Toward an optimal model based on inequality measures for treatment of historical & real time flood's dataset

Abstract

Flood is always a problem that Morocco tries to overcome it, because of the climate. The climate in Morocco can be divided into five sub-areas, determined by the different influences that the country suffers: oceanic, Mediterranean, montagnard, continental and saharan that's why Flood forecasting becomes a challenge for Morocco. Flood forecasting and control the water flow and water level on the surface is very critical to reduce the impacts while the flood disaster events. The flood forecasting model requires the management of huge spatial datasets, which implies data acquisition, storage and processing, as well as manipulation, reporting and display results. Thus, to reach an excellent prediction in terms of accuracy, it's important to implement a model which be interested by manipulation of the historical datasets from the database in order to minimize the response time of the decision. In this paper, we present a new model for treatment and for comparison by using the GINI Coefficient and the Variance Coefficient in this model which has two access modes to handle historical inundations informations according to the rainfall, the runoff and the water level. The main idea is to use the Inequality Measures to compare the observed distribution with the reference distribution, in other words compare the several data received from the sensors with data already stored in the database to have an appropriate decision about flooding without going through the decision support system for Real Time Flood Forecasting and Warning.