Pattern recognition of water quality variance in Yamuna River (India) using hierarchical agglomerative cluster and principal component analyses

Arora, Sameer; Keshari, Ashok K.

doi:10.1007/s10661-021-09318-1

Cited by 21 publications

(12 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The four models are designed using temperature, biological oxygen demand (BOD), COD, conductivity and ammonia. The spatial and temporal analysis of data suggests that temperature, BOD, COD and ammonia produces the most significant impact on the variation in DO concentration, hence they are used for the model development (Arora & Keshari 2021b). The first model (M1) is developed considering temperature, BOD and COD as input parameters.…”

Section: Model Development and Evaluationmentioning

confidence: 99%

“…Four models were designed with different input parameters to observe the contribution of each parameter in affecting DO concentration. The spatial and temporal behaviour of the parameters and results of hierarchically aligned cluster analysis and principal component analysis (PCA) is used to select the appropriate parameters (Arora & Keshari 2021b). The input parameters selected to design the FIS models are shown in Table 1.…”

Section: Model Development and Evaluationmentioning

confidence: 99%

“…Monthly water samples were collected for 5 years (Arora & Keshari 2021b) and physiochemical analyses were carried out as per the standard methods (APHA 2005). In-situ measurements were performed for temperature, DO and pH of the water samples.…”

Section: Study Areamentioning

confidence: 99%

“…All the microorganisms, aquatic plants and aquatic animals consume oxygen through respiration, known as sinks and remain active throughout the day and night. In contrast, photosynthesis generates oxygen only during the daytime, and the algae act as both sources and the sink of oxygen (Arora & Keshari 2021b). Another critical factor is solubility, which depends on the water pressure, temperature and salinity.…”

Section: Graphical Abstract Introductionmentioning

confidence: 99%

“…The variation in DO concentration occurs rapidly based on flow available in rivers, velocity, turbulence, the number of organics and atmospheric reactions involved in the riverine system (Cox 2003;Quick et al 2019). Anthropogenic activities are becoming the significant sinks of oxygen that consume the available DO through partially or untreated wastewater from domestic, industrial, commercial and agricultural sectors (Arora & Keshari 2021b). It is mandatory to maintain equilibrium between sources and sinks for the aquatic ecosystem's sustainability.…”

Section: Graphical Abstract Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Dissolved oxygen modelling of the Yamuna River using different ANFIS models

Arora

Keshari

2021

Water Science and Technology

Self Cite

View full text Add to dashboard Cite

Dissolved oxygen is one of the prime parameters for assessing the water quality of any stream. Thus, the accurate estimation of dissolved oxygen is necessary to evolve measures for maintaining the riverine ecosystem and designing the appropriate water quality improvement plans. Machine learning techniques are becoming valuable tools for the prediction and simulation of water quality parameters. A study has been performed in the Delhi stretch of Yamuna River, India, and physiochemical parameters were examined for five years to simulate the dissolved oxygen using various machine learning techniques. Simulation and prediction competencies of adaptive neuro fuzzy inference system – grid partitioning (ANFIS-GP) and subtractive clustering (ANFIS-SC) were performed on high dimensional river characteristics. Four different models (M1, M2, M3 and M4) were developed using different combination of input parameters to predict dissolved oxygen. Results obtained from the models were evaluated using root mean square error (RMSE) and coefficient of determination (R2) to identify the appropriate combination of parameters to simulate the dissolved oxygen. Results suggest that both types of ANFIS models work adequately and accurately predict the DO; however, ANFIS-GP outperforms the ANFIS-SC. M4 generated R2 of 0.953 from ANFIS-GP compared to 0.911 from ANFIS-SC.

show abstract

Section: Model Development and Evaluationmentioning

confidence: 99%

Section: Model Development and Evaluationmentioning

confidence: 99%

Section: Study Areamentioning

confidence: 99%

Section: Graphical Abstract Introductionmentioning

confidence: 99%

Section: Graphical Abstract Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Dissolved oxygen modelling of the Yamuna River using different ANFIS models

Arora

Keshari

2021

Water Science and Technology

Self Cite

View full text Add to dashboard Cite

show abstract

Implication of dissolved nitrogen dynamics of urban rivers using multivariate regression and structural equation modeling

Arora

Keshari

2023

CLEAN Soil Air Water

Self Cite

View full text Add to dashboard Cite

The river's functional balance varies according to flow regimes and nutrient distribution. Watercourses can receive water from different point and nonpoint sources, which discharge high nitrogen load in rivers generated from domestic and industrial effluents and agricultural runoff. The excessive concentration of nitrogen load affects the riverine ecosystem by attenuating dissolved oxygen balance and disturbing the water quality. A study has been conducted to determine the nitrogen dynamics and their impact on the water quality of the Delhi stretch of Yamuna River, India, which is highly influenced by anthropogenic activities. Multivariate linear regression (MLR) was performed to gain insight into nitrogen dynamics, and structural equation modeling was applied to determine the direct (primary) and indirect (secondary) relation between the water quality parameters and the subsequent impact on water quality. Dissolved inorganic nitrogen (DIN) was measured using ammonia, nitrate, and nitrite, and total Kjeldahl nitrogen was estimated to reflect the dissolved organic nitrogen (DON). The variation in dissolved nitrogen (organic and inorganic) signifies high negative flux on the river's organic content, causing further degradation of river water quality. MLR predicted a high correlation between predicted and observed DIN at the Income Tax Office (ITO) (92.5%) and Nizamuddin (94.37%) and DON at ITO (93.49%) and Nizamuddin (95.28%). The high concentration during the lean period deteriorates the water quality tremendously and suggests that the variation in dissolved nitrogen content should be monitored carefully to maintain the river's water quality dominated by the organic load.

show abstract