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  3. Large Language Model Assisted Hyper-heuristic Evolutionary Algorithm For Groundwater Level Prediction.
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  3. Large Language Model Assisted Hyper-heuristic Evolutionary Algorithm For Groundwater Level Prediction.

Related Experiment Video

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

Large language model assisted hyper-heuristic evolutionary algorithm for groundwater level prediction.

Mengqian Li1, Qifang Luo2,3, Ziang Xiao1

  • 1College of Artificial Intelligence, Guangxi Minzu University, Nanning, 530006, China.

Scientific Reports
|May 11, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a Large Language Model-assisted hyper-heuristic evolutionary algorithm for improved groundwater level prediction. The novel framework enhances accuracy and generalizability over traditional methods.

Keywords:
Evolutionary algorithmGroundwater level predictionHyper-heuristic algorithmLarge Language ModelNeural network

Related Experiment Videos

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

Area of Science:

  • Environmental Science
  • Artificial Intelligence
  • Computational Science

Background:

  • Traditional models and ANNs struggle with groundwater level prediction accuracy.
  • Metaheuristic optimization methods are time-consuming and require expert input.

Purpose of the Study:

  • To propose a novel Large Language Model-assisted hyper-heuristic evolutionary algorithm framework (LLMHHEA) for enhanced groundwater level (GWL) prediction accuracy.
  • To integrate LLM generative intelligence with hyper-heuristic search via a co-evolutionary mechanism.

Main Methods:

  • Developed LLMHHEA with dynamic mutation strategies (metaheuristic, ANN, LLM-evolved), mutation-type-constrained crossover, and adaptive selection.
  • Employed a hybrid encoding scheme for co-evolutionary mechanism.
  • Validated on two GWL datasets and one temperature time series dataset.

Main Results:

  • LLMHHEA demonstrated stable convergence and superior predictive results compared to traditional metaheuristic-ANN combinations.
  • Identified optimal LLM-improved algorithm-ANN pairings for different datasets (e.g., LLM-KOA-ANFIS, LLM-GA-BP).
  • LLM-evolved mutations significantly improved generalization and prediction accuracy over original algorithms.

Conclusions:

  • LLMHHEA offers a new intelligent paradigm for complex optimization and prediction tasks.
  • The framework shows generalizability across diverse data types and temporal characteristics.
  • Provides robust technical support for sustainable water resource management.