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Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
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Iterative classifier optimizer-based pace regression and random forest hybrid models for suspended sediment load

Sarita Gajbhiye Meshram1,2, Mir Jafar Sadegh Safari3, Khabat Khosravi4

  • 1Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam. saritagmeshram@tdtu.edu.vn.

Environmental Science and Pollution Research International
|October 30, 2020
PubMed
Summary

New artificial intelligence models accurately estimate river suspended sediment load. Hybrid models combining Iterative Classifier Optimizer with Random Forest and Pace Regression offer improved precision for dam management.

Keywords:
Hybrid techniqueIterative classifier optimizerPace regressionRandom forestRiverSuspended sediment load

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Area of Science:

  • Water Resources Engineering
  • Environmental Science
  • Computational Hydrology

Background:

  • Suspended sediment load significantly impacts river ecosystems and the lifespan of downstream dams.
  • Accurate estimation of suspended sediment load is crucial for effective water resource management.
  • Artificial intelligence (AI) offers advanced solutions for complex sediment transport modeling.

Purpose of the Study:

  • To propose and evaluate novel integrative intelligence models for computing suspended sediment load.
  • To enhance the accuracy of suspended sediment load estimation in the Seonath river basin.
  • To introduce hybrid models combining Iterative Classifier Optimizer (ICO) with Random Forest (RF) and Pace Regression (PR).

Main Methods:

  • Development of hybrid models: ICO-RF and ICO-PR.
  • Utilized 35 years of daily discharge and sediment data (1980-2015) from Simga station.
  • Model accuracy assessed using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R²).

Main Results:

  • The proposed hybrid models (ICO-RF and ICO-PR) demonstrated superior performance compared to standalone RF and PR models.
  • The ICO-RF model exhibited the highest accuracy among the evaluated models.
  • The developed models provide a precise methodology for suspended sediment load modeling in rivers.

Conclusions:

  • Novel hybrid AI models, particularly ICO-RF, offer a precise and effective approach for suspended sediment load estimation.
  • The findings support the applicability of these advanced computational methods in water resources engineering.
  • Accurate suspended sediment load modeling is vital for sustainable river basin management and infrastructure protection.