Regularized and explainable machine learning framework for anthropogenic-climate coupled prediction of wastewater influent in hyper-arid urban utilities
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Summary
This summary is machine-generated.Predicting wastewater influent is crucial for efficient operations in data-limited utilities. Penalized linear models like LASSO offer reliable, low-cost forecasts, aiding in water resource management.
Area Of Science
- Environmental Engineering
- Water Resource Management
- Data Science
Background
- Reliable wastewater influent prediction is vital for energy-efficient operation in climate-sensitive, data-limited water utilities.
- Accurate forecasting supports operational planning and resource management.
Purpose Of The Study
- To benchmark various predictive models for monthly wastewater influent in Kuwait.
- To identify the most effective forecasting methods using key environmental and consumption variables.
Main Methods
- Comparative analysis of stepwise linear regression (SLR), ensemble trees (ET), support vector machines (SVM), kernel approximation, and penalized linear regression (LASSO).
- Utilized air temperature, relative humidity, municipal water consumption, and population as predictors.
- Employed five-fold cross-validation with a chronologically held-out test set, evaluating performance with RMSE, MAE, MSE, R², and MAPE.
Main Results
- Penalized linear regression (LASSO) demonstrated the lowest test errors and selected a sparse model specification.
- Stepwise linear regression (SLR) and ensemble trees (ET) showed comparable performance, outperforming kernel methods.
- Model interpretability was enhanced using SHAP summary and feature importance plots.
Conclusions
- Low-complexity, transparent models, especially penalized linear models, offer significant predictive skill with minimal tuning.
- These models enhance operator trust and auditability, crucial for practical implementation.
- The developed framework provides month-ahead guidance for load management, storage, reuse planning, and alternative water supply decisions in hyper-arid regions.
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