Dissolved oxygen prediction using a new ensemble method

Kişi, Özgür; Alizamir, Meysam; Gorgij, Alireza Docheshmeh

doi:10.1007/s11356-019-07574-w

Cited by 69 publications

(20 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the prediction of dissolved oxygen, pH and water temperature are regarded as the most reliable parameters [143,144,160,161]. The fluctuation of pH affects the photosynthesis rate of aquatic life, higher photosynthesis results in higher oxygen released.…”

Section: Figure 3 Relation Of Input Attributes Modelling Techniques and Performance Metrics Of Water Quality Prediction Reviewed In This mentioning

confidence: 99%

Water, Soil and Air Pollutants’ Interaction on Mangrove Ecosystem and Corresponding Artificial Intelligence Techniques Used in Decision Support Systems - A Review

et al. 2021

View full text Add to dashboard Cite

The feasibility of artificial intelligence (AI) as a predictive model for thorough efficacy analysis on environmental pollution applied on mangrove forests are discussed. Mangrove forests are among the most productive and biological diverse ecosystems on the planet. However, due to environmental pollution and climate change, mangrove forests are in serious decline. Despite crucial issues pertaining mangrove forests, the law enforcement on the ecosystem is still dubious due to the lack of evidence and data that could provide accurate analysis and prediction. The main highlight of this review elaborates on pollutant markers in soil, water, and air, by correlating these three aspects to the sustainability of mangrove ecosystem. The research gap identified from this review suggests the application of an integrated environmental prediction system for practical environmental insights. A predictive model for environmental decision-making could be developed by integrating meteorological, climatological, hydrological, atmospheric, and heavy metal concentration to understand the interaction between each factor for an efficient solution of pollutant reduction scheme involving mangrove ecosystems. INDEX TERMS Mangrove estuarine; pollutant interaction; environmental quality modelling; integrated environmental decision system.

show abstract

Section: Figure 3 Relation Of Input Attributes Modelling Techniques and Performance Metrics Of Water Quality Prediction Reviewed In This mentioning

confidence: 99%

Water, Soil and Air Pollutants’ Interaction on Mangrove Ecosystem and Corresponding Artificial Intelligence Techniques Used in Decision Support Systems - A Review

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The response variables that were selected are those presented in at Water Treatment Plants, such as changes in the dosages of coagulants and auxiliaries, and the treatment of turbid waters for human consumption is expensive (CETESB, 2016). The parameter DO was chosen because it is an important parameter with regard to water quality and fundamental for maintaining the dynamics of aquatic ecosystems, furthermore, it shows the balance between the processes that produce and consume oxygen in the body of water (Ahmed, 2017), being one of the main ones regarding the monitoring of water quality (Kisi et al, 2020). An adequate supply of DO is essential for the maintenance of self-depuration processes in natural aquatic systems (CETESB, 2016).…”

Section: Selection Of Water Quality Parametersmentioning

confidence: 99%

Impact of Changes in Land Use and Land Cover in the Contribution Basin of Doutor João Penido Water Supply Reservoir of Juiz de Fora, MG, Brazil

Lana¹,

Oliveira²,

Paula³

et al. 2021

JMS

View full text Add to dashboard Cite

Changes in the land use and land cover in areas adjacent to water reservoirs directly affect the quality of this water. This research presents a study on the water quality in the basin of one of the most important public water supply reservoirs in the city of Juiz de Fora, Minas Gerais. The main objective of this study was to analyze the behavior of limnological parameters and the correlation with land use and land cover in the contribution basin of the Doutor João Penido reservoir (CBJPR). The methodology was based on the analysis of water quality parameters, related to water samples collected from 2012 to 2015. Six sampling points were chosen from different locations: spring, medium course, main tributaries of the reservoir and the reservoir catchment. The parameters analyzed were turbidity, total solids (TS), oxygen consumed (OC), dissolved oxygen (DO), electrical conductivity, total nitrogen (TN), total phosphorus (TP), E. Coli, temperature, pH and total dissolved solids (TDS). The Kendall’s tau test was used to analyze the correlations between the parameters of water quality, land use and land cover in the CBJPR. In general, measured parameters showed better results in spring and in reservoir catchment, showing a worse quality of the water along the tributaries and the dilution power of the reservoir. The correlations pointed to the need for protection and preservation of forests in strategic locations to ensure good water quality.

