Data-Driven Flood Detection using Neural Networks
نویسندگان
چکیده
This paper describes the approaches used by our team (MultiBrasil) for the Multimedia Satellite Task at MediaEval 2017. For both disaster image retrieval and flood-detection in satellite images, we employ neural networks for end-to-end learning. Specifically, for the first subtask, we exploit Convolutional Networks and Relation Networks while, for the latter, dilated Convolutional Networks were employed.
منابع مشابه
Flood Forecasting Using Artificial Neural Networks: an Application of Multi-Model Data Fusion technique
Floods are among the natural disasters that cause human hardship and economic loss. Establishing a viable flood forecasting and warning system for communities at risk can mitigate these adverse effects. However, establishing an accurate flood forecasting system is still challenging due to the lack of knowledge about the effective variables in forecasting. The present study has indicated that th...
متن کاملIntegration of remote sensing and meteorological data to predict flooding time using deep learning algorithm
Accurate flood forecasting is a vital need to reduce its risks. Due to the complicated structure of flood and river flow, it is somehow difficult to solve this problem. Artificial neural networks, such as frequent neural networks, offer good performance in time series data. In recent years, the use of Long Short Term Memory networks hase attracted much attention due to the faults of frequent ne...
متن کاملRiver Water Level Prediction Using Physically Based and Data Driven Models
The ability to simulate the propagation of flood waves is of crucial importance for planning and operational management of river floods. Hydrodynamic and hydrologic numerical models provide such capabilities and represent conventional approaches to river flood modelling. In the recent years, data driven models such as artificial neural networks (ANNs), and neurofuzzy systems have also emerged a...
متن کاملUrban flood prediction in real-time from weather radar and rainfall data using artificial neural networks
This paper describes the application of Artificial Neural Networks (ANNs) as Data Driven Models (DDMs) to predict urban flooding in real-time based on weather radar and/or raingauge rainfall data. A 123manhole combined sewer sub-network from Keighley, West Yorkshire, UK is used to demonstrate the methodology. An ANN is configured for prediction of flooding at manholes based on rainfall input. I...
متن کاملComparison and evaluation of the performance of data-driven models for estimating suspended sediment downstream of Doroodzan Dam
Dams control most of the sediment entering the reservoir by creating static environments. However, sediment leaving the dam depends on various factors such as dam management method, inlet sediment, water height in the reservoir, the shape of the reservoir, and discharge flow. In this research, the amount of suspended sediment of Doroodzan Dam based on a statistical period of 25 years has been i...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2017