Comparing ARIMA and various deep learning models for long-term water quality index forecasting in Dez River, Iran

Niknam, Amir Reza R.; Sabaghzadeh, Maryam; Barzkar, Ali; Shishebori, Davood

doi:10.1007/s11356-024-32228-x

Cited by 2 publications

(1 citation statement)

References 57 publications

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“…Under the lockdown circumstances, it is vital to quantify influences that industrial activities and humans' actions had on different aspects of environment (Patel et al 2020;Yunus et al 2020). Water plays a crucial role as the primary resource for maintaining ecosystem stability, being indispensable for both human populations and natural environments (Niknam et al 2024). Consequently, evaluating the quality of water is of paramount significance.…”

Section: Introductionmentioning

confidence: 99%

Assessing the impact of COVID-19 lockdowns on water quality in Anzali Wetland, Iran, using remote sensing data

Goodarzi,

Niknam,

Sabaghzadeh

et al. 2024

Water Practice &Amp; Technology

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This study investigates temporal variations of two water quality indices, namely, turbidity and chlorophyll-a (Chl-a), from 2016 to 2021 in the Anzali Wetland. For this purpose, ground-based measurements were collected at four stations from 2016 to 2019, while Sentinel-2/MSI images were utilized to predict Chl-a and turbidity from 2016 to 2021. To validate the forecasted results, remote sensing data for Chl-a and turbidity were compared to measurements. After the validation step, Chl-a and turbidity were predicted using remotely sensed data for the first and fourth peaks of the COVID-19 pandemic, which coincide with the COVID-19 lockdowns in 2020 and 2021 in Iran, respectively. The results indicate 26 and 16% decrease in Chl-a and turbidity, respectively, in the COVID-19 lockdown imposed in 2020, while it yielded to 1% decrease in turbidity and 21% increase in the Chl-a concentration in the COVID-19 lockdown applied in 2021, compared to the corresponding mean values measured from 2016 to 2019. In conclusion, the lockdown imposed in 2020, which turned out to be a much more restricted quarantine than the one applied in 2021, was found to be one of the primary reasons behind improving water quality indices of Anzali Wetland in March and April of 2020.

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Section: Introductionmentioning

confidence: 99%

Assessing the impact of COVID-19 lockdowns on water quality in Anzali Wetland, Iran, using remote sensing data

Goodarzi,

Niknam,

Sabaghzadeh

et al. 2024

Water Practice &Amp; Technology

View full text Add to dashboard Cite

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The Characteristics of ARMA (ARIMA) Model and Some Key Points to Be Noted in Application: A Case Study of Changtan Reservoir, Zhejiang Province, China

Liu,

Cui,

Ding

et al. 2024

Sustainability

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Accurate water quality prediction is the basis for good water environment management and sustainable use of water resources. As an important time series forecasting model, the Autoregressive Moving Average Model (ARMA) plays a crucial role in environmental management and sustainability research. This study addresses the factors that affect the ARMA model’s forecast accuracy and goodness of fit. The research results show that the sample size used for model parameters estimation is the main influencing factor for the goodness of fit of an ARMA model, and the prediction time is the main factor affecting the prediction error of the model. Constructing a stable and reliable ARMA model requires a certain number of samples for the estimation of model parameters. However, using an excessive number of samples will not further improve the ARMA model’s goodness of fit but rather increase the workload and difficulty of data collection. The ARMA model is not suitable for long-term forecasting because the prediction error of ARMA models increases with the increase of prediction time, and when the prediction time exceeds a certain limit, the fitted values of an ARMA model will almost no longer change with the time, which means the model has lost its significance of prediction. For time series with periodic components, introducing periodic adjustment factors into the ARMA model can reduce the prediction error. These findings enable environmental managers and researchers to apply the ARMA model more rationally, hence developing more precise pollution control and sustainable development plans.

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Comparing ARIMA and various deep learning models for long-term water quality index forecasting in Dez River, Iran

Cited by 2 publications

References 57 publications

Assessing the impact of COVID-19 lockdowns on water quality in Anzali Wetland, Iran, using remote sensing data

Assessing the impact of COVID-19 lockdowns on water quality in Anzali Wetland, Iran, using remote sensing data

The Characteristics of ARMA (ARIMA) Model and Some Key Points to Be Noted in Application: A Case Study of Changtan Reservoir, Zhejiang Province, China

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