Drought prediction data for IoT

It includes continuous and binary features that simulate realistic environmental conditions, along with a binary drought label. Features: date – Daily date (YYYY-MM-DD) soilMoisture – Soil moisture percentage (continuous, 5–80%) soilDigitalWet – Soil wetness indicator (binary: 1 = wet, 0 = dry) rainPercent – Daily rainfall percentage/probability (continuous, 0–100%) rainDetected – Rain presence (binary: 1 = yes, 0 = no) drought – Drought label (binary: 1 = drought, 0 = no drought) How it was created: Soil moisture and rainfall were simulated using seasonal sinusoidal patterns and random noise to reflect realistic environmental variability.

Binary features were derived from thresholds on soil moisture and rainfall. Drought labels were generated using rule-based conditions combining low soil moisture, absence of rain, and dry soil. Additional random noise was added to introduce realistic uncertainty, making the dataset suitable for training machine learning and deep learning models.