Sensitivity analysis and accuracy assessment of the land transformation model using cellular automata

Abstract

This study evaluates the effects of cellular automata (CA) with different neighborhood sizes on the predictive performance of the Land Transformation Model (LTM). Landsat images were used to extract urban footprints and the driving forces behind urban growth seen for the metropolitan areas of Tehran and Isfahan in Iran. LTM, which uses a back-propagation neural network, was applied to investigate the relationships between urban growth and the associated drivers, and to create the transition probability map. To simulate urban growth, the following two approaches were implemented: (a) the LTM using a top-down approach for cell allocation grounding on the highest values in the transition probability map and (b) a CA with varying spatial neighborhood sizes. The results show that using the LTM-CA approach increases the accuracy of the simulated land use maps when compared with the use of the LTM topdown approach. In particular, the LTM-CA with a 7 × 7 neighborhood size performed well and improved the accuracy. The level of agreement between simulated and actual urban growth increased from 58% to 61% for Tehran and from 39% to 43% for Isfahan. In conclusion, even though the LTM-CA outperforms the LTM with a topdown approach, more studies have to be carried out within other geographical settings to better evaluate the effect of CA on the allocation phase of the urban growth simulation.

A Review of Simulation Urban Growth Model

“…To know urban growth there are several approaches to study such as population, soil surveys, air surveys, water surveys, remote sensing, and urbanization [16]. Remote sensing in GIS is highly accurate and efficient in assessing urban growth [17]. Along with increasingly sophisticated technological advances especially in technological developments in the field, in use detailed temporal and spatial data on the geographic information system and remote sensing directly lead to an increasing number of model development for land use especially in urban growth [4,8].…”

Long-Term Land Cover Data for the Lower Peninsula of Michigan, 2010–2050

“…LTM uses ANN, which is a machine learning technique, for modelling land cover change [37,[39][40][41]. The multilayer perceptron is one of the well-known ANN forms that is most commonly employed in land cover change science [1].…”

“…First, the ANN initially assigns random values to ANN parameters (weights and biases), then the multilayer perceptron applies random weights and biases to the input data in order to estimate the outputs. Second, the ANN calculates the mean squared error (difference between the estimated outputs and reference outputs), which is then propagated backward to the previous layers [40]. Optimum model parameters values are estimated by iterating the LTM model through many cycles.…”

“…PCM describes the proportion of the reference map where urbanization, and no-urban, have been correctly predicted by the LTM [29]. In contrast, ROC is capable of calculating the accuracy across a range of thresholds vary from 0 to 1 [30,40]. For each given threshold (e.g., 0, 0.1, 0.2, .…”

Simulating Intraurban Land Use Dynamics under Multiple Scenarios Based on Fuzzy Cellular Automata: A Case Study of Jinzhou District, Dalian

“…Shafizadeh-Moghadam et al and Zheng et al evaluated the effect of cell neighborhood size on the accuracy of CA simulation by selecting two cities in Iran, Tehran and Isfahan, as the research areas. Through PA (product accuracy), OA (overall accuracy), and FoM (the figure of merit), the accuracy of the simulation under the scales of 3 m × 3 m, 5 m × 5 m, 7 m × 7 m, and 9 m × 9 m was verified, and the results showed that the best simulation was achieved under the scale of 7 m × 7 m [57,58]. Therefore, in order to determine the reliability of CA, the selection of cell neighborhood size must be verified.…”