Application of Predictive Intelligence in Water Quality Forecasting of the River Ganga Using Support Vector Machines

2019 ◽  
pp. 206-218
Author(s):  
Anil Kumar Bisht ◽  
Ravendra Singh ◽  
Rakesh Bhutiani ◽  
Ashutosh Bhatt
Keyword(s):  
Water Quality ◽  
Quality Parameters ◽  
Support Vector ◽  
Data Sets ◽  
Water Quality Prediction ◽  
Water Quality Forecasting ◽  
River Ganga ◽  
Best Parameter ◽  
Linear Svm

Predicting the water quality of rivers has attracted a lot of researchers all around the globe. A precise prediction of river water quality may benefit the water management bodies. However, due to the complex relationship existing among various factors, the prediction is a challenging job. Here, the authors attempted to develop a model for forecasting or predicting the water quality of the river Ganga using application of predictive intelligence based on machine learning approach called support vector machine (SVM). The monthly data sets of five water quality parameters from 2001 to 2015 were taken from five sampling stations from Devprayag to Roorkee in the Uttarakhand state of India. The experiments are conducted in Python 2.7.13 language (Anaconda2 4.3.1) using the radial basis function (RBF) as a kernel for developing the non-linear SVM-based classifier as a model for water quality prediction. The results indicated a prediction performance of 96.66% for best parameter combination which proved the significance of predictive intelligence in water quality forecasting.

scholarly journals Computational and Numerical Modeling for Classification of Water Quality of Lake

2020 ◽  
Vol 11 (9) ◽  
pp. 425-431
Author(s):  
Jonalyn G. Ebron ◽  
◽  
Rommel Ivan D. De Leon ◽  
Arviejhay D. Alejandro ◽  
Basaron A. Amoranto
Keyword(s):  
Water Quality ◽  
Linear Regression ◽  
Water Quality Monitoring ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Water Quality Parameter ◽  
Support Vector ◽  
Poor Correlation ◽  
Study Results

In this study, the Multivariate Linear Regression (MLR), Artificial Neural Network (ANN), k-Nearest Neighbour (kNN), and Support Vector Machine (SVM) models had been developed to simulate and to predict the water quality of Laguna Lake. The input variables for the MLR model had been determined through linear regression. The ANN, kNN, and SVM had been modelled per water quality parameter with cross validation and evaluated through its accuracy. The performance of the MLR models had been evaluated with the statistical metrics R-squared, Mean Absolute Error, and Root Mean Square Error. A web-based water quality monitoring had been developed to incorporate in their monitoring. The results had indicated that the performance of SVM is superior in the prediction of classes in most water quality parameters. The study results had shown that the poor correlation between the water quality parameters indicated that the data cannot be modelled. The results had shown that the correlation had not reached the threshold to be significant of 60% for R-squared. As per the classification models, the results of the comparison had shown that SVM had been the best model in the majority of parameters.

scholarly journals Research on Sewage Monitoring and Water Quality Prediction Based on Wireless Sensors and Support Vector Machines

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Cong Liu ◽  
Hongji Li ◽  
Qinkun Zhang
Keyword(s):  
Water Quality ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Wireless Sensor ◽  
Dynamic Monitoring ◽  
Sensor Technology ◽  
Support Vector ◽  
Quality Prediction ◽  
Water Quality Prediction

Water resource protection has an important impact on ecosystem security and human survival. Therefore, water quality testing and early warning of the sewage status are getting more and more attention. In order to solve the problems of information transmission delay and insufficient water quality prediction in current water quality monitoring, this paper proposes a wireless sensor-based dynamic water quality monitoring and prediction technology. Firstly, this paper uses the wireless sensor technology and ZigBee protocol to establish a sewage monitoring model and real-time dynamic monitoring of total nitrogen, total phosphorus, ammonia nitrogen, and other indicators of the water quality of the basin. Secondly, on the basis of wireless monitoring, a support vector algorithm is used to construct a water quality prediction model to make a reasonable prediction of the water quality of the watershed. Finally, the actual test results show that the technology can automatically and real-timely monitor the water quality of the watershed to meet the requirements of water quality monitoring in practical applications.

scholarly journals Assessment of Water Quality Status of Chandrabhaga River at Rishikesh, Dehradun, Uttarakhand, India

