Evaluation of the fast orthogonal search method for forecasting chloride levels in the Deltona groundwater supply (Florida, USA)

El-Jaat, Majda; Hulley, Mike; Tétreault, Michel

doi:10.1007/s10040-017-1719-5

Cited by 4 publications

(3 citation statements)

References 25 publications

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“…The proposed GNN-SAGE architecture surpasses the results found in both studies, providing a superior coefficient of determination of 0.88, meaning an improvement of 3.5% over the regression tree forecasting was achieved. In [28], a data-driven approach was used to evaluate future chloride concentrations in Deltona, Florida. Unlike the other studies mentioned, the authors proposed estimating Cl − concentration for groundwater supply.…”

Section: Discussionmentioning

confidence: 99%

“…Future chloride concentration estimates can also benefit from the data-driven paradigm. In [28], the authors propose a data-driven approach to determine future chloride levels in Florida, USA, for groundwater supply. Their approach showed robust performance when forecasting chloride, reaching RMSE and coefficient of determination (R 2 ) values of 28 mg/L and 0.90, respectively.…”

Section: Introductionmentioning

confidence: 99%

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Graph-Based Deep Learning Model for Forecasting Chloride Concentration in Urban Streams to Protect Salt-Vulnerable Areas

Oliveira Santos,

Costa Rocha,

Thé

et al. 2023

Environments

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In cold-climate regions, road salt is used as a deicer for winter road maintenance. The applied road salt melts ice and snow on roads and can be washed off through storm sewer systems into nearby urban streams, harming the freshwater ecosystem. Therefore, aiming to develop a precise and accurate model to determine future chloride concentration in the Credit River in Ontario, Canada, the present work makes use of a “Graph Neural Network”–“Sample and Aggregate” (GNN-SAGE). The proposed GNN-SAGE is compared to other models, including a Deep Neural Network-based transformer (DNN-Transformer) and a benchmarking persistence model for a 6 h forecasting horizon. The proposed GNN-SAGE surpassed both the benchmarking persistence model and the DNN-Transformer model, achieving RMSE and R2 values of 51.16 ppb and 0.88, respectively. Additionally, a SHAP analysis provides insight into the variables that influence the model’s forecasting, showing the impact of the spatiotemporal neighboring data from the network and the seasonality variables on the model’s result. The GNN-SAGE model shows potential for use in the real-time forecasting of water quality in urban streams, aiding in the development of regulatory policies to protect vulnerable freshwater ecosystems in urban areas.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Graph-Based Deep Learning Model for Forecasting Chloride Concentration in Urban Streams to Protect Salt-Vulnerable Areas

Oliveira Santos,

Costa Rocha,

Thé

et al. 2023

Environments

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show abstract

“…The intrusion at the river mouth is mainly due to natural factors, but there are also human factors (Casillas-Trasvina et al, 2019;Xiao et al, 2021;Hu et al, 2024). The problem of seawater intrusion at river mouths is a major issue affecting the construction of water sources for coastal cities at present and in the future (Liu et al, 2014;El-Jaat et al, 2018;Chen et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Analysis of influencing factors of seawater intrusion in the Yangtze River Estuary and control for water supply security

Chen,

Xu,

Chen

et al. 2024

Front. Mar. Sci.

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Seawater intrusion poses a significant threat to the water supply of coastal cities both presently and in the future. It is crucial to identify the controllable factors influencing seawater intrusion, both natural and anthropogenic, in order to ensure water supply security. This study examined seawater intrusion characteristics using monitoring data from 1994 to 2019. Factors such as daily flow rate, duration of intrusion, water quality, and tidal level were analyzed to establish correlations and identify the primary influencing factors in the Yangtze River Estuary. The findings reveal that seawater intrusion in this area is most prevalent from November to April, peaking in February and March. The key controllable factors affecting chloride levels at the intake are the daily flow rate at Datong Station and the tidal range at Xuliujing Station. Additionally, the study proposes control methods to safeguard water supply, including providing daily flow rate values for flushing seawater intrusion at Datong Station under different tidal ranges and intrusion durations. These research results provide valuable guidance for the emergency operation of the Three Gorges-centered reservoir group against seawater intrusion.

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Groundwater contaminant source identification via Bayesian model selection and uncertainty quantification

Cao

Wang

et al. 2019

Hydrogeol J

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Evaluation of the fast orthogonal search method for forecasting chloride levels in the Deltona groundwater supply (Florida, USA)

Cited by 4 publications

References 25 publications

Graph-Based Deep Learning Model for Forecasting Chloride Concentration in Urban Streams to Protect Salt-Vulnerable Areas

Graph-Based Deep Learning Model for Forecasting Chloride Concentration in Urban Streams to Protect Salt-Vulnerable Areas

Analysis of influencing factors of seawater intrusion in the Yangtze River Estuary and control for water supply security

Groundwater contaminant source identification via Bayesian model selection and uncertainty quantification

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