Offshore FM Broadcasting Signal Path Loss Predictions for the South China Sea

A Support Vector Regression model achieving up to 55.50% higher accuracy than the ITU-R P.1546 standard for predicting path loss. This 2.2 MB Excel dataset contains predictions for Frequency Modulation broadcasting signal propagation in offshore environments. Researcher Qingzhi Hao published the data under a CC-BY-4.0 license in April 2026.

Use Cases

• Benchmarking new propagation models against the proposed SVR model based on the described accuracy improvement.
• Analyzing path loss characteristics for FM signals in offshore environments based on the study's focus.
• Training or validating machine learning models for signal prediction using the provided measurement data.
• Planning offshore broadcasting infrastructure based on more reliable path loss foundations mentioned in the description.

Strengths

• Model performance is quantitatively validated, showing a 55.50% improvement in prediction accuracy over an established standard.
• Data is shared under a permissive CC-BY-4.0 license, facilitating reuse and modification.
• The dataset has a specific, documented application focus on offshore FM broadcasting in the South China Sea.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment for large-scale model training.
• The dataset's 2.2 MB size suggests it is relatively small in scale.

Provenance

Source

figshare

Collection Method

Likely contains model predictions and verification data from a research study proposing a Support Vector Regression model.

Time Range

null

Freshness

Last updated 2026-04-23 04:19:01; freshness should be verified.

Geography

Offshore environment of the South China Sea.