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Leachate generation rate modeling using artificial intelligence algorithms aided by input optimization method for an

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Environmental Science and Pollution Research International
|December 5, 2018
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Summary

Accurate leachate prediction is crucial for waste management. This study developed AI models, finding Artificial Neural Network models with two hidden layers most effective for predicting leachate generation rates using optimized inputs like waste quantity and rainfall.

Keywords:
Artificial neural network-multilayers perceptron (ANN-MLP)Input optimizationLandfill leachateRegression support vector machine (R-SVM)

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Area of Science:

  • Environmental Science
  • Water Resource Management
  • Artificial Intelligence in Environmental Modeling

Background:

  • Leachate poses a significant surface water pollution risk in Selangor State, Malaysia.
  • Accurate leachate generation rate prediction is vital for sustainable waste management and treatment, yet challenging in developing countries due to data scarcity and high costs.
  • Leachate generation is influenced by complex factors including meteorological data, waste generation, and landfill design, complicating traditional modeling.

Purpose of the Study:

  • To identify key factors influencing leachate production.
  • To develop and compare Artificial Intelligence (AI)-based models for predicting leachate generation rates.
  • To optimize input parameters for efficient and accurate leachate modeling.

Main Methods:

  • Development of Artificial Neural Network (ANN) models, specifically Multi-layer Perceptron (MLP) with single (ANN-MLP1) and double (ANN-MLP2) hidden layers.
  • Application of Support Vector Machine (SVM) regression time series algorithm for comparative analysis.
  • Input optimization process to identify critical factors, resulting in the selection of dumped waste quantity, rainfall level, and emanated gases.

Main Results:

  • The ANN-MLP2 model demonstrated the highest performance in predicting leachate generation rates.
  • ANN-MLP1 models showed better accuracy than SVM models, as indicated by relative error analysis.
  • The optimized input set (waste quantity, rainfall, emanated gases) proved effective for efficient modeling.

Conclusions:

  • AI-based models, particularly ANN-MLP with two hidden layers, offer efficient and accurate solutions for leachate generation prediction.
  • Input optimization significantly enhances model performance and reduces complexity in hydrological modeling.
  • The developed models provide a valuable tool for sustainable waste management and environmental protection in data-scarce regions.