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Related Concept Videos

Testing Water Quality01:14

Testing Water Quality

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 analysis based on LSTM and BP optimization with a transfer learning model.

Qun Luo1,2, Dingzhi Peng3,4, Wenjian Shang5

  • 1College of Water Sciences, Beijing Normal University, Beijing, 100875, China.

Environmental Science and Pollution Research International
|November 24, 2023
PubMed
Summary

This study introduces a novel deep learning model for urban water quality analysis, enhancing prediction accuracy by incorporating river upstream and downstream relationships. The method offers a promising approach for effective water management, especially in areas lacking extensive monitoring data.

Keywords:
BPBeijing’s sub-centerDeep learningLSTMWater quality

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Area of Science:

  • Environmental Science
  • Water Resource Management
  • Data Science

Background:

  • Rapid urbanization necessitates efficient urban water environmental management.
  • Existing water quality analysis methods require enhancement for speed and effectiveness.

Purpose of the Study:

  • To develop and optimize a deep learning model for accurate water quality analysis.
  • To improve existing Long Short-Term Memory (LSTM) and Back Propagation (BP) models using transfer learning.
  • To leverage upstream and downstream riverine relationships for enhanced model performance.

Main Methods:

  • Case study in Beijing's sub-center.
  • Development of Long Short-Term Memory (LSTM) and Back Propagation (BP) models.
  • Application of a novel transfer learning model integrating hydrological connectivity.

Main Results:

  • Significant improvements in Nash-Sutcliffe Efficiency (NSE) were observed across multiple river gauges.
  • LSTM model performance improved by 7-16%, and BP model by 9-17%.
  • The transfer learning approach demonstrated superior predictive capabilities due to the inclusion of spatial hydrological data.

Conclusions:

  • The proposed deep learning model effectively enhances water quality prediction accuracy.
  • Incorporating upstream and downstream river relationships is crucial for improving hydrological models.
  • This methodology offers a valuable framework for water quality modeling in ungauged basins.