2021 ◽  
Vol 16 (1) ◽  
pp. 163-175
Author(s):  
Shail Kulshrestha
Keyword(s):  
Water Quality ◽  
River Water ◽  
Oxygen Demand ◽  
Quality Parameters ◽  
Total Hardness ◽  
The Status ◽  
River Ganga ◽  
Magnesium Adsorption Ratio ◽  
Total Dissolved Solid

In the background of the ambitious ‘National Mission for Clean Ganga’ to clean, conserve and protect the River Ganga in a comprehensive manner, this study was undertaken to evaluate the status of Water Quality of Chandrabhaga river at Rishikesh, district Dehradun, India, between the Dhalwala bridge area and Mayakund area where it merges with the river Ganga, affecting adversely its water quality. The characteristics of Chandrabhaga river water were assessed during July to September 2017 by monitoring the water quality at most garbeged and contaminated five locations by determining physicochemical and biological parameters and metal ions. The observed values of dissolved oxygen (DO) varied from 0.4 to 1.29 mg/L, such a low DO, high biological oxygen demand (BOD, 21 ± 2.64 to 56 ±6.08 mg/L) and much high Coliform (1760 ±13.23 to3180 ±27.61 MPN/ 100 ml) at all the locations reflects the poor water quality of Chandrabhaga River. Recorded values of total dissolved solid (TDS), electrical conductivity (EC), total hardness (TH), alkalinity, phosphate, sodium, potassium and calcium exceeded the WHO standards. Pearson’s correlation analysis revealed the highly positive correlations all the time between EC and TDS, TH and TDS, TH and EC, while during high flow period good correlations were recorded between alkalinity and pH, TDS, EC and TH. Irrigation water quality parameters such as soluble sodium percentage (SSP), sodium adsorption ratio (SAR), magnesium adsorption ratio (MAR) and Kelly’s Ratio was evaluated to test the suitability of river water for irrigation purpose.

scholarly journals Analysis of Different Water Quality Parameters of Ganga River by Multivariate Tools

2020 ◽  
pp. 268-275
Keyword(s):  
Water Quality ◽  
Cluster Analysis ◽  
Regression Analysis ◽  
Fecal Coliform ◽  
Quality Index ◽  
Total Coliform ◽  
Quality Parameters ◽  
P Value ◽  
River Ganga

: This Study Statistically analyzes the deteriorating water quality of the River Ganga. Statistical techniques such as Water Quality index (WQI), Cluster Analysis, Best Subsets Regression and Multiple Regression Analysis were applied to seven water quality parameters, collected from 21 sampling Stations in India. Water Quality Index identified the most polluted stations that are Kadaghat, Allahabad, Khurgi, Patna U/S, Bihar, Varanasi D/S (Malviya Bridge), U.P, Indrapuri, Dehri and Varanasi U/S (ASSIGHAT), U.P. Cluster Analysis for the different Stations showed a similarity of 99.99% between the stations Ganga D/S, Mirzapur , Varanasi D/S (Malviya Bridge) and Varanasi U/S (Assighat), U.P. Cluster Analysis for variables showed a 98.96% similarity of parameter BOD with WQI and 96.06% similarity between the parameters Total Coliform and Fecal Coliform. After applied the Best Subset Regression Analysis we get the highest Mallow c-p value with high R2 for the parameters BOD, Nitrate, Total Coliform and Fecal Coliform. In the Regression analysis the p value for the estimated coefficients of BOD is 0.00, indicates that BOD is significantly related to WQI.In this paper we conclude that BOD is the most critical parameter and we study the comparison of water quality of river Ganga for different stations.

Assessement of Water Quality Parameters in Some Potential Pollutant Areas from Romania

2018 ◽  
Vol 69 (8) ◽  
pp. 2045-2049
Author(s):  
Catalina Gabriela Gheorghe ◽  
Andreea Bondarev ◽  
Ion Onutu
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Surface Water Quality ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Quality Data ◽  
Chemical Parameters ◽  
Water Quality Data ◽  
Pollutant Sources ◽  
Water Quality Forecasting

Monitoring of environmental factors allows the achievement of some important objectives regarding water quality, forecasting, warning and intervention. The aim of this paper is to investigate water quality parameters in some potential pollutant sources from northern, southern and east-southern areas of Romania. Surface water quality data for some selected chemical parameters were collected and analyzed at different points from March to May 2017.

