Floods pose significant threats to both urban and rural infrastructure, endangering lives and damaging properties. Effective streamflow prediction becomes increasingly necessary for enhancing flood management strategies, especially as climate change intensifies these natural disasters. Recent research has introduced the Long Short-Term Memory (LSTM) model, demonstrating its potential for accurately forecasting daily streamflow over the next 20 years, with results being utilized to create flood hazard maps through HEC-RAS software.
Historically, floods have wreaked havoc worldwide, leading to devastating consequences. The global rise in flooding is attributed to severe weather conditions intensified by human factors, jeopardizing communities and prompting the need for improved flood prediction tools. Given the arid and semi-arid climate of Iran, which has consistently faced flood risks, this research is of utmost importance.
The study focuses on streamflow forecasting within the Nesa River basin, located between longitudes 55°17 to 61°11’E and latitudes 27°52° to 34°7°N. It evaluates streamflow data spanning 40 years, collected from 1978 to 2019. The integration of the LSTM model with HEC-RAS software assumes importance as it offers a technical approach to flood modeling and real-time data analysis. Such advancements pave the way for enhanced flood risk assessments and improve preventative measures.
The methodology employed by the researchers involved using the LSTM model to process historical streamflow data, making predictions about daily inflow. By splitting the dataset distinctively for training and testing, the model achieved significant accuracy—demonstrated by evaluations such as Root Mean Square Error (RMSE) and Nash-Sutcliffe efficiency. The results indicate the reliability of the LSTM model, showing remarkable competence, particularly during arid conditions when reliable data is scarce.
Peak streamflow predictions extracted from the model become indispensable inputs for the HEC-RAS software, which generates flood hazard maps. The assessments revealed inflow volumes of 76.3 million cubic meters, indicating the potential hazards, which could escalate to catastrophic levels for return periods reaching up to 500 years.
Statistical analysis within the study revealed promising outcomes. The selected LSTM model (MD-8) returned values such as RMSE of 4.57 during training and 6.40 during testing. The efficiency of this model suggests it is far superior to traditional methods, making it viable for municipalities needing to prioritize flood safety measures. The operationalization of LSTM for daily forecast generation also allows local authorities to proactively prepare for potential flooding events.
The necessity for flood mapping serves multiple purposes beyond immediate risk assessment. These maps provide valuable insight, allowing for effective urban planning and infrastructure development. The flood hazard maps resulting from this study help identify regions most susceptible to flooding and guide the strategic placement of resources for emergency management. Analysis of past flood events significantly aids the creation of predictive models, supporting the establishment of formal plans aimed at managing flood risks effectively.
Flood hazard assessments using the Australian method identify varying levels of risk, with this research classifying the Nesa River as H6—indicating extreme vulnerability. Essential precautions are necessary, especially for populations residing near flood-prone areas. Therefore, continued research and model refinement are pivotal for adapting to ever-changing climatic scenarios, as precipitation patterns and storm intensities evolve.
Future work may elaborate on the integration of additional machine learning and deep learning methods to bolster predictions. The evolution of the LSTM framework could lay groundwork not only for more accurate streamflow forecasting but also for more resilient infrastructural development, aiding key decision-makers and urban planners.
The study reveals the potential of deep learning frameworks within hydrology, emphasizing the celebration of advancements made through the use of LSTM. Researchers now advocate for the blend of these technologies along with conventional methods, ensuring more accurate assessments of flood risks and management capabilities. Nevertheless, it is clear the LSTM model holds promise for forecasting daily streamflow, making it significant for effective water resource management strategies and flood preparedness.