The consequences of disposing wastewater in an endorheic wetland in southwest Iran

Jafari, Hadi; Bagheri, Rahim; Forghani, Giti; Esmaeili, Mahin

doi:10.1007/s10661-015-4560-0

Cited by 6 publications

(1 citation statement)

References 25 publications

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“…Human activities such as agriculture and industrialization significantly impact the region's hydrological system (Nassery et al, 2009;Rashidi and Hosseinzadeh, 2019). Agricultural practices, particularly irrigation, heavily rely on E, which can lower the water level and alter the natural balance (Jafari et al, 2015;Neissi et al, 2020). Industrial activities and urbanization contribute to changes in surface runoff patterns and can introduce pollutants into water sources (Rashidi and Hosseinzadeh, 2019;Ziyari and Latifi, 2022).…”

Section: Spearman's and Pearson's Correlation And Error Metricsmentioning

confidence: 99%

Predicting groundwater level using traditional and deep machine learning algorithms

Feng,

Ghorbani,

Radwan

2024

Front. Environ. Sci.

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This research aims to evaluate various traditional or deep machine learning algorithms for the prediction of groundwater level (GWL) using three key input variables specific to Izeh City in the Khuzestan province of Iran: groundwater extraction rate (E), rainfall rate (R), and river flow rate (P) (with 3 km distance). Various traditional and deep machine learning (DML) algorithms, including convolutional neural network (CNN), recurrent neural network (RNN), support vector machine (SVM), decision tree (DT), random forest (RF), and generative adversarial network (GAN), were evaluated. The convolutional neural network (CNN) algorithm demonstrated superior performance among all the algorithms evaluated in this study. The CNN model exhibited robustness against noise and variability, scalability for handling large datasets with multiple input variables, and parallelization capabilities for fast processing. Moreover, it autonomously learned and identified data patterns, resulting in fewer outlier predictions. The CNN model achieved the highest accuracy in GWL prediction, with an RMSE of 0.0558 and an R2 of 0.9948. It also showed no outlier data predictions, indicating its reliability. Spearman and Pearson correlation analyses revealed that P and E were the dataset’s most influential variables on GWL. This research has significant implications for water resource management in Izeh City and the Khuzestan province of Iran, aiding in conservation efforts and increasing local crop productivity. The approach can also be applied to predicting GWL in various global regions facing water scarcity due to population growth. Future researchers are encouraged to consider these factors for more accurate GWL predictions. Additionally, the CNN algorithm’s performance can be further enhanced by incorporating additional input variables.

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Section: Spearman's and Pearson's Correlation And Error Metricsmentioning

confidence: 99%

Predicting groundwater level using traditional and deep machine learning algorithms

Feng,

Ghorbani,

Radwan

2024

Front. Environ. Sci.

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Impact of methionine on a partial-nitritation biofilter

González‐Martínez

Rodríguez-Sánchez

García-Ruiz

et al. 2015

Environ Sci Pollut Res

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It has been demonstrated that an anaerobic digestion process cannot attain an efficient removal of several amino acids, with methionine being one of the most persistent of these. Thus, the effect that methionine amino acid has over the partial-nitritation process with fixed-biofilm configuration in terms of performance and bacterial community dynamics has been investigated. With respect to the performance with no addition, 100 mg/L methionine loading decreased ammonium oxidation efficiency in 60% and 100% at concentrations of 300 and 500 mg/L methionine, respectively. Bacterial biomass sharply increased by 30, 65, and 230% with the addition of 100, 300, and 500 mg/L methionine, respectively. Bacterial community analysis showed that methionine addition supported the proliferation of a diversity of heterotrophic genera, such as Lysobacter and Micavibrio, and reduced the relative abundance of ammonium oxidizing genus Nitrosomonas. This research shows that the addition of methionine affects the performance of the partial-nitritation process. In this sense, amino acids can pose a threat for the of partial-nitritation process treating anaerobic digester supernatant at full-scale implementation.

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Hydrological and hydrogeological characteristics and environmental assessment of Hashilan Wetland, a national heritage in NW Iran

Abbasi

Ashayeri

Jafarzadeh

et al. 2022

Ecohydrology & Hydrobiology

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The consequences of disposing wastewater in an endorheic wetland in southwest Iran

Cited by 6 publications

References 25 publications

Predicting groundwater level using traditional and deep machine learning algorithms

Predicting groundwater level using traditional and deep machine learning algorithms

Impact of methionine on a partial-nitritation biofilter

Hydrological and hydrogeological characteristics and environmental assessment of Hashilan Wetland, a national heritage in NW Iran

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