Predicting Groundwater Levels in South Korea Using Machine Learning and SHAP Techniques

Predicting Groundwater Levels in South Korea Using Machine Learning and SHAP Techniques

Understanding Groundwater Level Prediction

Groundwater levels (GWLs) play a crucial role in water resource management. Predicting these levels helps in agricultural planning, managing water supply, and minimizing drought impact. Machine learning (ML) and deep learning (DL) models provide advanced techniques to analyze complex environmental data. By leveraging historical weather and groundwater data, these models can offer accurate predictions that benefit both ecosystems and human activity.

Key Components Influencing GWL Predictions

The prediction of groundwater levels involves various meteorological and hydrological variables. Key components include:

1. Rainfall Data: This is a primary input affecting groundwater recharge. Daily rainfall data from meteorological stations is crucial for accurate predictions.
2. Temperature and Humidity: Air temperature and relative humidity contribute to evaporation rates, which in turn influence groundwater levels.
3. Historical GWLs: Previous groundwater measurements serve as essential data points that inform the model about fluctuations due to seasonal changes or extreme weather patterns.

For instance, in a study focusing on the Bongseong well in South Korea, predictive models used daily rainfall, temperature, and measured groundwater data from multiple nearby wells for enhanced accuracy.

ML and DL Model Lifecycle for GWL Prediction

To develop reliable predictions, a systematic approach is necessary. The typical lifecycle consists of several steps:

1. Data Collection: Gathering meteorological data and historical groundwater levels is the first task. This includes accessing databases or utilizing sensors for real-time data.
2. Pre-processing: The data must be cleaned for inconsistencies, missing values, or outliers. Techniques such as normalization may be applied.
3. Model Selection: Different ML and DL algorithms—like Random Forest (RF), Support Vector Regression (SVR), and Long Short-Term Memory (LSTM)—are evaluated for suitability in prediction tasks.
4. Training and Evaluation: Models are trained using a subset of data, and their predictive power is evaluated using metrics like Root Mean Square Error (RMSE) and Coefficient of Correlation (CC).
5. Deployment: Once validated, models are deployed in real-time scenarios to provide predictions, which can be adjusted based on new incoming data.

In the case of the Bongseong well, researchers utilized various models to assess their effectiveness in predicting GWLs, leading to significant insights into model performance.

Practical Examples and Case Studies

The application of machine learning in groundwater prediction can be illustrated through specific scenarios.

In Scenario 01, daily rainfall, air temperature, and relative humidity from two stations were used alongside GWL data from seven observation wells. The Random Forest model outperformed others in terms of prediction accuracy measured by RMSE and correlation coefficients—indicating that it was better suited to capture the intricate relations of the data compared to models such as LSTM.

Similarly, in Scenario 02, additional factors, including groundwater temperature and conductivity, were introduced. A General Regression Neural Network (GRNN) model provided the most reliable predictions, emphasizing the impact of including a broader range of variables.

Common Pitfalls and Solutions

While employing ML and DL for predicting GWLs, several pitfalls can arise:

• Overfitting: This phenomenon occurs when a model is too complex and captures noise along with the data signal. To avoid this, techniques such as regularization and cross-validation should be employed.
• Data Quality: Inaccurate or incomplete data can severely affect model predictions. Implementing thorough data validation processes helps mitigate this risk.
• Model Selection: Not all ML/DL models are appropriate for every dataset. Utilizing performance metrics to compare models ensures the best fit for the specific data characteristics is selected.

Addressing these common issues can significantly enhance predictive accuracy, ensuring that stakeholders can make informed decisions based on reliable data.

Tools and Metrics in Practice

Several tools and frameworks assist in implementing ML and DL for groundwater predictions. Popular platforms include:

• Python Libraries: Libraries such as Sklearn for traditional ML models and TensorFlow or Keras for DL applications provide robust functionalities.
• Metrics: Evaluation is done using RMSE, Nash-Sutcliffe Efficiency (NSE), and Coefficient of Determination (R²) to quantify model performance. Each metric offers insights into different aspects of model accuracy.

These tools are employed by researchers and data scientists in academia and industry, facilitating a deeper understanding of groundwater dynamics.

Variations and Alternatives to Consider

While various ML models have shown efficacy, choosing between them can depend on specific project requirements such as data availability, computation resources, and desired accuracy. For instance:

• Random Forests are favored for their robustness against overfitting while being interpretable.
• LSTMs are effective for time series forecasting but require extensive data and training time.

Balancing complexity and performance is key; simpler models may provide adequate accuracy with quicker deployment.

Interpreting Model Predictions Using SHAP

SHAP (SHapley Additive exPlanations) is a tool that enhances the transparency of ML models. It helps users understand how individual feature contributions affect predictions, which is crucial for decision-making in resource management.

Using the SHAP methodology:

• Global Interpretations: Summarize the importance of various features across all data points, highlighting which factors most influence predictions.
• Local Interpretations: Assess the impact of individual features on specific predictions, offering insights for individual cases.

Visual tools like summary and force plots enable clearer presentations of contributions, assisting stakeholders in understanding model decisions clearly.

Practical Implications and Future Directions

By improving GWL predictions through ML and SHAP techniques, water resource management can be more proactive rather than reactive. As these methodologies become established, their integration into policy-making can help address issues like climate change and urbanization impacts on water resources. Enhanced predictions can lead to better sustainability practices, efficient water usage, and strategic planning in agriculture and urban development.

This research underscores the potential of combining advanced computational techniques with environmental science. As technology evolves, further developments in ML and SHAP will likely contribute to even more refined approaches in predicting not just groundwater levels, but a broad range of environmental phenomena.