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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.04050 |
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| _version_ | 1866915373243170816 |
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| author | Elkayam, Roy |
| author_facet | Elkayam, Roy |
| contents | Accurate prediction of effluent temperature in recharge basins is essential for optimizing the Soil Aquifer Treatment (SAT) process, as temperature directly influences water viscosity and infiltration rates. This study develops and evaluates predictive models for effluent temperature in the upper recharge layer of a Shafdan SAT system recharge basin using ambient meteorological data. Multiple linear regression (MLR), neural networks (NN), and random forests (RF) were tested for their predictive accuracy and interpretability. The MLR model, preferred for its operational simplicity and robust performance, achieved high predictive accuracy (R2 = 0.86-0.87) and was used to estimate effluent temperatures over a 10-year period. Results highlight pronounced seasonal temperature cycles and the importance of topsoil temperature in governing the thermal profile of the infiltrating effluent. The study provides practical equations for real-time monitoring and long-term planning of SAT operations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_04050 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Predictive Modeling of Effluent Temperature in SAT Systems Using Ambient Meteorological Data: Implications for Infiltration Management Elkayam, Roy Machine Learning Artificial Intelligence Accurate prediction of effluent temperature in recharge basins is essential for optimizing the Soil Aquifer Treatment (SAT) process, as temperature directly influences water viscosity and infiltration rates. This study develops and evaluates predictive models for effluent temperature in the upper recharge layer of a Shafdan SAT system recharge basin using ambient meteorological data. Multiple linear regression (MLR), neural networks (NN), and random forests (RF) were tested for their predictive accuracy and interpretability. The MLR model, preferred for its operational simplicity and robust performance, achieved high predictive accuracy (R2 = 0.86-0.87) and was used to estimate effluent temperatures over a 10-year period. Results highlight pronounced seasonal temperature cycles and the importance of topsoil temperature in governing the thermal profile of the infiltrating effluent. The study provides practical equations for real-time monitoring and long-term planning of SAT operations. |
| title | Predictive Modeling of Effluent Temperature in SAT Systems Using Ambient Meteorological Data: Implications for Infiltration Management |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2507.04050 |