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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Artificial ecosystem optimization with Deep Learning Enabled Water Quality Prediction and Classification model.

Nazrul Islam1, Kashif Irshad2

  • 1Department of Mechanical Engineering, King Abdulaziz University, Jeddah, 21589, Saudi Arabia.

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|October 2, 2022
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Summary
This summary is machine-generated.

This study introduces an Artificial Ecosystem Optimization with Deep Learning Enabled Water Quality Prediction and Classification (AEODL-WQPC) model for rapid and economical water quality assessment. The model accurately predicts and classifies water quality, outperforming existing methods.

Keywords:
Deep learningMachine learningPredictionWater quality classificationWater quality index

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Area of Science:

  • Environmental Science
  • Artificial Intelligence
  • Data Science

Background:

  • Deteriorating water quality due to industrialization and urbanization poses significant health risks.
  • Traditional water quality testing methods are time-consuming, labor-intensive, and costly, hindering real-time monitoring.
  • There is a critical need for rapid and economical alternatives for water quality assessment.

Purpose of the Study:

  • To develop and present an Artificial Ecosystem Optimization with Deep Learning Enabled Water Quality Prediction and Classification (AEODL-WQPC) model.
  • To accurately predict and classify various levels of water quality.
  • To offer a rapid and economical solution for real-time water quality monitoring.

Main Methods:

  • The AEODL-WQPC model utilizes data normalization as a preliminary processing step.
  • An Optimal Stacked Bidirectional Gated Recurrent Unit (OSBiGRU) model, optimized with the Adam optimizer, forecasts the Water Quality Index (WQI).
  • An Artificial Ecosystem Optimization with enhanced Elman Neural Network (AEO-IENN) model is employed for water quality classification, with AEO algorithm tuning ENN parameters.

Main Results:

  • The AEODL-WQPC model demonstrated superior performance in predicting and classifying water quality.
  • Experimental validation using a benchmark Kaggle dataset confirmed the model's effectiveness.
  • The proposed model outperformed several state-of-the-art methods in accuracy and efficiency.

Conclusions:

  • The AEODL-WQPC model provides an effective and efficient approach for water quality prediction and classification.
  • This AI-driven model addresses the limitations of traditional methods, enabling timely interventions.
  • The study highlights the potential of deep learning and optimization algorithms in environmental monitoring.