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When the quality of water for concrete preparation is uncertain, its impact on the setting time of cement and compressive strength of mortar is assessed by comparison with de-ionized or distilled water benchmarks. American Society for Testing and Materials (ASTM) C1602 requires the setting times to be within 90 minutes of the control, British Standard (BS) 3146:1980 allows a 30-minute variance in the initial setting, while British Standards European Norm (BS EN) 1008 specifies initial setting...
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Water quality assessment with emphasis in parameter optimisation using pattern recognition methods and genetic

Gonzalo Sotomayor1, Henrietta Hampel2, Raúl F Vázquez3

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Summary

Water quality data mining in Ecuador

Keywords:
Genetic algorithmLand coverPattern recognitionWater quality

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Area of Science:

  • Environmental Science
  • Data Science
  • Water Resource Management

Background:

  • Complex water quality (WQ) data from the Paute river basin (Ecuador) spanning five years (2008, 2010-2013) required advanced analysis.
  • 21 physical, chemical, and microbiological parameters were collected from 80 sampling stations.

Purpose of the Study:

  • To apply pattern recognition algorithms for evaluating and interpreting complex WQ data.
  • To identify distinct WQ classes and the key parameters influencing them.
  • To validate the data mining approach using land use cover data.

Main Methods:

  • Unsupervised (k-means) and supervised (k-Nearest Neighbour with genetic algorithm optimisation, k-NN/GA) machine learning algorithms were employed.
  • k-means identified two WQ classes: low (C1) and high (C2) pollution.
  • k-NN/GA reduced 21 parameters to nine key indicators influencing WQ classes.

Main Results:

  • Two distinct water quality classes were identified: low and high pollution.
  • Nine critical parameters were identified: electric conductivity, faecal coliforms, dissolved oxygen, chlorides, total hardness, nitrate, total alkalinity, biochemical oxygen demand, and turbidity.
  • The spatial distribution of WQ classes showed strong agreement with land use cover, validating the data mining approach.

Conclusions:

  • Pattern recognition algorithms effectively classify water quality and identify key influencing parameters.
  • The identified key parameters provide valuable insights for targeted water resource management in the Paute river basin.
  • The study demonstrates the utility of data mining techniques for understanding complex environmental datasets.