Improving land-use change modeling by integrating ANN with Cellular Automata-Markov Chain model

Abstract

Urban growth and land-use change are a few of many puzzling factors affecting our future cities. Creating a precise simulation for future land change is a challenging process that requires temporal and spatial modeling. Many recent studies developed and trained models to predict urban expansion patterns using Artificial Intelligence (AI). This study aims to enhance the simulation capability of Cellular Automata Markov Chain (CA-MC) model in predicting changes in land-use. This study integrates the Artificial Neural Network (ANN) into CA-MC to incorporate several driving forces that highly impact land-use change. The research utilizes different socio-economic, spatial, and environmental variables (slope, distance to road, distance to urban centers, distance to commercial, density, elevation, and land fertility) to generate potential transition maps using ANN Data-driven model. The generated maps are fed to CA-MC as additional inputs. We calibrated the original CA-MC and our models for 2015 cross-comparing simulated maps and actual maps obtained for Irbid city, Jordan in 2015. Validation of our model was assessed and compared to the CA-MC model using Kappa indices including the agreement in terms of quantity and location. The results elucidated that our model with an accuracy of 90.04% substantially outperforms CA-MC (86.29%) model. The improvement we obtained from integrating ANN with CA-MC suggested that the influence imposed by the driving force was necessary to be taken into account for more accurate prediction. In addition to the improved model prediction, the predicted maps of Irbid for the years 2021 and 2027 will guide local authorities in the development of management strategies that balance urban expansion and protect agricultural regions. This will play a vital role in sustaining Jordan's food security.

Integrating Spatial Markov Chains and Geographically Weighted Regression-Based Cellular Automata to Simulate Urban Agglomeration Growth: A Case Study of the Guangdong–Hong Kong–Macao Greater Bay Area

“…In addition to analyzing historical land-use change using the SMC approach, this study also evaluated urban growth suitability using the GWR model. Generally, many spatial factors need to be considered in evaluating urban growth suitability [35,52]. However, given that the present study performed such an evaluation on the scale of an urban agglomeration-especially since we used the Markov chain to reconstruct the spatial structure of the GBA-we felt that it was better to choose relatively few but highly stable spatial factors.…”

“…In addition to analyzing historical land-use change using the SMC approach, this study also evaluated urban growth suitability using the GWR model. Generally, many spatial factors need to be considered in evaluating urban growth suitability [35,52]. However, given that the present study performed such an evaluation on the scale of an urban…”

“…The Markov chain (MC) is a stochastic process that is often used to predict changes in land-use structure [21,35,64] and a Markov model can be expressed by Equation (1):…”

“…In contrast to these two methods, the Markov chain model is able to predict changes in land-use structure based only on the land-use transfer probability. For this reason, the Markov chain method is widely used to predict the size of urban growth [35][36][37].…”

An Artificial Neural Network Approach to Predict the Future Land Use Land Cover of Great Malang Region, Indonesia

“…Furthermore, uncertainty, class consistency, and mis-registration issues become more of a problem as more time periods are used in the SLEUTH model. There are many studies have been implemented the combination of CA-Markov for land use modelling (Gharaibeh et al, 2020;Halmy et al, 2015;Mansour et al, 2020;Okwuashi & Ndehedehe, 2020;Rahnama, 2021;Wang et al, 2021;Wu et al, 2019;Zhou et al, 2020). However very limited studies using neural network combination with CA for modelling land-use dynamic.…”