River Discharge Regression: A Machine Learning-Driven River Flow Prediction System

River Discharge Regression is a river flow prediction tool based on an optimized ensemble model, which combines the Arithmetic Optimization Algorithm (AOA) to achieve accurate next-day flow prediction, providing data support for water resource management and flood control decisions.

River discharge refers to the volume of water passing through a cross-section of a river per unit time, usually measured in cubic meters per second (m³/s). Accurate flow prediction is crucial for multiple fields:

Floods are among the most frequent and destructive natural disasters globally. Accurate advance prediction of river flow can gain valuable time for evacuation decisions, reservoir operation, and the activation of flood control projects. Statistics show that accurate flood warnings 24 hours in advance can reduce economic losses by more than 30%.

Precise flow prediction is needed for agricultural irrigation, urban water supply, and ecological flow protection. Over-extraction of water can lead to river drying and ecological degradation, while insufficient prediction may waste valuable water resources.

Hydropower stations need to optimize power generation plans based on inflow predictions, balancing power generation benefits and reservoir safety.

Maintaining the minimum flow of river ecosystems requires dynamic management based on long-term predictions.

River flow prediction faces multiple challenges:

1. High Non-linearity: The rainfall-runoff relationship is affected by complex factors such as terrain, soil, and vegetation
2. Spatio-temporal Heterogeneity: Hydrological response characteristics vary greatly across different basins
3. Data Scarcity: Many regions lack long-term, high-quality observation data
4. Extreme Events: Extreme flow events like floods have few samples, which are difficult for traditional models to capture
5. Multi-source Data Fusion: Need to integrate multi-dimensional information such as meteorology, remote sensing, and geology

Although physical hydrological models (e.g., SWAT, HEC-HMS) have a solid theoretical foundation, they require extensive parameter calibration and are computationally complex. Statistical methods (e.g., ARIMA) have limited performance in handling non-linear relationships.

This project adopts a combined strategy of ensemble learning + intelligent optimization to balance prediction accuracy and computational efficiency.