flood-detection

To identify flood events from water level time-series data, you compare sensor readings against established flood stage thresholds. This process filters the data to pinpoint when water levels exceed critical limits.

Aggregating instantaneous water level sensor data into daily maximums requires time-series resampling. Using pandas, you group the high-frequency readings by day and extract the maximum value for reliable daily analysis.

Classifying flood severity across multiple stations involves comparing aggregated water levels against NWS flood stages. This categorizes events into action, minor, moderate, or major levels to enable prioritized response.

Yes, you can use pandas to filter water level data based on thresholding. The library provides the necessary time-series resampling and filtering logic to compare readings against defined flood stage thresholds.

Yes, this approach converts raw hydrology sensor readings into actionable flood reports. It transforms high-frequency instantaneous data into daily maximums and applies thresholding to generate summaries of flood events.