Preprint Article Version 1 This version is not peer-reviewed

A New Model Transfer Mechanism Framework for SLEUTH Model Performance Evaluation

Version 1 : Received: 21 December 2017 / Approved: 27 December 2017 / Online: 27 December 2017 (02:40:27 CET)

How to cite: Liu, F.; Quan, J.; Zhu, L.; Chen, Q.; Meng, Y.; Na, Z.; Li, Y.; Gao, A.; Li, S. A New Model Transfer Mechanism Framework for SLEUTH Model Performance Evaluation. Preprints 2017, 2017120187 (doi: 10.20944/preprints201712.0187.v1). Liu, F.; Quan, J.; Zhu, L.; Chen, Q.; Meng, Y.; Na, Z.; Li, Y.; Gao, A.; Li, S. A New Model Transfer Mechanism Framework for SLEUTH Model Performance Evaluation. Preprints 2017, 2017120187 (doi: 10.20944/preprints201712.0187.v1).

Abstract

SLEUTH Model (slope, landuse, exclusion, urban extent, transportation and hillshade) is an important tool for landuse planning and land policy. To evaluate the performance of SLEUTH model, implementation of Sensitivity Analysis (SA) is essential. The main limitation of SA in SLEUTH application is a lack of insight into model input self-modification parameters (SMPs) variation, namely, uncertainty involved in the model transfer metrics and model presumptions, which often misled the decision makers and model users. To address this issue, this study divided the forward process into two stages. Firstly, during the transfer process ①, the contribution scores of five SMPs were drawn, and parameters highly sensitive to model output were given. Apart from that, the recommended initial value for SMPs of 0.11, 0.2, 0.87, 1.13, 15, 1.01, 0.49 were found to be subordinated to such a heterogeneous urban area simulation. Secondly, during the transfer process ②, SMP caused imagery metrics indicated the disparity between parameters with Fixed Reference and with Successive Reference. Reversely, it derives reasonable threshold for the best fit values of five prediction coefficients’ initialization by comparing the real image with the predicted one. The framework of SLEUTH model transfer mechanism not only could distinguish highly sensitive SMPs with higher contribution scores, but also could give parametric analysis for simulation imagery based on metrics. The study was found to be a practical tool for quantization response of model input variables for modelling complex urban systems. So, this insight can help geographic information scientists decide how to find out the inner forward transfer mechanism of SLEUTH model for further make good use of it and improve the model.

Subject Areas

SLEUTH model; Sensitivity Analysis; uncertainty assessment; urban expansion.

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