Analysis of Water Quality of River Ganga from Digha Ghat to Gai Ghat in Patna District, Bihar, India

2017 ◽  
Vol 3 (5) ◽  
pp. 1350-1354
Author(s):  
Sushil Kumar Singh ◽  
Manish Kumar Kanth ◽  
Dhirendra Kumar ◽  
Rishikesh Raj ◽  
Abhijeet Kashyap ◽  
...  
Keyword(s):  
Water Quality ◽  
River Ganga

Environmental Impact Evaluation of Istanbul Wastewater Treatment and Marine Disposal Systems

1992 ◽  
Vol 25 (9) ◽  
pp. 85-92 ◽  
Author(s):  
I. Ozturk ◽  
T. Zambal ◽  
A. Samsunlu ◽  
E. Göknel
Keyword(s):  
Water Quality ◽  
Wastewater Treatment ◽  
Environmental Impact ◽  
Quality Parameters ◽  
Sea Of Marmara ◽  
General Evaluation ◽  
Marine Outfall ◽  
Secondary Stage ◽  
Pre Treatment

Metropolitan Istanbul Wastewater Treatment System contains 14 marine outfalls, seven of which include secondary stage biological treatment processes. The others have only mechanical treatment units including bar screens and grit chambers. Only one mechanical pre-treatment and marine disposal system, Yenikapi plant, has been operated since 1988 among these 14 plants and six of them are ready for construction. In this paper, the environmental impact of Yenikapi pretreatment and marine disposal system on the water quality of the Bosphorus and the Sea of Marmara has been investigated. Long term water quality measurements which were performed in pre-and post-dischange applications have been evaluated. Water quality parameters including pH, DO, BODs, TKN, P and total coliforms were measured at various sampling stations around the discharge points. A general evaluation of marine outfall systems to be constructed in the scope of Istanbul wastewater treatment project, on the water quality of the Sea of Marmara and the Bosphorus has been presented.

First flush effects in an urban catchment area in Aalborg

1998 ◽  
Vol 37 (1) ◽  
pp. 251-257 ◽  
Author(s):  
Torben Larsen ◽  
Kirsten Broch ◽  
Margit Riis Andersen
Keyword(s):  
Water Quality ◽  
Catchment Area ◽  
Quality Parameters ◽  
Average Concentration ◽  
First Flush ◽  
Storm Events ◽  
Urban Catchment ◽  
Discharge Measurements ◽  
Quality Measurements

The paper describes the results of measurements from a 2 year period on a 95 hectare urban catchment in Aalborg, Denmark. The results of the rain/discharge measurements include 160 storm events corresponding to an accumulated rain depth of totally 753 mm. The water quality measurements include 15 events with time series of concentration of SS, COD, BOD, total nitrogen and total phosphorus. The quality parameters showed significant first flush effects. The paper discusses whether either the event average concentration or the accumulated event mass is the most appropriate way to characterize the quality of the outflow.

scholarly journals Tap Water Quality: Seasonal User Surveys in Poland

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3841
Author(s):  
Józef Ober ◽  
Janusz Karwot
Keyword(s):  
Water Quality ◽  
World Literature ◽  
Mixed Methods Research ◽  
Tap Water ◽  
Quality Parameters ◽  
Subjective Perception ◽  
Quality Of Water ◽  
Profit Analysis ◽  
Tap Water Quality

Security of supply of water, which meets the quality parameters specified in applicable standards, is now the basis for the functioning of most societies. In addition to climatic, biological, chemical, and physical hazards, it is worth paying attention to consumers’ subjective perception of the quality of tap water supplied in the area of Poland. The article discusses various activities related to water resources management and analyses the results of an evaluation of selected quality parameters of tap water in Poland. A novelty on a European scale here is an examination of the evaluation of these parameters based on potential seasonal differences (spring, summer, autumn, winter). For the first time in the world literature, PROFIT analysis was used to evaluate selected parameters of tap water quality. The aim of the article was to present a model for the evaluation of the parameters of tap water supplied in different seasons of the year in Poland. Due to the complexity of the research aspects, a mixed-methods research procedure was used in which a literature review was combined with a survey and statistical analysis. For the purpose of the survey, an original survey questionnaire called “Survey of customer opinions on selected parameters of tap water supplied in Poland” was developed especially for this study. The conducted research confirmed the adopted hypothesis that the results of evaluation of selected tap water parameters vary depending on the period (spring, summer, autumn, winter) in Poland. The model developed by means of PROFIT analysis makes it possible to highlight to water suppliers the specific quality parameters in particular seasons of the year (spring, summer, autumn, winter), which may improve the quality of water supplied in Poland and thus, in the long-term perspective, increase the level of satisfaction of water recipients and confidence in drinking tap water in Poland.

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