Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Modular learning models in forecasting natural phenomena.

D P Solomatine1, M B Siek

  • 1Hydroinformatics and Knowledge Management Department, UNESCO-IHE Institute for Water Education, P.O. Box 3015, 2601 DA Delft, The Netherlands. d.solomatine@unesco-ihe.org

Neural Networks : the Official Journal of the International Neural Network Society
|March 15, 2006
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Experiments with AdaBoost.RT, an improved boosting scheme for regression.

Neural computation·2006
Same author

Machine learning in soil classification.

Neural networks : the official journal of the International Neural Network Society·2006
Same author

Machine learning in sedimentation modelling.

Neural networks : the official journal of the International Neural Network Society·2006
Same author

Computational intelligence in earth sciences and environmental applications: issues and challenges.

Neural networks : the official journal of the International Neural Network Society·2006
Same journal

TraNce: Type-aware hypergraph neural network with biological mediators for drug repositioning.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Decentralized ADMM for factorization-based Low-rank matrix estimation.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Memristive neuromorphic circuit design inspired by the neural mechanisms of conditioned fear.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Q-learning based asynchronous Boolean control networks stabilization with data loss.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

New results on prescribed-time synchronization of complex networks via intermittent control.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Variance-constrained multi-view ensemble broad network for imbalanced data.

Neural networks : the official journal of the International Neural Network Society·2026
See all related articles

This study introduces modular models for forecasting natural processes, allowing domain expert input. These models demonstrate higher accuracy and transparency than global models.

Area of Science:

  • Machine Learning
  • Computational Science

Background:

  • Modular models, a type of committee machine, use specialized local models for distinct input space regions.
  • Traditional algorithms often automate region allocation, limiting domain expert knowledge integration.

Purpose of the Study:

  • To present novel approaches for building modular models with domain expert involvement.
  • To explore various training set splitting and model combination techniques.
  • To introduce new algorithms for model trees, enhancing piecewise linear modular regression.

Main Methods:

  • Developing modular models using hard splits and soft combinations (statistical, deterministic, fuzzy committees).
  • Integrating domain expert knowledge into model allocation and selection.
  • Presenting new algorithms for model trees (piecewise linear modular regression models).

Related Experiment Videos

Main Results:

  • Modular models achieved higher accuracy compared to traditional global learning models.
  • The resulting modular models offer improved transparency in forecasting natural processes.
  • Demonstrated effectiveness on benchmark tests and river flow forecasting problems.

Conclusions:

  • Modular modeling approaches enhance forecasting accuracy and model interpretability.
  • Incorporating domain expertise leads to more effective and transparent predictive models.
  • The presented methods, including model trees, offer a powerful alternative for complex natural process forecasting.