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Modeling Long-term Groundwater Levels By Exploring Deep Bidirectional Long Short-Term Memory using Hydro-climatic Data
Water Resources Management ( IF 4.3 ) Pub Date : 2021-07-08 , DOI: 10.1007/s11269-021-02899-z
Sangita Dey 1 , Rajesh Kumar Mall 1 , Arabin Kumar Dey 2
Affiliation  

Inevitable issues concerning the sustainability of groundwater resources are crucial under the present climatic situation. Therefore, the prevision of groundwater environments may able to reinforce the management system. In this respect present study considered a new method to predict long-term groundwater level framework as an alternative option of expensive physical models. The proposed Bidirectional Long Short-Term Memory (BLSTM) model can efficiently capture Spatio-temporal features from historical data. A highway LSTM network is also introduced within the architecture of the model to optimize the analysis. The relative performance of the proposed BLSTM with the highway LSTM (BHLSTM) network compared with simple BLSTM. Stack size increment of the BHLSTM and BLSTM layers can enhance the learning ability and improve by incorporating straight LSTM at the top of the architecture. The proposed model was applied to predict the groundwater level exemplary of the Varuna River basin for twenty years. The model incorporates the historical annual average of total precipitation, temperature, relative humidity, actual evapotranspiration, and groundwater level data to develop and validate the models. The result shows that the signals are captured reasonably well by a stack of four BHLSTM and straight LSTM models in forecasting groundwater levels. The predicted water level range (0—20 mbgl) has four categories low, medium, high, and very high which eventually, illustrates the water-threatened situation in upcoming years in the study area. It is also recommended to exploring this proposed method for further improvements and extensions towards interpreting spatial features.



中文翻译:

通过使用水文气候数据探索深层双向长期短期记忆来模拟长期地下水位

在当前的气候条件下,有关地下水资源可持续性的不可避免的问题至关重要。因此,地下水环境的预测可能能够加强管理系统。在这方面,本研究考虑了一种预测长期地下水位框架的新方法,作为昂贵物理模型的替代选择。提出的双向长短期记忆 (BLSTM) 模型可以有效地从历史数据中捕获时空特征。模型架构中还引入了高速公路 LSTM 网络以优化分析。与简单 BLSTM 相比,所提出的 BLSTM 与高速公路 LSTM (BHLSTM) 网络的相对性能。BHLSTM 和 BLSTM 层的堆栈大小增量可以通过在架构顶部合并直 LSTM 来增强学习能力和改进。所提出的模型被应用于预测二十年瓦鲁纳河流域地下水位的范例。该模型结合了总降水量、温度、相对湿度、实际蒸发量和地下水位数据的历史年平均值来开发和验证模型。结果表明,在预测地下水位时,四个 BHLSTM 和直接 LSTM 模型的堆栈相当好地捕获了信号。预测的水位范围(0-20 mbgl)分为低、中、高和非常高四个类别,最终说明了研究区未来几年的水威胁情况。

更新日期:2021-07-08
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