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Machine learning-based prediction of landscape pattern variations: a case study in the Yushenfu mining area, northern Shaanxi, China

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Abstract

Anthropogenic activities have great impacts on ecological environmental protection. Landscape patterns are important indicators of ecological risks. Therefore, effectively and accurately predicting landscape pattern variations is vital for qualitatively identifying the ecological risks faced by an area, especially in ecologically fragile underground coal mining areas with intense anthropogenic mining activities. Conventional landscape pattern prediction models mainly consider the driving factors of climatic and topographical conditions, whereas geological conditions are rarely considered. To overcome this limitation, seven representative driving factors of landscape pattern variations including two geological property-related factors (the thickness of coal seams and groundwater level), two climatic factors (precipitation and evaporation), and three topographical factors (elevation, slope, and NDVI which is abbreviated for the normalized difference vegetation index) were selected for statistical correlation with six major landscape pattern indices. Landscape pattern variation prediction models were subsequently developed using three different machine learning approaches and applied in the Yushenfu mining area of China. The model validation results showed that the model using the particle swarm optimization–extreme learning machine (PSO–ELM) method outperformed that using an extreme learning machine (ELM) and an artificial neural network (ANN). Furthermore, the spatial distribution law of the six landscape pattern indices under coal mining conditions was predicted using the PSO–ELM-based prediction model. The results show that the average increase rates of landscape pattern indices LSI, DP, LPI, and PSA are 7.6%, 26.1%, 44.9%, and 135.8%, respectively; whereas, the average decrease rates of LDI and SHDI are 19.1% and 12.5%, respectively, after mining in the #3 and #4 planning areas. Mining activities reduced the diversity of landscape patterns and increased regional ecological risks, as two high-risk areas were identified. The proposed prediction models were proven to be useful for planning mining areas and protecting local ecological environments.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Funding

Financial support for this work is provided by the National Natural Science Foundation of China (No.42007239), the Natural Science Foundation of Shandong Province (No. ZR2020QE130, No. ZR2021QE009), the Future Plan for Young Talent in Shandong University, State Key Laboratory of Water Resources and Hydropower Engineering Science (Wuhan University) (No.2019NSG03), the State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute (No. 2021nkms02).

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Authors and Affiliations

  1. School of Civil Engineering, Shandong University, Jinan, 250061, Shandong, China

    Shiliang Liu, Ao Wang, Weiguo Li, Wenhui Zhang, Deqiang Mao, Shanlin Wang & Jun Wang

  2. School of Water Conservancy and Environment, University of Jinan, Jinan, 250022, Shandong, China

    Yang Liu

  3. Center for Hydrogeology and Environmental Geology Survey, China Geological Survey, Baoding, 071000, Hebei, China

    Yinfei Luo

  4. Integrated Science Education and Research Centre (ISERC), Institute of Science, Visva-Bharati University, Santiniketan, Birbhum, 731235, West Bengal, India

    Indrani Mukherjee

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  1. Shiliang Liu
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  2. Yang Liu
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Contributions

SL and YL: wrote the original draft manuscript; AW and YL: conducted result analysis and completed investigation; WL and WZ: worked on literature review and data collection; SW: worked on data analysis and result validation; DM and IM: provided guidance and worked on reviewing and editing; JW: worked on conceptualization, methodology, and editing; All authors reviewed the manuscript.

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Correspondence to Jun Wang.

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Liu, S., Liu, Y., Wang, A. et al. Machine learning-based prediction of landscape pattern variations: a case study in the Yushenfu mining area, northern Shaanxi, China. Environ Earth Sci 83, 201 (2024). https://doi.org/10.1007/s12665-024-11490-9

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