Water Quality Analysis at Mancherial, Jagdalpur and Konta Using Non-parametric Methods

Chintalacheruvu, Madhusudana Rao; Modi, Prakhar; Jhajharia, Deepak

doi:10.1007/978-981-16-4629-4_42

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…Similarly, modern researcher's utilisation of internet of things (IoT) and big data have made predictions of WQ relying on new technology applications such as Fuzzy logic, stochastic, Artificial Intelligence (AI)/Machine Learning (ML) systems, deep learning, or a combination of these methods depending on the problem statement had made it good to real-time situations analysis and prediction (Ahmed et al 2019;Bui et al 2020;Rao et al 2022). These methods had made it possible to identify prospective factors that have an impact on water environment systems and are useful tools for water resource management but required special training, and expert knowledge for usage and in a complex situation to understand.…”

Section: Introductionmentioning

confidence: 99%

“…Because of intra-annual variation, outliers, and non-detected data, WQ data are frequently not normally distributed, and nonparametric statistical analyses were often used (Bracmort et al 2006;Rao et al 2022). The use of the nonparametric Mann-Kendall test for detecting monotonic trends in WQ modelling is reported by Rao et al (2022), Kumar &Rathnam (2019), andWang et al (2019). The Mann-Kendall τ value has been used to measure the correlation between the observed parameters and the period.…”

Section: Introductionmentioning

confidence: 99%

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Time series trend modelling and forecasting of selected water quality parameters in the Mthatha River Catchment, South Africa

Amoo

Abayomi

Ikudayisi

et al. 2023

Water Practice &Amp; Technology

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Over the last decades, water quality at the Mthatha River Catchment (MRC) within the Eastern Cape Province of South Africa has been threatened by various pollutants. The continuous effluent concentration discharges from the Mthatha Prison and the Efata School for the Blind and Deaf have caused ineffable damage to the Mthatha River's water quality. Thus, the time series-measured data between 2012 and 2020 were analysed to determine the trends and enable forecasting of selected water quality parameters using the Thomas–Fiering (T–F) stochastic model. The Kendall's τ test trends show an increase in the coefficient of variation of 0.54 and 0.67 at the Mthatha Prison and Efata School, respectively, for abrupt changes, whereas the mean monthly T–F forecasted model shows a good correlation value range from 0.79 to 0.87 for the various predicted variables. The simulated predicted models and trends could serve as a measure to forecast selected water quality parameters' occurrence and a likelihood period where the river pollutants could be controlled. Water managers and researchers would find usefulness in the employed tools for an effective control planning of the river pollutants.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Time series trend modelling and forecasting of selected water quality parameters in the Mthatha River Catchment, South Africa

Amoo

Abayomi

Ikudayisi

et al. 2023

Water Practice &Amp; Technology

View full text Add to dashboard Cite

show abstract

“…Due to these characteristics, it is encouraged to use a nonparametric test to detect potential trends in water quality parameters (Helsel & Hirsch, 1992;Mozejko, 2012). Consequently, a multitude of researchers have embraced non-parametric approaches (Ballantine & Davies-Colley, 2014;Chen et al, 2019;Gomes & Karunatilaka, 2023;Helsel & Hirsch, 2002;Rao et al, 2022;Rentachintala et al, 2023;Yenilmez et al, 2011) to delineate trends in the concentrations of water quality parameters.…”

Section: Introductionmentioning

confidence: 99%

Investigating river water quality assessment through non‐parametric analysis: A case study of the Godavari River in India

Modi,

Chintalacheruvu

2023

Environmental Quality Mgmt

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This study examines temporal variations in water quality parameters of Godavari River. The present study examines the concentrations of seven water quality parameters, namely total alkalinity (Alk), calcium (Ca), chlorine (Cl), total hardness (Hard), magnesium (Mg), sodium (Na), and sulfate (SO4), using data from 1981 to 2005 at seven gauging stations, that is, Mancherial, Pathagudem, Nowrangpur, Jagdalpur, Perur, Konta, and Polavaram. A seasonal analysis (pre‐monsoon, monsoon, and post‐monsoon) is conducted to detect trends in water quality parameters. The study also uses normality tests Kolmogorov–Smirnov (K–S) and Shapiro–Wilk (S–W) which reveals nonnormal distribution in the majority of datasets, necessitating non‐parametric analysis. The concentration of Alk, Ca, and Hard is found to be significantly increasing trend at some stations during all three seasons, along with significantly decreasing trend found in the concentrations of Cl, Na, and SO4 using non‐parametric Mann–Kendall's (M–K) and Sen's Slope test. The concentration of Mg depicts mixed trends at different stations which may be due the variation in agricultural practices or industrial activities surrounding the stations. It is evident that none of the parameter exhibits statistically significant trends across all seasons at any station. The results of Sen's Slope test are found to be in good agreement with Mann–Kendall's test statistics. A second order auto‐regressive model (AR (2)) is successfully employed to predict and forecast the water quality parameters in present and future time steps. The authenticity of AR (2) model is assessed using Nash‐Sutcliffe (NSE) efficiency and coefficient of determination (R2). The values of NSE and R2 are found to be more than 0.85 for all parameters during all the three seasons, along with, the values of mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE). The results of coefficient of variation (CV) and persistence‐criterion (PC) also depicts that AR (2) can successfully be utilized in forecasting the water quality parameters. The research contributes insights for effective water resource management in Godavari River basin.

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Examining the spatial and temporal vulnerability to drought in Odisha, India

Modi,

Majhi,

Gogineni

2024

Water Practice &Amp; Technology

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The research focuses on investigating the spatial and temporal vulnerability to drought in Odisha, India, utilizing the Standard Precipitation Index (SPI), Drought Index (DI), and Rainfall Anomaly Index (RAI). The historical precipitation data are utilized to identify areas with prolonged rainfall deficits, quantifying the severity and duration of drought conditions. Preliminary analysis includes Normality and Homogeneity tests to ensure the validity of the precipitation datasets. The results depict the use of a non-parametric test for which Mann–Kendall trend test and Sen's Slope test are conducted. The districts exhibiting significant decreasing trends are identified as most critical for drought assessment. The findings from drought indices indicate that most districts have been affected by drought for approximately 50 years, with some districts experiencing severe or extreme conditions for over 10 years. Among the nine districts exhibiting a decreasing trend, four districts demonstrate a significant trend at a 5% significance level. These four districts are experiencing extremely and severely dry conditions, and while they display a significant decreasing trend, they are potentially vulnerable to the adverse impacts of drought conditions in the future. The study will serve in understanding and addressing the vulnerability to drought in Odisha, providing crucial insights for effective mitigation and management strategies.

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Water Quality Analysis at Mancherial, Jagdalpur and Konta Using Non-parametric Methods

Cited by 3 publications

References 15 publications

Time series trend modelling and forecasting of selected water quality parameters in the Mthatha River Catchment, South Africa

Time series trend modelling and forecasting of selected water quality parameters in the Mthatha River Catchment, South Africa

Investigating river water quality assessment through non‐parametric analysis: A case study of the Godavari River in India

Examining the spatial and temporal vulnerability to drought in Odisha, India

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