A Loosely Coupled Model for Simulating and Predicting Land Use Changes

The analysis and modeling of spatial temporal changes in land use can reveal changing urban patterns trends. In this paper, we introduce a linear transformation optimization Markov (LTOM) model that be exploited to estimate the state transition probability matrix use, building loosely coupled ANN-CA-LTOM for simulating predicting changes. advantages are it is flexible high expansibility; maintain semantic coupling between Artificial Neural Networks (ANN), Cellular Automata (CA), LTOM enhance their functions; break limitation requiring two periods data when calculating matrix. We also construct suitability atlas as rules into CA-LTOM model, taking account regional natural socioeconomic driver factors, by exploiting ANN model. employed simulate distribution three major types i.e., construction land, agricultural unused Nansha District, China, 2018 2020. results show performs well overall accuracy simulation was 97.72%, with kappa coefficient 0.962761. Furthermore, simulated predicted from 2021 2023 District trends construction, agricultural, use. This study provides an approach estimating mode models