基于不平等测度的历史与实时洪水数据集处理的最优模型

El-Mabrouk Marouane, Ezziyyani Mostafa, Essaaidi Mohammad
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引用次数: 2

摘要

由于气候的原因,洪水一直是摩洛哥试图克服的一个问题。摩洛哥的气候可以根据该国遭受的不同影响分为五个子区域:海洋性、地中海性、山地性、大陆性和撒哈拉性,这就是为什么洪水预报对摩洛哥来说是一个挑战。洪水预报和控制地表水流和水位对于减少洪水灾害事件的影响至关重要。洪水预报模型需要管理庞大的空间数据集,包括数据的采集、存储和处理,以及结果的操作、报告和显示。因此,为了在准确性方面达到一个优秀的预测,重要的是实现一个对数据库中的历史数据集的操作感兴趣的模型,以最小化决策的响应时间。本文提出了一个新的处理和比较模型,该模型采用GINI系数和方差系数,该模型具有两种访问方式,可以根据降雨、径流和水位处理历史淹没信息。主要思想是使用不平等度量来比较观测分布与参考分布,换句话说,将从传感器接收到的几个数据与数据库中已经存储的数据进行比较,从而在不通过实时洪水预报和预警决策支持系统的情况下对洪水做出适当的决策。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Toward an optimal model based on inequality measures for treatment of historical & real time flood's dataset
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.
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