Predictive Modeling of Effluent Temperature in SAT Systems Using Ambient Meteorological Data: Implications for Infiltration Management

TL;DR

This study develops and evaluates predictive models for effluent temperature in SAT recharge basins using meteorological data, highlighting the effectiveness of simple models like MLR for operational planning.

Contribution

It introduces a practical, high-accuracy linear regression model for effluent temperature prediction in SAT systems based on ambient meteorological data.

Findings

MLR achieved R2 of 0.86-0.87 in predictions

Seasonal temperature cycles significantly affect effluent temperature

Topsoil temperature is a key factor in thermal profile

Abstract

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.