A Methodology for Forecasting Dissolved Oxygen in Urban Streams

Abstract

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.

Warming Vistula River – the effects of climate and local conditions on water temperature in one of the largest rivers in Europe

“…The issue is important in the context of use of waters of the Vistula River for economic purposes (provision of the population with drinkable water, irrigation, industry), as well as in reference to ac-tivities aimed at its protection against degradation. Dissolved oxygen concentration is the basic indicator of the state of water habitats, and its modelling is of key importance for the management of water quality in rivers (Stajkowski et al, 2020). Higher water temperature in a river, particularly in summer, also results in an increase in the amount of suspension of organic origin (Skolasińska and Nowak, 2018), leading to water turbidity, and therefore to worsening of the life conditions of many animals and plants inhabiting the river.…”

A New Approach for Estimating Dissolved Oxygen Based on a High-Accuracy Surface Modeling Method

“…Previous studies predicted DO levels using several environmental and meteorological variables as inputs [16,24,37,38]. However, the conclusions were inconsistent due to variations in the study area and spatiotemporal scales.…”

“…Harvey et al [15] established a regression model to predict the monthly water temperature and DO level. Stajkowski used an autoregressive integrated moving average (ARIMA) model to estimate water quality parameters and demonstrated its capability in DO concentration prediction [16]. In many statistical methods, the geostatistical method known as kriging has been widely applied, and cokriging is an extension of kriging used when estimating one variable from other variables [17,18].…”

A Feature-Based Analysis for Time-Series Classification of COVID-19 Incidence in Chile: A Case Study

“…The mobility index corresponds to the number of trips within a specific commune normalized by the number of commune residents. We standardized these datasets, i.e., we centered the values around the mean with unit standard deviation [47]. Standardization is one of the feature scaling techniques that aim to make the gradient descent converge faster to minimum values in neural network-based algorithms or make all features contribute equally in the case of distance-based algorithms such as SVM [48].…”