Real-time monitoring of river water quality is at the forefront of a proactive urban water management strategy to meet the global challenge of vital freshwater resource sustainability. The concentration of dissolved oxygen (DO) is a primary indicator of the health state of the aquatic habitats, and its modeling is crucial for river water quality management. This paper investigates the importance of the choices of different techniques for preprocessing and stochastic modeling for developing a simple and reliable linear stochastic model for forecasting DO in urban rivers. We describe several methods of evaluation, preprocessing, and modeling for the DO parameter time series in the Credit River, Ontario, Canada, to achieve the optimum data preprocessing and input selection techniques and consequently obtain the optimum performance of the stochastic models as an effective river management tool. The Manly normalization and standardization (Std) methods were chosen for preprocessing the time series. Modeling the preprocessed time series using the stochastic autoregressive integrated moving average (ARIMA) model resulted in very accurate forecasts with a negligible difference from sole normalization and spectral analysis (Sf) methods.

中文翻译：

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预测城市河流中溶解氧的方法

河流水质的实时监测处于积极的城市水资源管理战略的最前沿，以应对重要淡水资源可持续性的全球挑战。溶解氧 (DO) 浓度是水生栖息地健康状况的主要指标，其建模对于河流水质管理至关重要。本文研究了选择不同技术进行预处理和随机建模的重要性，以开发一个简单可靠的线性随机模型来预测城市河流中的溶解氧。我们描述了加拿大安大略省 Credit River 中 DO 参数时间序列的几种评估、预处理和建模方法，实现最佳数据预处理和输入选择技术，从而获得随机模型作为有效河流管理工具的最佳性能。选择曼利归一化和标准化 (Std) 方法来预处理时间序列。使用随机自回归积分移动平均 (ARIMA) 模型对预处理的时间序列进行建模，得出非常准确的预测，与单一归一化和谱分析 (Sf) 方法的差异可以忽略不计。