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Related Concept Videos

Testing Water Quality01:14

Testing Water Quality

303
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...
303
Quality of Water01:19

Quality of Water

441
In concrete preparation, the quality of water is paramount as it affects the strength and durability of the concrete. Potable water is usually preferred; however, it must not have excessive sodium or potassium to prevent compromising the concrete's integrity. Water quality is typically evaluated based on impurities such as dissolved solids, chlorides, and sulfates, and its pH value is ideally between 6 and 8. Even slightly acidic natural water may be acceptable unless it contains harmful...
441

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Related Experiment Video

Updated: Dec 26, 2025

Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds
12:50

Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds

Published on: September 26, 2017

11.7K

A water quality prediction method based on the multi-time scale bidirectional long short-term memory network.

Qinghong Zou1,2, Qingyu Xiong3,4, Qiude Li2

  • 1State Key Laboratory of Power Transmission Equipment and System Security and New Technology, Chongqing University, Chongqing, China.

Environmental Science and Pollution Research International
|March 8, 2020
PubMed
Summary

This study introduces a novel multi-time scale bidirectional LSTM network for accurate mangrove water quality prediction. The enhanced model improves forecasting by considering multiple time dependencies, aiding ecosystem health management.

Keywords:
Bidirectional long short-term memoryMangrove wetland ecosystemMulti-time scaleTime series data predictionWater quality prediction

Related Experiment Videos

Last Updated: Dec 26, 2025

Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds
12:50

Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds

Published on: September 26, 2017

11.7K

Area of Science:

  • Environmental Science
  • Ecosystem Health
  • Water Quality Management

Background:

  • Mangrove wetland ecosystems are crucial and sensitive to water quality changes.
  • Accurate water quality prediction is vital for managing pollution and ensuring ecosystem health.
  • Existing prediction methods require enhancement for comprehensive analysis.

Purpose of the Study:

  • To develop an advanced method for accurate mangrove water quality prediction.
  • To improve the comprehensiveness and accuracy of water quality forecasting.
  • To establish a robust framework for water pollution assessment and management.

Main Methods:

  • Proposed a multi-time scale bidirectional Long Short-Term Memory (LSTM) network for water quality prediction.
  • Implemented data preprocessing to enhance data integrity and volume.
  • Utilized Box-Behnken experimental design for hyper-parameter optimization.
  • Applied the model to Beilun Estuary for performance evaluation.

Main Results:

  • The proposed multi-time scale bidirectional LSTM model demonstrated superior performance compared to standalone LSTM or bidirectional LSTM.
  • The model effectively processes data bidirectionally, capturing dependencies across multiple time scales.
  • Data preprocessing and hyper-parameter tuning contributed to improved prediction accuracy.

Conclusions:

  • The developed multi-time scale bidirectional LSTM network offers a significant advancement in water quality prediction for mangrove ecosystems.
  • This method provides a more accurate and comprehensive tool for monitoring and managing water quality in estuarine environments.
  • The findings support better regulatory frameworks for assessing and managing water pollution and ecosystem health.