A machine learning approach to predict river hydrographs for water quality sampling.

Accurate water‑quality sampling in rivers needs precise timing, often tied to specific hydrologic events shown in hydrographs. This project offers a machine‑learning framework that predicts river hydrographs, helping to schedule and locate sampling campaigns. Using past discharge records, weather data such as rainfall, and watershed characteristics, we evaluate several predictive models—including Long Short‑Term Memory (LSTM) networks and Random Forest regressors—for their forecasting accuracy. The system captures the complex link between rain events and the resulting changes in river flow, predicting both peak flows and the recession limbs. With these forecasts, environmental engineers can plan sampling during runoff events when pollutant concentrations usually spike. The framework also provides step‑by‑step guidance on

data preprocessing, feature engineering, and model validation. Overall, this computational approach makes water‑quality monitoring more efficient by reducing field effort, using resources better, and ensuring representative samples are collected during key hydrologic phases.

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