A BOTTOM-UP QUANTITATIVE MECHANISM FOR UPDATING THE SELF- MODIFICATION PARAMETERS IN THE SLEUTH MODEL

Generally, the default Self-Modification Parameters (SMPs) values, rather than the proper SMPs parameters group, has been applied in the SLEUTH model. However, various pre-setting of SMPs will simulate different morphology and structure of urban sprawl. The study is intended to propose a practical tool for the quantification response to model input variables on modelling complex urban systems. In this research, the parameter weight sorting job has been carried out to provide an adjusting priority experience. Besides, the model output imagery indices were used to describe the morphology, distribution of the urban prediction sprawl and the correlationship with urban road network effectively. Finally, the adjusting factors have been calculated with AHP method. The work helps geographers to determine how to make further use of the inner forward transmission mechanism on the SLEUTH model for further improving it in performance. * Corresponding author: Fang Liu (lf@bucea.edu.cn) 1. INSTRUCTION With the prosperity of urbanization, the development of predicting tools of urban land system has been promoted(Smith, 2019). As one popular representative, the SLEUTH (slope, land-use, exclusion, urban extent, transportation and hill-shade) model has been used for simulating complex urban systems with historic remote sensing data (Chaudhuri and Clarke, 2013; Clarke, 2008; Feng et al., 2012). Characterized by the "selfmodification" rules and a "brute force calibration" technique, which could narrow down the range of prediction behaviour parameters automatically, the SLEUTH model predict the land use in future (Feng, 2012). However, the morphology and structure of urban sprawl vary greatly because the selfmodification parameters (SMPs) settings (Feng et al., 2012; Jafarnezhad et al., 2016). Meanwhile, the model's behaviour is affected by its other controlling parameters, such as Working Grids, Random Number Seed, Monte Carlo Iteration, Excluded Map and Calibration Parameters setup(Chaudhuri and Foley, 2019; Feng et al., 2012). Little detailed research is available about the transmission mechanism for the proper sleuth model self-modification parameters setting yet (Saxena and Jat, 2019). A sensitivity analysis, which has been used in Cellular Automata model, is also a useful tool to explore the quantitative transmission mechanism for urbanization model research. Lessons from sensitivity analysis of a Cellular Automata urban land use model is also the sensitivity analysis for stable performance of SLEUTH model. In this paper, the SMPs setting is updated with the images predicted, rather than the default ones. Besides, a set of image metrics proposed will be used to quantitatively evaluate the differences among image simulation. 2. RESEARCH BACKGROUND Sensitivity analysis (SA) represents an important step in improving the understanding and use of land-use change prediction models (Hewitt and Diaz-Pacheco, 2017; Kocabas and Dragicevic, 2006). The inaccurate results and model uncertainty might be attributed to the structure and nature of the model. SA is the only critical tool for quantifying the response of model input variables for modelling complex urban systems and avoiding unusually high or low growth rates of urban expansion. There are many different SA approaches. Overall, they can be categorized into two groups: local SA and global SA (Hewitt and Diaz-Pacheco, 2017; Schouten et al., 2014; Zoras et al., 2007). The local SA explores the changes of model responses by varying one parameter while keeping the other parameters constant. The simplest and most common approach is differential SA (DSA), which uses partial derivatives or finite differences of parameters at a fixed step as the measure of parametric sensitivity (Song et al., 2015). On the other hand, the global SA examines the changes in the model responses by varying all parameters at the same time, allowing them to provide robust measures in the presence of nonlinearity and interactions among the parameters; thus, they are generally preferred due to their global properties (Saltelli et al., 2008). The generalized SA (GSA) method is one of the global SA methods that have been designed to overcome the limitations of local SA methods (Park et al., 2016). A version of the GSA method, the Generalized Likelihood Uncertainty Estimation (GLUE) method, was developed by Beven & Binley (1992) (Beven and Binley, 1992). GSA is simple to implement and can work with different pseudo-likelihoods (i.e., goodness of fit) measures (Beven, 2011), but it is computationally inefficient. Especially speaking, One-at-a-time (OAT) approaches have recently gained popularity because they offer the most representative local sensitivity measures while maintaining computational efficiency. In sum, the complexity of these ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume V-3-2020, 2020 XXIV ISPRS Congress (2020 edition) This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper. https://doi.org/10.5194/isprs-annals-V-3-2020-309-2020 | © Authors 2020. CC BY 4.0 License. 309 problems highlights the need to understand the sources and range of uncertainty associated with different aspects of the modelling process (Shafizadeh-Moghadam et al., 2017; Xu and Zhang, 2013). It is easy to implement, computationally inexpensive and useful for providing a glimpse at the model behaviour. Recently, Hewitt and Diaz-Pacheco (2017) investigated the influence of the stochastic factor, the influence of scale and cell size to the simulation results (Hewitt and DiazPacheco, 2017). Saxena and Jat (2018) studied the SLEUTH model sensitivity to self-modifying parameter and identified the influence of self-modifying parameters on fragmented urban growth (Jat et al., 2017). Then, the authors above optimized the slope layer, identified the weight of factors for land suitability (Saxena and Jat, 2020). However, when it comes to single factor sensitivity analysis of SMPs, where the default SMPs setting are usually adopted, rather than the proper ones, it has been seldom discussed. But as all know, proper SMPs values will produce healthy predicted images with OAT SA method, including the morphology and structure of urban sprawl. In this paper, two issues would be dealt with: 1) One scheme which probes the response relationship between independent variables and dependent variables will be proposed. 2) with the knowledge rules, the input parameters strategy is adjusted according to the trend and size of the simulation to make sure the predicted urban expansion close to the real one. These questions are addressed with the ample image data from SLEUTH3.0 beta_p01 LINUX, released 6/2005 and sponsored by Project Gigalopolis (USGS and UCSB, 2005). 3. MATERIALS AND METHODS