Abstract
Accurate quantification of lake areas is pivotal for understanding regional hydrological cycles and conserving wetlands. Existing methodologies, including remote sensing interpretations and conventional deep learning approaches, face inherent limitations. Addressing this, the present study introduces an innovative deep learning model for lake area prediction, incorporating physical mechanisms. Initially, the study extracts the monthly areas of East Dongting Lake over 2000 to 2020, utilizing the Google Earth Engine (GEE) platform. Subsequently, it integrates water level data from Chenglingji, developing a response relationship between area and water level. This process enhances the loss function of the traditional Long Short-Term Memory (LSTM) model, leading to the creation of a physically informed Area and Water Level-LSTM model (AWL-LSTM). This model is then assessed in comparison to its traditional LSTM counterpart.The findings reveal a pronounced shrinking trend in the water area of East Dongting Lake over the past two decades, characterized by significant seasonal fluctuations. In periods of rising, high, and receding water levels, the lake's area exhibits a robust correlation with the Chenglingji water levels. However, this correlation weakens during low water periods. The AWL-LSTM model outperforms the conventional LSTM in several aspects, including extensibility, precision, interpretability, and stability. It demonstrates superior accuracy in peak data series fitting, evidenced by a 34% and 28% improvement in the Nash-Sutcliffe Efficiency Coefficient (NSE) and significant reductions in the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) by 77% and 80%, respectively. By integrating the characteristics of the “dry, rising, abundant, and receding” periods under the constraint of physical relationships, the model’s correction effect depends on the correlation of these physical relationships. A higher correlation translates to greater model interpretability. The AWL-LSTM model offers valuable technological support for scientifically forecasting lake areas and managing water resources efficiently.
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No datasets were generated or analysed during the current study.
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This research received funding from the Hubei Key Laboratory of Intelligent Yangtze and Hydroelectric Science, China Yangtze Power Co., Ltd. open research fund, Fund number: ZH2102000111.
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Conceptualization, Yanfei.Chen. , Yuru.Dong.and Yongxi.Sun; methodology, Yuru.Dong. and Yongxi.Sun; software, Yuru.Dong. and Yongxi.Sun; formal analysis, Yuru.Dong.; investigation, Yanfei.Chen.; resources, Yanfei.Chen.; data curation, Yuru.Dong.; writing—original draft preparation, Yuru.Dong.and Yongxi.Sun.; writing—review and editing, Chao.He. and Yanfei.Chen.
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Chen, Y., Dong, Y., Sun, Y. et al. Typical lake area is accurately predicted and assessed based on deep learning algorithms and associated physical mechanisms. Earth Sci Inform (2024). https://doi.org/10.1007/s12145-024-01282-x
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DOI: https://doi.org/10.1007/s12145-024-01282-x