Early Flood Warning System And Reservoir Optimization: Model For Namal Dam
1. This project deals with the problem of flash floods at Namal, which is always been a threat during the monsoon seasons, as well as with the problem of water management.
2. The Deep learning architecture called Long-Short-Term Memory (LSTM) Network is used, for predicting the water level of Namal lake. These predictions are then used for flood risk calculation.
3. Python programming language is used along with the TensorFlow and KERAS framework for building the DL model. The data from In-Situ sensors installed at inflows and Namal Dam is utilized for training the model.
4. Linear programming technique is adopted to make Optimization model for the optimal operation of Namal Dam. Hence, contributing in decision-making and water management.
5. The mobile application is developed, using Android studio, for communicating the alert services in addition, it serves the purpose of real-time monitoring of the situation at Namal Dam. Thus helping in taking the in-time safety actions
6. This project is in collaboration with Namal AI & Big Data-center and Center for Water Informatics & Technology LUMS.
Keywords: Deep learning, LSTM, Flood risk, Mobile app, Optimization, Namal Dam
Tools: Python, TensorFlow, KERAS, Linear Programming, Android studio
Department: Department of Electrical Engineering
Project Team Members
| Name |
Email |
|
Talha Rehman
|
talha2018@namal.edu.pk |
|
Arslan Mehmood
|
arslan2018@namal.edu.pk |
Project Poster
