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Self-attention (SA) temporal convolutional network (SATCN)-long short-term memory neural network (SATCN-LSTM): an

Mohammad Ehteram¹, Elham Ghanbari-Adivi²

¹Department of Water Engineering, Semnan University, Semnan, Iran.

Environmental Science and Pollution Research International

|July 27, 2023

Summary

A new self-attention temporal convolutional network-long short-term memory neural network (SATCN-LSTM) model improves groundwater level prediction accuracy. This advanced model offers better water management decisions and sustainable resource use.

Keywords:

Deep learning Feature extraction Groundwater management Predictive models

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Area of Science:

Environmental Science
Hydrology
Artificial Intelligence

Background:

Effective water management relies on accurate groundwater level prediction.
Existing models like LSTM have limitations in capturing complex temporal dependencies.

Purpose of the Study:

To introduce and evaluate a novel SATCN-LSTM model for enhanced groundwater level prediction.
To improve the accuracy and reliability of groundwater forecasting for better water resource management.

Main Methods:

Developed a hybrid SATCN-LSTM model integrating Temporal Convolutional Networks (TCN) with Long Short-Term Memory (LSTM).
Employed self-attention mechanisms and skip connections within the TCN component to address vanishing gradients and identify relevant data.
Utilized meteorological data as input for predicting groundwater levels (GWL).

Main Results:

The SATCN-LSTM model achieved the lowest Mean Absolute Error (MAE) of 0.09 and Root Mean Square Error (RMSE) of 0.14.
Outperformed other models including SATCN (MAE: 0.12, RMSE: 0.15), SALSTM (MAE: 0.16), TCN-LSTM (MAE: 0.17), TCN (MAE: 0.22), and LSTM (MAE: 0.23).

Conclusions:

The SATCN-LSTM model demonstrates superior performance and robustness in groundwater level prediction.
Improved prediction accuracy facilitates informed decision-making for water allocation, abstraction, and drought preparedness.
The model contributes to the sustainable and efficient management of groundwater resources.