Nowadays, floods have become the widest global environmental and economic hazard in many countries, causing huge loss of lives and materials damages. It is, therefore, necessary to build an efficient flood forecasting system. The physical-based flood forecasting methods have indeed proven to be limited and ineffective. In most cases, they are only applicable under certain conditions. Indeed, some methods do not take into account all the parameters involved in the flood modeling, and these parameters can vary along a channel, which results in obtaining forecasted discharges very different from observed discharges. While using machine learning tools, especially artificial neural networks schemes appears to be an alternative. However, the performance of forecasting models, as well as a minimum error of prediction, is very interesting and challenging issues. In this paper, we used the multilayer perceptron in order to design a flood forecasting model and used discharge as input–output variables. The designed model has been tested upon intensive experiments and the results showed the effectiveness of our proposal with a good forecasting capacity.

如今，洪水已成为许多国家/地区中最广泛的全球环境和经济灾害，造成了巨大的生命损失和财产损失。因此，有必要建立一个有效的洪水预报系统。事实证明，基于物理的洪水预报方法是有限的且无效的。在大多数情况下，它们仅在某些条件下适用。实际上，某些方法并未考虑洪水建模中涉及的所有参数，并且这些参数会沿通道变化，从而导致获得的预测排放量与观测到的排放量截然不同。在使用机器学习工具时，尤其是人工神经网络方案似乎是一种替代方案。但是，预测模型的性能以及最小的预测误差，是非常有趣且具有挑战性的问题。在本文中，我们使用多层感知器来设计洪水预报模型，并使用流量作为输入-输出变量。设计的模型经过大量实验测试，结果表明我们的建议具有很好的预测能力。