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Mixing data for multivariate statistical study of groundwater quality.

P G Dileep Kumar1, Narayanan C Viswanath2, Sobha Cyrus1

  • 1Division of Civil Engineering, School of Engineering, Cochin University of Science and Technology, Kochi, Kerala, 682022, India.

Environmental Monitoring and Assessment
|July 12, 2020
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Summary

This study applied multivariate statistical models to analyze water quality in Kozhikode City, India. The adaptive neuro-fuzzy inference system (ANFIS) model demonstrated superior performance in predicting total dissolved solids (TDS) compared to multiple linear regression (MLR).

Keywords:
Adaptive neuro-fuzzy inference systemGroundwater monitoringMixing dataMultiple linear regressionStatistical modelingStructural equation modeling

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Area of Science:

  • Environmental Science
  • Water Quality Assessment
  • Statistical Modeling

Background:

  • Water quality monitoring is crucial for public health and environmental management.
  • Kozhikode City, Kerala, India, faces challenges in maintaining water quality due to various sources.
  • Multivariate statistical methods offer powerful tools for analyzing complex water quality datasets.

Purpose of the Study:

  • To apply and compare multivariate statistical models, including Multiple Linear Regression (MLR), Structural Equation Modeling (SEM), and Adaptive Neuro-Fuzzy Inference System (ANFIS), for water quality assessment.
  • To develop a unified MLR model by combining water quality data from different locations and time periods.
  • To evaluate the predictive performance of ANFIS against MLR for total dissolved solids (TDS).

Main Methods:

  • Collected and combined water quality data from multiple sites and times in Kozhikode City.
  • Developed and tested several MLR models with TDS as the dependent variable and various water quality parameters as independent variables.
  • Constructed an SEM model using a combined dataset and compared its coefficients with the corresponding MLR model.
  • Developed an ANFIS model using the combined dataset, with TDS as the output and other parameters as inputs.

Main Results:

  • A unified MLR model was established by mixing datasets, showing comparable performance to unmixed models.
  • SEM analysis yielded identical regression coefficients to the corresponding MLR model, likely due to increased sample size.
  • The ANFIS model exhibited superior predictive accuracy for TDS on an external dataset compared to the MLR model.
  • Key water quality parameters identified as significant predictors for TDS included calcium, magnesium, nitrate, sodium, chloride, potassium, total hardness, and sulfate.

Conclusions:

  • Combining datasets can enhance the robustness of statistical models like MLR.
  • ANFIS is a more effective modeling approach than MLR for predicting total dissolved solids (TDS) in complex water quality scenarios.
  • The findings provide valuable insights for water resource management and pollution control strategies in Kozhikode City.