Prediction of water quality index using artificial neural network and multiple linear regression modelling approach in Shivganga River basin, India

Kadam, Ajaykumar; Wagh, Vasant; Muley, Aniket; Umrikar, Bhavana; Sankhua, R. N.

doi:10.1007/s40808-019-00581-3

Cited by 186 publications

(66 citation statements)

References 47 publications

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“…Kadam et al. [ 14 ] uses Artificial Neural Network and Multiple Linear Regression to predicting ground water quality fitness for drinking from Shivganga river basin located in the eastern slope of the western Ghats, India. Quasi-Monte Carlo combined with multiple linear regression (QMC-MLR) is suggested by Xu and Yan [ 15 ] to solve the calculation of probabilistic load flow (PLF).…”

Section: Discussionmentioning

confidence: 99%

Prediction of new active cases of coronavirus disease (COVID-19) pandemic using multiple linear regression model

Rath

Tripathy

2020

Diabetes & Metabolic Syndrome: Clinical Research & Revi

186

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Introduction and Aims The COVID-19 pandemic originated from the city of Wuhan of China has highly affected the health, socio-economic and financial matters of the different countries of the world. India is one of the countries which is affected by the disease and thousands of people on daily basis are getting infected. In this paper, an analysis of daily statistics of people affected by the disease are taken into account to predict the next days trend in the active cases in Odisha as well as India. Material and methods A valid global data set is collected from the WHO daily statistics and correlation among the total confirmed, active, deceased, positive cases are stated in this paper. Regression model such as Linear and Multiple Linear Regression techniques are applied to the data set to visualize the trend of the affected cases. Results Here a comparison of Linear Regression and Multiple Linear Regression model is performed where the score of the model tends to be 0.99 and 1.0 which indicates a strong prediction model to forecast the next coming days active cases. Using the Multiple Linear Regression model as on July month, the forecast value of 52,290 active cases are predicted towards the next month of 15th August in India and 9,358 active cases in Odisha if situation continues like this way. Conclusion These models acquired remarkable accuracy in COVID-19 recognition. A strong correlation factor determines the relationship among the dependent (active) with the independent variables (positive, deceased, recovered).

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Section: Discussionmentioning

confidence: 99%

Prediction of new active cases of coronavirus disease (COVID-19) pandemic using multiple linear regression model

Rath

Tripathy

2020

Diabetes & Metabolic Syndrome: Clinical Research & Revi

186

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“…The result of this study is supporting the findings of previous studies that mentioned in the literature review. According to Kadam et al [10], ANN model predictions are reasonable and confirm consistently acceptable performance during pre-and post-monsoon seasons. Furthermore, according to the result of Stamenković et al [11], ANN was a reliable model to predict the nitrate concentration.…”

Section: Resultsmentioning

confidence: 83%

“…Kadam et al [10] applied ANN and MLR methods to forecast groundwater quality fitness for drinking from the Shivganga River basin, located on the eastern slopes of India 's western Ghats area. They collected thirty-four (34) representative groundwater samples and analyzed the pre-and post-monsoon seasons of 2015 for major cations and anions.…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Neural Network for Forecasting Nitrate Concentration as a Water Quality Parameter: A Case Study of Feitsui Reservoir, Taiwan

Latif¹,

Azmi²,

Ahmed³

et al. 2020

IJDNE

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Water resources play a vital role in various economies such as agriculture, forestry, cattle farming, hydropower generation, fisheries, industrial activity, and other creative activities, as well as the need for drinking water. Monitoring the water quality parameters in rivers is becoming increasingly relevant as freshwater is increasingly being used. In this study, the artificial neural network (ANN) model was developed and applied to predict nitrate (NO3) as a water quality parameter (WQP) in the Feitsui reservoir, Taiwan. For the input of the model, five water quality parameters were monitored and used namely, ammonium (NH3), nitrogen dioxide (NO2), dissolved oxygen (DO), nitrate (NO3) and phosphate (PO4) as input parameters. As a statistical measurement, the correlation coefficient (R) is used to evaluate the performance of the model. The result shows that ANN is an accurate model for predicting nitrate as a water quality parameter in the Feitsui reservoir. The regression value for the training, testing, validation, and overall are 0.92, 0.93, 0.99, and 0.94, respectively.

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“…The weightage was assigned to each individual parameters based on the perceived threat to water quality. The weights were assigned on scale of 1-5 based on their importance in drinking and threatening potential to human health [29][30][31][32]. The maximum weight 5 was assigned to TDS, Cl and NO 3 due to their major importance in drinking suitability.…”

Section: Conceptualization Of Water Quality Index (Wqi)mentioning

confidence: 99%

Study of groundwater hydrochemistry and drinking suitability through Water Quality Index (WQI) modelling in Kadava river basin, India

et al. 2019

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The present study aims to know the groundwater hydrochemistry and its drinking suitability in Kadava river basin through Water Quality Index (WQI) modelling approach. Eighty (80) dug/bore well samples were collected and analysed for the span of dry (pre) and wet (post) monsoon seasons of 2012 by following standard procedures of APHA. According to BIS standard, the parameters, viz., pH, Ca, Mg, Na, Cl, NO 3 , TH and TDS, surpass the threshold limits in both the seasons. It is observed that TDS content in pre-(75%) and post (97.5%)-monsoon seasons is exceeding the desirable limit (500 mg/l). Also, 67.5% (pre-monsoon) and 75% (post-monsoon) of the groundwater samples are beyond the nitrate permissible limit (45 mg/l) of the BIS. The magnesium content in 15 and 37.5% samples is surpassing the permissible limit (100 mg/l) in pre-and post-monsoon seasons. TH content in 15 and 25% samples surpassed the permissible limit (600 mg/l) in pre-and post-monsoon seasons. WQI results demonstrated that 50.0 and 22.5% samples come under good quality of water; 47.5 and 72.5% samples fall in poor water category, while 2.5 and 5.0% samples exhibit very poor category in pre-/ post-monsoon seasons. Hydrochemical characterization based on Durov plot, Chaddha diagram, Gibbs plot and various scatter plots was used to ascertain the types of weathering, influence of rock, precipitation and evaporation, types of reactions, etc., influenced on groundwater composition of the region. The groundwater chemistry is mostly deteriorated due to the influence of agricultural and anthropogenic activities. Spatial variation map of WQI exhibits that groundwater quality is affected mainly in south and few patches in north regions of the study area.

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Prediction of water quality index using artificial neural network and multiple linear regression modelling approach in Shivganga River basin, India

Cited by 186 publications

References 47 publications

Prediction of new active cases of coronavirus disease (COVID-19) pandemic using multiple linear regression model

Prediction of new active cases of coronavirus disease (COVID-19) pandemic using multiple linear regression model

Application of Artificial Neural Network for Forecasting Nitrate Concentration as a Water Quality Parameter: A Case Study of Feitsui Reservoir, Taiwan

Study of groundwater hydrochemistry and drinking suitability through Water Quality Index (WQI) modelling in Kadava river basin, India

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