用气象数据预测污水渗滤温度，优化土壤-地下水处理系统运行
Predictive Modeling of Effluent Temperature in SAT Systems Using Ambient Meteorological Data: Implications for Infiltration Management
- 基于气象数据构建多元线性回归模型预测渗滤水温
- 模型准确率高达R²=0.86-0.87，可回溯10年温度变化
- 适用于实时监测与长期运维规划，尤其适合季节性运行系统
精确预测再生水池中渗滤水温度对优化土壤-地下水处理（SAT）过程至关重要，因温度直接影响水体黏度和入渗速率。本研究利用环境气象数据，针对沙夫丹SAT系统上层渗滤区的出水温度，评估了多元线性回归（MLR）、神经网络（NN）和随机森林（RF）的预测性能。其中，因操作简便且表现稳健，多元线性回归模型被优选，其预测准确率达R²=0.86–0.87。模型用于估算长达10年的出水温度变化，揭示明显的季节性温变规律，并强调表层土壤温度在控制渗滤水热分布中的关键作用。研究提供了可用于实时监控与长期规划的实际计算公式。
原文摘要 · Abstract (English)
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.
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