Introducing a LSTM based Flood Forecasting Model for the Nilwala river basin with a Mobile Application – a Review
Abstract
Flooding is one of the most
devasting natural disasters in the world. The
impact of flooding is damage to property,
Agriculture, Infrastructure of a country and
destroy human life. Flood Forecasting
models and proper awareness about floods,
sufficient communication between the flood
victims and the responsible authorities are
important to safeguard the life of human and
the infrastructure of a country. This paper
contains review of different Machine
Learning methods and Algorithms like
Artificial Neural Networks (ANN), Support
Vector Machine (SVM), Multilayer
Perception (MLP), Convolution Neural
Networks (CNN) and Long Short-Term
Memory (LSTM) which are used to forecast
floods. Long Short-Term Memory is one of
the Recurrent Neural Network models to
forecast Flood. According to the reviewed
literature Long Short-Term Memory
networks are better than ANN, MLP and SVM
because Long Short-Term Memory models
can learn long-term patterns better.
Collections
- Computer Science [66]