show abstract

“…These variables were flow rate (Q), WT, pH, EC, specific conductivity (SC), water depth (WD), total solids (TS), total alkalinity (TA), water hardness (WH), air temperature (AT), nitrite ion (NO 2 − ), nitrate ion (NO 3 − ), ammonium ion (NH 4 + ), phosphate ion (PO 4 3− ), total phosphorus (TP), chemical oxygen demand (COD), sulfate ion (SO 4 2− ), sodium ion (Na + ), potassium ion (K + ), calcium ion (Ca 2+ ), chloride ion (Cl − ), and biochemical oxygen demand (BOD). Taking into account the literature review [29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48], the WT, the EC, and the pH (which are most effective in modeling studies) were selected as the independent variables.…”

Section: Modeling Variablesmentioning

confidence: 99%

Spatial Forecasting of Dissolved Oxygen Concentration in the Eastern Black Sea Basin, Turkey

Nacar

Bayram

BAKİ

et al. 2020

Water

View full text Add to dashboard Cite

The aim of this study was to model, as well as monitor and assess the surface water quality in the Eastern Black Sea (EBS) Basin stream, Turkey. The water-quality indicators monitored monthly for the seven streams were water temperature (WT), pH, total dissolved solids (TDS), and electrical conductivity (EC), as well as luminescent dissolved oxygen (LDO) concentration and saturation. Based on an 18-month data monitoring, the surface water quality variation was spatially and temporally evaluated with reference to the Turkish Surface Water Quality Regulation. First, the teaching–learning based optimization (TLBO) algorithm and conventional regression analysis (CRA) were applied to three different regression forms, i.e., exponential, power, and linear functions, to predict LDO concentrations. Then, the multivariate adaptive regression splines (MARS) method was employed and three performance measures, namely, mean absolute error (MAE), root means square error (RMSE), and Nash Sutcliffe coefficient of efficiency (NSCE) were used to evaluate the performances of the MARS, TLBO, and CRA methods. The monitoring results revealed that all streams showed the same trend in that lower WT values in the winter months resulted in higher LDO concentrations, while higher WT values in summer led to lower LDO concentrations. Similarly, autumn, which presented the higher TDS concentrations brought about higher EC values, while spring, which presented the lower TDS concentrations gave rise to lower EC values. It was concluded that the water quality of the streams in the EBS basin was high-quality water in terms of the parameters monitored in situ, of which the LDO concentration varied from 9.13 to 10.12 mg/L in summer and from 12.31 to 13.26 mg/L in winter. When the prediction accuracies of the three models were compared, it was seen that the MARS method provided more successful results than the other methods. The results of the TLBO and the CRA methods were very close to each other. The RMSE, MAE, and NSCE values were 0.2599 mg/L, 0.2125 mg/L, and 0.9645, respectively, for the best MARS model, while these values were 0.4167 mg/L, 0.3068 mg/L, and 0.9086, respectively, for the best TLBO and CRA models. In general, the LDO concentration could be successfully predicted using the MARS method with various input combinations of WT, EC, and pH variables.

show abstract

Dissolved oxygen prediction using a new ensemble method

Cited by 69 publications

References 49 publications

Water, Soil and Air Pollutants’ Interaction on Mangrove Ecosystem and Corresponding Artificial Intelligence Techniques Used in Decision Support Systems - A Review

Water, Soil and Air Pollutants’ Interaction on Mangrove Ecosystem and Corresponding Artificial Intelligence Techniques Used in Decision Support Systems - A Review

Impact of Changes in Land Use and Land Cover in the Contribution Basin of Doutor João Penido Water Supply Reservoir of Juiz de Fora, MG, Brazil

Spatial Forecasting of Dissolved Oxygen Concentration in the Eastern Black Sea Basin, Turkey

Contact Info

Product

Resources

About