ARTÍCULO
TITULO

Early Flood Monitoring and Forecasting System Using a Hybrid Machine Learning-Based Approach

Eleni-Ioanna Koutsovili    
Ourania Tzoraki    
Nicolaos Theodossiou and George E. Tsekouras    

Resumen

The occurrence of flash floods in urban catchments within the Mediterranean climate zone has witnessed a substantial rise due to climate change, underscoring the urgent need for early-warning systems. This paper examines the implementation of an early flood monitoring and forecasting system (EMFS) to predict the critical overflow level of a small urban stream on Lesvos Island, Greece, which has a history of severe flash flood incidents requiring rapid response. The system is supported by a network of telemetric stations that measure meteorological and hydrometric parameters in real time, with a time step accuracy of 15 min. The collected data are fed into the physical Hydrologic Engineering Center?s Hydrologic Modeling System (HEC-HMS), which simulates the stream?s discharge. Considering the HEC-HMS?s estimated outflow and other hydro-meteorological parameters, the EMFS uses long short-term memory (LSTM) neural networks to enhance the accuracy of flood prediction. In particular, LSTMs are employed to analyze the real-time data from the telemetric stations and make multi-step predictions of the critical water level. Hydrological time series data are utilized to train and validate the LSTM models for short-term leading times of 15 min, 30 min, 45 min, and 1 h. By combining the predictions obtained by the HEC-HMS with those of the LSTMs, the EMFS can produce accurate flood forecasts. The results indicate that the proposed methodology yields trustworthy behavior in enhancing the overall resilience of the area against flash floods.

 Artículos similares

       
 
Yong Tu, Yanwei Zhao, Lingsheng Meng, Wei Tang, Wentao Xu, Jiyang Tian, Guomin Lyu and Nan Qiao    
Flash floods are ferocious and destructive, making their forecasting and early warning difficult and easily causing casualties. In order to improve the accuracy of early warning, a dynamic early warning index system was established based on the distribut... ver más
Revista: Water

 
Felipe Duque, Greg O?Donnell, Yanli Liu, Mingming Song and Enda O?Connell    
Polders are low-lying areas located in deltas, surrounded by embankments to prevent flooding (river or tidal floods). They rely on pumping systems to remove water from the inner rivers (artificial rivers inside the polder area) to the outer rivers, espec... ver más
Revista: Hydrology

 
Evangelos Rozos, Vasilis Bellos, John Kalogiros and Katerina Mazi    
This paper presents an efficient flood early warning system developed for the city of Mandra, Greece which experienced a devastating flood event in November 2017 resulting in significant loss of life. The location is of particular interest due to both it... ver más
Revista: Hydrology

 
Gaurav Parajuli, Shankar Neupane, Sandeep Kunwar, Ramesh Adhikari and Tri Dev Acharya    
Flood is one of the most frequently occurring and devastating disasters in Nepal. Several locations in Nepal are at high risk of flood, which requires proper guidance on early warning and safe evacuation of people to emergency locations through optimal r... ver más

 
Shufang Bai, Yun Zeng, Fang Dao, Boyi Xiao, Xiang Li and Jing Qian    
Studies show that sediment erosion is one of the main factors attributing to hydraulic turbine failure. The present paper represents an investigation into acoustic vibration signals generated by the water flow impacting the hydraulic turbine runner under... ver más
Revista: Water