Estimating ammonium changes in pilot and full-scale constructed wetlands using kinetic model, linear regression, and machine learning

Nguyen, X. Cuong; Nguyen, T. Phuong; Lam, V. Son; Le, Phuoc-Cuong; Vo, T. Dieu Hien; Hoang, Thu-Huong Thi; Chung, W. Jin; Chang, S. Woong; Nguyen, D. Duc

doi:10.1016/j.scitotenv.2023.168142

Cited by 10 publications

(2 citation statements)

References 55 publications

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“…TTOP ≤ 0, Permafrost TTOP > 0, Seasonally frozen ground (5) 2.3.2. Regression Model [48,49] The ground freeze-thaw index (DDT/DDF) of the TTOP model is calculated based on GST. Currently, there are two methods for obtaining GST data: remote-sensing-based retrieval and meteorological station measurements.…”

Section: Ttop Modelmentioning

confidence: 99%

The Impact of Permafrost Change on Soil Organic Carbon Stocks in Northeast China

Song,

Huang,

Zhang

et al. 2023

Forests

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Climate warming has resulted in significant changes in permafrost in Northeast China, leading to notable alterations in soil organic carbon (SOC) stocks. These changes are crucial for both the global carbon cycle and climate change, as well as directly impacting the sustainable development of ecosystems. In order to examine the SOC dynamics and the impact of permafrost changes on SOC, we investigate the changes of permafrost extent based on a regression model and TTOP (top temperature of permafrost) model and the relationship between land use and land cover (LULC), SOC stocks, and permafrost changes in Northeast China. The results showing a shrinking permafrost area from 37.43 × 104 km2 to 16.48 × 104 km2 during the period from the 1980s to the 2010s in Northeast China, and the SOC stock decreased by 24.18 Tg C from the 1980s to the 1990s and then rapidly increased by 102.84 Tg C in the 2000s. Permafrost degradation speeds up the succession of LULC, impacting about 90% of the SOC in permafrost regions. The relationship between permafrost changes and SOC in Northeast China shows that permafrost degradation significantly reduces SOC stocks in the short term but increases SOC stocks in the long term, and that LULC play a crucial role in regulating this relationship. The goals of this study are to acquire an understanding of permafrost status and deepening insights into the dynamics of SOC. Simultaneously, the study aims to furnish valuable scientific references for shaping policies on sustainable land use and management in the future, all the while advancing the cause of ecological equilibrium and sustainable development in Northeast China and other areas.

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Section: Ttop Modelmentioning

confidence: 99%

The Impact of Permafrost Change on Soil Organic Carbon Stocks in Northeast China

Song,

Huang,

Zhang

et al. 2023

Forests

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“…The objective of this work was to derive k values suitable for the optimal design of HFCWs treating low-OLR systems. The machine learning (ML) approach was followed as ML is better suited to solve complex problems due to its ability to generate robust nonlinear relationships among the datasets and build models correlating input and output parameters. − ML algorithms, namely, Support Vector Regression (SVR), Random Forest (RF), and Artificial Neural Networks (ANN), were used for predicting the k values for HFCWs treating low-OLR wastewater. In our previous study, the performance data of 74 HFCWs from secondary sources were used to calculate the k values for nutrient removal.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Application for Nutrient Removal Rate Coefficient Analyses in Horizontal Flow Constructed Wetlands

Singh,

Soti,

Kulshreshtha

et al. 2024

ACS EST Water

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Land area optimization for horizontal flow constructed wetlands (HFCWs) with a low organic loading rate (OLR) needs special considerations as the microflora changes dramatically with the OLR. The P-k-C* approach does not lead to an accurate calculation of k-values in these wetlands. In this research, nonlinear machine learning models [Support Vector Regression (SVR), Random Forest (RF), and Artificial Neural Networks (ANN)] are applied to predict realistic k-values. Data from 37 low-OLR HFCWs (n = 544) were analyzed, and the k-values calculated for these wetlands were found to vary markedly (0.059−0.249 with an average of 0.113 ± 0.090 m/day). The classification of k-values based on the OLR, applied loading rate, and media depth leads to the reduction in standard deviations (SDs) from 83.40 to 35.27%. kvalues with the least SDs are needed for optimal design for low-OLR CWs. The SVR, RF, and ANN models were tested, and the best prediction efficiency on testing datasets was achieved through the ANN model with R 2 (k TKN )= 0.768 (RMSE = 0.067) for total Kjeldahl nitrogen (TKN), R 2 (k TN )= 0.835 (RMSE = 0.043) for total nitrogen (TN), and R 2 (k TP ) = 0.723 (RMSE = 0.087) for total phosphorus (TP). The outcome was validated using primary data from HFCWs, which also confirmed the superiority of the ANNbased model, which can be used for design customization of low-OLR HFCWs.

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Machine learning analysis/optimization of auxetic performance of a polymeric meta-hybrid structure of re-entrant and meta-trichiral

Zhou,

Leng,

Dutta

et al. 2025

European Journal of Mechanics - A/Solids

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Estimating ammonium changes in pilot and full-scale constructed wetlands using kinetic model, linear regression, and machine learning

Cited by 10 publications

References 55 publications

The Impact of Permafrost Change on Soil Organic Carbon Stocks in Northeast China

The Impact of Permafrost Change on Soil Organic Carbon Stocks in Northeast China

Machine Learning Application for Nutrient Removal Rate Coefficient Analyses in Horizontal Flow Constructed Wetlands

Machine learning analysis/optimization of auxetic performance of a polymeric meta-hybrid structure of re-entrant and meta-trichiral

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