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The study of artificial intelligence for predicting land use changes in an arid ecosystem

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Abstract

During the 21st century, artificial intelligence methods have been broadly applied in geosciences to simulate complex dynamic ecosystems, but the use of artificial intelligence (AI) methods to reproduce land-use/cover change (LUCC) in arid ecosystems remains rare. This paper presents a hybrid modeling approach to understand the complexity in LUCC. Fuzzy logic, equation-based systems, and expert systems are combined to predict LUCC as determined by water resources and other factors. The driving factors of LUCC in this study include climate change, ecological flooding, groundwater conditions, and human activities. The increase of natural flooding was found to be effective in preventing vegetation degradation. LUCCs are sensitive under different climate projections of RCP2.6, RCP4.5, and RCP8.5. Simulation results indicate that the increase of precipitation is not able to compensate for the additional evaporation losses resulting from temperature increases. The results indicate that grassland, shrub, and riparian forest regions will shrink in this study area. The change in grasslands has a strong negative correlation with the change in groundwater salinity, whereas forest change had a strong positive correlation with ecological flooding. The application of artificial intelligence to study LUCC can guide land management policies and make predictions regarding land degradation.

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Authors and Affiliations

  1. State Key Laboratory of Desert and Oasis Ecology, **njiang Institute of Ecology and Geography, CAS, Urumqi, 830011, China

    Yang Yu, Haiyan Zhang & Ruide Yu

  2. Advanced Research Institute, Southwest University of Political Science & Law, Chongqing, 401120, China

    Yiguo Cao & Dongde Hou

  3. Chair of Hydrology and River Basin Management, Technical University of Munich, Munich, 80333, Germany

    Markus Disse

  4. Chair of Statistics and Risk Management, Universitaet der Bundeswehr Muenchen, Neubiberg, D-85577, Germany

    Andreas Brieden

Authors
  1. Yang Yu
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  2. Yiguo Cao
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  5. Andreas Brieden
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  6. Haiyan Zhang
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  7. Ruide Yu
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Correspondence to Yiguo Cao.

Additional information

Foundation: Chinese Academy of Sciences “Light of West China” Program, No.2018-XBQNXZ-B-017; National Natural Science Foundation of China, No.42107084; Philosophy and Social Science Major Project funded by the Ministry of Education of the People’s Republic of China, No.20JZD026

Author: Yu Yang, Associate Professor, specialized in environmental change in arid lands.

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Yu, Y., Cao, Y., Hou, D. et al. The study of artificial intelligence for predicting land use changes in an arid ecosystem. J. Geogr. Sci. 32, 717–734 (2022). https://doi.org/10.1007/s11442-022-1969-6

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  • DOI: https://doi.org/10.1007/s11442-022-1969-6

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