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Mapping coastal transformations with a novel Cellular Automata-Markov-Random forest framework for land use change

Mohammad Reza Nikoo1, Erfan Zarei2, Malik Al-Wardy3

  • 1Department of Civil and Architectural Engineering, Sultan Qaboos University, Muscat, Oman.

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Summary

Accurate coastal change prediction is vital for sustainable management. This study enhances land use/land cover (LULC) and shoreline projections in Oman using a hybrid CA-Markov and machine learning model, improving accuracy for future planning.

Keywords:
CA–MarkovHybrid modelingLULC predictionNDWIRandom forestShoreline change

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Area of Science:

  • Environmental Science
  • Geospatial Analysis
  • Coastal Management

Background:

  • Coastal areas face dynamic changes from natural processes and human activities.
  • Accurate prediction of shoreline and land use/land cover (LULC) changes is crucial for sustainable coastal management.
  • Oman's coastal regions are particularly vulnerable to these dynamic changes.

Purpose of the Study:

  • To develop and evaluate a hybrid modeling framework combining CA-Markov and machine learning for enhanced LULC and shoreline change projections in Oman.
  • To assess the predictive performance of different hybrid models against the traditional CA-Markov model.
  • To provide accurate projections for future coastal LULC and shoreline dynamics to support sustainable management strategies.

Main Methods:

  • Delineation of coastlines using multi-temporal Landsat images (1997-2024) and the Normalized Difference Water Index.
  • Quantification of coastal erosion and accretion rates using End Point Rate and Linear Regression Rate analyses.
  • Evaluation of four models (CA-Markov, CA-Markov+XGBoost, CA-Markov+CART, CA-Markov+RF) for future LULC prediction, with CA-Markov+RF showing superior performance.

Main Results:

  • Significant spatial variability in shoreline changes observed between 1997 and 2024, with notable erosion in Rakhyut (-1.81 m/year) and accretion in Bawshar (1.41 m/year).
  • Rapid urban expansion detected, particularly in Muscat, where built-up area increased from 10.31 km² (1997) to 116.41 km² (2015).
  • The hybrid CA-Markov+RF model achieved the highest predictive accuracy (0.935) compared to CA-Markov (0.905), demonstrating the effectiveness of machine learning integration.

Conclusions:

  • The hybrid CA-Markov+RF model significantly enhances the accuracy of LULC and shoreline change projections in vulnerable coastal areas.
  • Future projections (2033) indicate continued urban growth in Salalah and Sohar, with potential reductions in vegetation cover in arid zones.
  • The findings underscore the importance of advanced modeling techniques for effective coastal zone management and sustainable development planning.