ArticleScientific reports2024
Improving earthquake prediction accuracy in Los Angeles with machine learning.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Article
- An optimization-driven hierarchical deep learning approach using the Gray Langurs algorithm for data-driven seismic activity prediction.Scientific reports · 2026Article
- Intelligent earthquake prediction using animal vocal behavior analysis based on machine learning and deep learning approaches.Scientific reports · 2026Article
- Investigating the predictive power of seismic statistical features using ensemble learning.PloS one · 2026Article
- Advances in AI-based diagnosis of Alzheimer's disease using MRI: a comprehensive survey.Frontiers in medicine · 2026Review
- Review
- A method for instrumental seismic intensity assessment in Western China based on RF and MLP.Scientific reports · 2025Article
- Scalable earthquake magnitude prediction using spatio-temporal data and model versioning.Scientific reports · 2025Article
- SeismoQuakeGNN: a hybrid framework for spatio-temporal earthquake prediction with transformer-enhanced models.Frontiers in artificial intelligence · 2025Article
Corrections and comments
- Retraction · 2026-04-20Concerns/Issues about Methods · Concerns/Issues about Results and/or Conclusions · Investigation by Third Party · Objections by Author(s) · Original Data and/or Images not Provided and/or not Available · Unreliable Results and/or Conclusions · · See also: https://pubpeer.com/publications/3195869037CAEE1148F3A4D6DAA17C
- Retracted
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This research breaks new ground in earthquake prediction for Los Angeles, California, by leveraging advanced machine learning and neural network models. We meticulously constructed a comprehensive feature matrix to maximize predictive accuracy. By synthesizing existing research and integrating novel predictive features, we developed a robust subset capable of estimating the maximum potential earthquake magnitude. Our standout achievement is the creation of a feature set that, when applied with the Random Forest machine learning model, achieves a high accuracy in predicting the maximum earthquake category within the next 30 days. Among sixteen evaluated machine learning algorithms, Random Forest proved to be the most effective. Our findings underscore the transformative potential of machine learning and neural networks in enhancing earthquake prediction accuracy, offering significant advancements in seismic risk management and preparedness for Los Angeles.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.