Land use change detection and prediction using Similarity Weighted Instance-based Learning, A Case Study: Tehran, Iran

The development of cities cannot be considered useful or harmful itself, but it will have irreparable consequences if this development is unplanned and unbridled. Unplanned land use changes in cities not only disrupt urban management but also cause damage to the environment. Therefore, modelling and predicting these changes can play a significant role in urban management planning. In this study, a way to model and predict multiple land changes has been provided. In this regard, a similarity weighted instance-based learning method was used. In this study, Landsat satellite images were used in 2002, 2008 and 2014 to extract the land use map using the support vector machine (SVM) classification method. Modelling was performed to reach the probability of change map, where pixels with higher probability indicated that they belong to the intended land use class. The Multi Objective Land Allocation (MOLA) method then identified potential areas for land use change for each land use class for 2020, using maps of the probability of land use change from the changeable area between 2002 and 2008. Kappa coefficients are obtained for two algorithms. Results showed the high capability of the proposed method used.

Land use change detection and prediction using Similarity Weighted Instance-based Learning, A Case Study: Tehran, Iran Keywords:

Markov chain , Cellular automata , Land use change Prediction , Similarity Weighted Instance-based Learning