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Related Experiment Video

Updated: Jun 16, 2026

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump
09:04

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

Published on: June 1, 2022

An intelligent hybrid deep learning-machine learning model for monthly groundwater level prediction.

Fatemeh Barzegari Banadkooki1, Elham Ghanbari-Adivi2, Fatemeh Sayyahi3

  • 1Department of Agriculture, Faculty of Engineering, Payame Noor University, Tehran, Iran.

Scientific Reports
|January 7, 2026
PubMed
Summary

A new hybrid artificial intelligence model, PSO-COO-GRU-ANFIS (PCGA), accurately predicts monthly groundwater levels (GWLs). This advanced model significantly improves upon existing methods for environmental protection and water resource management.

Keywords:
Artificial intelligence modelsDeep learning modelsGroundwater level predictionOptimizers

Related Experiment Videos

Last Updated: Jun 16, 2026

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump
09:04

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

Published on: June 1, 2022

Area of Science:

  • Environmental Science
  • Artificial Intelligence
  • Water Resource Management

Background:

  • Accurate groundwater level (GWL) prediction is crucial for effective environmental protection and sustainable water resource management.
  • Traditional methods often struggle with the complex, nonlinear dynamics inherent in hydrological systems.
  • The need for advanced predictive models is driven by increasing environmental pressures and water scarcity concerns.

Purpose of the Study:

  • To develop and evaluate a novel hybrid artificial intelligence model for precise monthly groundwater level forecasting.
  • To integrate optimization algorithms with deep learning and fuzzy inference systems for enhanced predictive accuracy.
  • To assess the performance of the proposed model against established benchmark methods using rigorous evaluation metrics.

Main Methods:

  • A hybrid model, termed PSO-COO-GRU-ANFIS (PCGA), was developed, combining Particle Swarm Optimization (PSO), Coati Optimization (COO), Gated Recurrent Unit (GRU), and Adaptive Neuro-Fuzzy Inference System (ANFIS).
  • The PSO-COO algorithm was employed for optimizing the parameters of both GRU and ANFIS components.
  • GRU was utilized for extracting temporal dependencies, while ANFIS handled the final prediction generation based on these extracted patterns.

Main Results:

  • The PCGA model demonstrated high accuracy, achieving a Mean Absolute Error (MAE) of 1.90 and a Nash-Sutcliffe Efficiency (NSE) of 0.90 for monthly GWL forecasting in the Ardabil Plain.
  • PCGA outperformed benchmark models, improving MAE by 14-77% and NSE by 1-20%.
  • The study confirmed the effectiveness of the PSO-COO optimization and GRU's capability in capturing long-term data dependencies, reducing error fluctuations during parameter tuning.

Conclusions:

  • The PCGA model is a robust and superior tool for forecasting monthly groundwater levels, outperforming conventional and standalone models.
  • The hybrid approach effectively captures complex, nonlinear relationships within GWL data.
  • This research offers a significant advancement in hydrological forecasting, supporting better environmental and water resource management strategies.