Proposed Intelligent Pre-Processing Model of Real-Time Flood Forecasting and Warning for Data Classification and Aggregation

Abstract

Abstract-A wireless sensor network is a network that can design a selforganizing structure and provides effective support for several protocols such as routing, locating, discovering services, etc. It is composed of several nodes called sensors grouped together into a network to communicate with each other and with the base stations. Nowadays, the use of Wireless sensor networks increased considerably. It can collect physical data and transform it into a digital values in real-time to monitor in a continuous manner different disaster like flood. However, due to various factors that can affect the wireless sensor networks namely, environmental, manufacturing errors hardware and software problems etc... It is necessary to carefully select and filter the data from the wireless sensors since we are providing a decision support system for flood forecasting and warning. In this paper, we presents an intelligent Pre-Processing model of real-time flood forecasting and warning for data classification and aggregation. The proposed model consists on several stages to monitor the wireless sensors and its proper functioning, to provide the most appropriate data received from the wireless sensor networks in order to guarantee the best accuracy in terms of real-time data and to generate a historical data to be used in the further flood forecasting.

Towards a Real Time Distributed Flood Early Warning System

“…Classification and Segregation [23] The pre-processing phase is used to monitor the function and performance of sensors installed in flood-prone areas. This model is based on multi-agent systems and is used to classify the data received into valid and invalid data.…”

“…The modelling was performed on three levels, structural modelling, behavioural modelling and interaction between objects modelling. [23].…”

“…1) Pre-Processing: In this stage, as already described and shown in [23], the system first verifies the functioning of the wireless sensors installed in the surveillance area to identify the functioning and non-functioning sensors. The system disables the wireless sensors that are broken to remove them from the forecasting process and to have them repaired later.…”

“…In this stage, we also obtain the invalid values and the sensors that sent the erroneous values. The full details and information about this stage is presented in [23].…”