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U-Net-LSTM: Time Series-Enhanced Lake Boundary Prediction Model

Author

Listed:
  • Lirong Yin

    (Department of Geography & Anthropology, Louisiana State University, Baton Rouge, LA 70803, USA)

  • Lei Wang

    (Department of Geography & Anthropology, Louisiana State University, Baton Rouge, LA 70803, USA)

  • Tingqiao Li

    (School of Automation, University of Electronic Science and Technology of China, Chengdu 610054, China)

  • Siyu Lu

    (School of Automation, University of Electronic Science and Technology of China, Chengdu 610054, China)

  • Jiawei Tian

    (School of Automation, University of Electronic Science and Technology of China, Chengdu 610054, China)

  • Zhengtong Yin

    (College of Resource and Environment Engineering, Guizhou University, Guiyang 550025, China)

  • Xiaolu Li

    (School of Geographical Sciences, Southwest University, Chongqing 400715, China)

  • Wenfeng Zheng

    (School of Automation, University of Electronic Science and Technology of China, Chengdu 610054, China)

Abstract

Change detection of natural lake boundaries is one of the important tasks in remote sensing image interpretation. In an ordinary fully connected network, or CNN, the signal of neurons in each layer can only be propagated to the upper layer, and the processing of samples is independent at each moment. However, for time-series data with transferability, the learned change information needs to be recorded and utilized. To solve the above problems, we propose a lake boundary change prediction model combining U-Net and LSTM. The ensemble of LSTMs helps to improve the overall accuracy and robustness of the model by capturing the spatial and temporal nuances in the data, resulting in more precise predictions. This study selected Lake Urmia as the research area and used the annual panoramic remote sensing images from 1996 to 2014 (Lat: 37°00′ N to 38°15′ N, Lon: 46°10′ E to 44°50′ E) obtained by Google Earth Professional Edition 7.3 software as the research data set. This model uses the U-Net network to extract multi-level change features and analyze the change trend of lake boundaries. The LSTM module is introduced after U-Net to optimize the predictive model using historical data storage and forgetting as well as current input data. This method enables the model to automatically fit the trend of time series data and mine the deep information of lake boundary changes. Through experimental verification, the model’s prediction accuracy for lake boundary changes after training can reach 89.43%. Comparative experiments with the existing U-Net-STN model show that the U-Net-LSTM model used in this study has higher prediction accuracy and lower mean square error.

Suggested Citation

  • Lirong Yin & Lei Wang & Tingqiao Li & Siyu Lu & Jiawei Tian & Zhengtong Yin & Xiaolu Li & Wenfeng Zheng, 2023. "U-Net-LSTM: Time Series-Enhanced Lake Boundary Prediction Model," Land, MDPI, vol. 12(10), pages 1-18, September.
  • Handle: RePEc:gam:jlands:v:12:y:2023:i:10:p:1859-:d:1250886
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    References listed on IDEAS

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    1. Alireza Taheri Dehkordi & Mohammad Javad Valadan Zoej & Hani Ghasemi & Ebrahim Ghaderpour & Quazi K. Hassan, 2022. "A New Clustering Method to Generate Training Samples for Supervised Monitoring of Long-Term Water Surface Dynamics Using Landsat Data through Google Earth Engine," Sustainability, MDPI, vol. 14(13), pages 1-24, June.
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    Cited by:

    1. Anwar Hussain & Masoud Reihanifar & Rizwan Niaz & Olayan Albalawi & Mohsen Maghrebi & Abdelkader T. Ahmed & Ali Danandeh Mehr, 2024. "Characterizing Inter-Seasonal Meteorological Drought Using Random Effect Logistic Regression," Sustainability, MDPI, vol. 16(19), pages 1-20, September.
    2. Khouloud Ben Messaoud & Yunda Wang & Peiyi Jiang & Zidi Ma & Kaiqi Hou & Fei Dai, 2024. "Spatial-Temporal Dynamics of Urban Green Spaces in Response to Rapid Urbanization and Urban Expansion in Tunis between 2000 and 2020," Land, MDPI, vol. 13(1), pages 1-21, January.
    3. Longqing Liu & Shidong Zhang & Wenshu Liu & Hongjiao Qu & Luo Guo, 2024. "Spatiotemporal Changes and Simulation Prediction of Ecological Security Pattern on the Qinghai–Tibet Plateau Based on Deep Learning," Land, MDPI, vol. 13(7), pages 1-20, July.
    4. Elsadek, Elsayed Ahmed & Zhang, Ke & Hamoud, Yousef Alhaj & Mousa, Ahmed & Awad, Ahmed & Abdallah, Mohammed & Shaghaleh, Hiba & Hamad, Amar Ali Adam & Jamil, Muhammad Tahir & Elbeltagi, Ahmed, 2024. "Impacts of climate change on rice yields in the Nile River Delta of Egypt: A large-scale projection analysis based on CMIP6," Agricultural Water Management, Elsevier, vol. 292(C).
    5. Bagher Shirmohammadi & Arash Malekian & Saeid Varamesh & Abolfazl Jaafari & Javad Abdolahi & Saeed Shahbazikia & Mohammad Mohsenzadeh, 2024. "How can biomechanical measures incorporate climate change adaptation into disaster risk reduction and ecosystem sustainability?," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 120(9), pages 8323-8336, July.

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