Development of sensor systems for flood water monitoring and alerting: Article - Sarsenov, Adilbek; Ilipbayeva, Lyazzat; Seidaliyeva, Ulzhalgas (Journal of Emerging Technologies and Computing, 2025)
This study addresses the systematic prediction of river water levels in Kazakhstan via hydrological computations, which are essential for forecasting water-related events and formulating plans for sustainable water resource management. Particular focus is placed on the significance of prompt and efficient monitoring of river dynamics to alleviate natural disasters such as floods and mudflows, especially in high-risk places like Almaty, situated in geologically unstable mountainous landscapes. The research focuses the potential of intelligent sensor-based monitoring systems that can gather real-time data on water levels, precipitation, soil moisture, and various environmental conditions. Systems integrated with artificial intelligence and data analysis can substantially augment decision-making processes, facilitate early warning mechanisms, and boost the precision of forecasts. This method ultimately protects natural ecosystems and local communities from the detrimental effects of hydrological hazards.