A novel 24- Hour deep neural network based Streamflow Forecasting method in Data-Scarce Regions

Accurate forecasting of streamflow is vital for effective water resource management and environmental conservation, particularly in regions with limited hydrological data. This paper presents a pioneering 24-hour streamflow forecasting method designed for areas with sparse data. By harnessing deep neural networks, this approach addresses challenges associated with scarce and unreliable data. The model's performance is evaluated using data from diverse hydrological domains, highlighting its adaptability and robustness. The results indicate that the deep neural network-based model surpasses traditional methods, offering more reliable streamflow predictions. This research contributes to disaster preparedness, efficient resource allocation, and environmental sustainability, while also paving the way for broader applications of machine learning in hydrology.