantakonstantis D, Mountrakis G (2012) Urban growth prediction: a review of computational models and human perceptions. J Geogr Inf Syst 4:555–587Google Scholar
- Veenman SA (2013) Futures studies and uncertainty in public policy: a case study on the ageing population in the Netherlands. Futures 53:42–52View ArticleGoogle Scholar
- Veldkamp A, Lambin EF (2001) Predicting land-use change. Agric Ecosyst Environ 85:1–6View ArticleGoogle Scholar
- Wang F (1994) The use of artificial neural networks in a geographical information system for agricultural land-suitability assessment. Environ Plan A 26:265–284View ArticleGoogle Scholar
- Weng Y (2007) Spatiotemporal changes of landscape pattern in response to urbanization. Landsc Urban Plan 81:341–353View ArticleGoogle Scholar
- Yan Z (2010) Algorithms of extracting fuzzy rules from sample data. In: Intelligent computing and intelligent systems (ICIS). International conference, Shanghai, China, Oct 29–31, 2010, pp 823–827Google Scholar
- Yeh AGO, Li X (2003) Simulation of development alternatives using neural networks, cellular automata and GIS for urban planning. Photogramm Eng Remote Sens 69(9):1043–1052View ArticleGoogle Scholar