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Spatio-temporal simulation and prediction of land-use change using conventional and machine learning models: a review

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Abstract

Spatio-temporal land-use change modeling, simulation, and prediction have become one of the critical issues in the last three decades due to uncertainty, structure, flexibility, accuracy, the ability for improvement, and the capability for integration of available models. Therefore, many types of models such as dynamic, statistical, and machine learning (ML) models have been used in the geographic information system (GIS) environment to fulfill the high-performance requirements of land-use modeling. This paper provides a literature review on models for modeling, simulating, and predicting land-use change to determine the best approach that can realistically simulate land-use changes. Therefore, the general characteristics of conventional and ML models for land-use change are described, and the different techniques used in the design of these models are classified. The strengths and weaknesses of the various dynamic, statistical, and ML models are determined according to the analysis and discussion of the characteristics of these models. The results of the review confirm that ML models are the most powerful models for simulating land-use change because they can include all driving forces of land-use change in the simulation process and simulate linear and non-linear phenomena, which dynamic models and statistical models are unable to do. However, ML models also have limitations. For instance, some ML models are complex, the simulation rules cannot be changed, and it is difficult to understand how ML models work in a system. However, this can be solved via the use of programming languages such as Python, which in turn improve the simulation capabilities of the ML models.

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Funding

The study presented here is part of a research project funded by Universiti Sains Malaysia (USM) under grant no. 1001/PAWAM/8022043.

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Authors and Affiliations

  1. School of Civil Engineering, Engineering Campus, Universiti Sains Malaysia, 14300, Nibong Tebal, Penang, Malaysia

    Maher Milad Aburas & Mohd Sanusi S. Ahamad

  2. Department of Civil Engineering College of Engineering, University of Kirkuk, Kirkuk, Iraq

    Najat Qader Omar

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  1. Maher Milad Aburas
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  2. Mohd Sanusi S. Ahamad
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  3. Najat Qader Omar
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Correspondence to Mohd Sanusi S. Ahamad.

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Highlights

1. We conducted a comprehensive overview of conventional and ML models for land-use change.

2. We focused on ML models and their strong and weak points.

3. We conducted a review of data and factors that influence the simulation process of land-use change.

4. The ML models should be able to address the limitations of dynamic and statistical models.

5. The limitations of ML models could be improved using programming languages such as Python.

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Aburas, M.M., Ahamad, M.S.S. & Omar, N.Q. Spatio-temporal simulation and prediction of land-use change using conventional and machine learning models: a review. Environ Monit Assess 191, 205 (2019). https://doi.org/10.1007/s10661-019-7330-6

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