This study used Artificial Intelligence (AI) techniques as a modeling tool to estimate the risk of Nile flooding in the cities of southern Sudan. Climatic records, and precipitation, from stations along the area were used between 2010 and 2019. To test how well the models worked, the forecast was done using a variety of stations. To determine the flood rate in southern Sudan with the highest degree of accuracy, various artificial neural network techniques were investigated. Six artificial neural network (ANN) models were created and compared to show flood prediction to reach the maximum level of accuracy and to improve the results (NN, GRNN, RNN, CFNN, PNN, FFNN). The artificial neural network (FFNN) produced the best results in the first test, reaching a 95 % accuracy rate. Three further strategies were evaluated by increasing the neural network's hidden layer count to ten. Tests with 15 and 25 hidden layers also showed that the accuracy changes with the increase of hidden layers. Also, six other algorithms were applied to reach the highest value expected from Using one of the artificial intelligence techniques (AI), in predicting the flood by machine learning methods (ML). The highest expected value of flooding was reached through the (Gradient Boosting) model, where it was Classification Accuracy (CA) 0.937, followed by (AdaBoost), (CA 0.916).

中文翻译：

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气候变化下使用人工智能模型对南苏丹的洪水进行分类和预测

这项研究使用人工智能（AI）技术作为建模工具来估计苏丹南部城市尼罗河洪水的风险。使用了 2010 年至 2019 年间该地区沿线气象站的气候记录和降水量。为了测试模型的效果，我们使用了多个气象站进行了预测。为了最准确地确定苏丹南部的洪水发生率，研究了各种人工神经网络技术。创建并比较了六个人工神经网络 (ANN) 模型，以显示洪水预测达到最大准确度并改进结果（NN、GRNN、RNN、CFNN、PNN、FFNN）。人工神经网络（FFNN）在第一次测试中产生了最好的结果，达到了 95% 的准确率。通过将神经网络的隐藏层数增加到十来评估另外三种策略。 15和25个隐藏层的测试也表明，精度随着隐藏层的增加而变化。此外，还应用了其他六种算法，以达到使用人工智能技术（AI）之一通过机器学习方法（ML）预测洪水的最高预期值。通过（梯度提升）模型达到了洪水的最高预期值，其分类精度（CA）为0.937，其次是（AdaBoost）（CA 0.916）。