Insect Population and Meteorological Data for Ecological Modeling in Turkey, 2020-2022

An agricultural dataset containing meteorological and vegetation variables used as a case study for comparing AI-based machine learning methods. The data is based on population observations of Cimbex quadrimaculata in Diyarbakır and Elazığ provinces in Türkiye between 2020 and 2022. The dataset was uploaded by Yunus Güral and is available under a CC-BY-4.0 license.

Use Cases

• Benchmarking binary classification models based on insect population data.
• Benchmarking multiclass classification models based on ecological variables.
• Benchmarking regression models based on meteorological and vegetation data.
• Conducting SHAP analysis to interpret model predictions based on temperature- and humidity-related variables.
• Comparing ensemble-based boosting algorithms (Gradient Boosting, XGBoost, LightGBM) on complex ecological data structures.

Strengths

• Dataset is associated with a published study comparing model performance, with reported metrics like 94.3% accuracy for Gradient Boosting.
• Data supports three distinct modeling approaches (binary classification, multiclass classification, regression) on the same underlying variables.
• SHAP analysis was performed, indicating a focus on model interpretability.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The dataset is small at 73.2 KB, indicating limited scope.

Provenance

Source

figshare, uploaded by Yunus Güral.

Collection Method

Based on population observations of Cimbex quadrimaculata.

Time Range

2020 to 2022

Freshness

Last updated 2026-04-03 17:48:50; freshness should be verified.

Geography

Diyarbakır (Eğil) and Elazığ (Keban) provinces, Türkiye.