论文标题
使用可解释的人工智能的洪水预测和分析特征的相关性
Flood Prediction and Analysis on the Relevance of Features using Explainable Artificial Intelligence
论文作者
论文摘要
本文通过分析每月降雨数据数据并应用机器学习算法，包括逻辑回归，k-nearest邻居，决策树，随机森林和支持向量机，介绍了印度喀拉拉邦喀拉拉邦的洪水预测模型。尽管这些模型在特定年份显示出洪水发生的高精度预测，但它们并未定量和定性地解释预测决策。本文展示了如何学习有助于预测决策的背景特征，并进一步扩展了可解释的可解释人工智能模块的内部工作。获得的结果证实了基于喀拉拉邦历史洪水每月降雨数据所发现的解释器模块所发现的发现的有效性。
This paper presents flood prediction models for the state of Kerala in India by analyzing the monthly rainfall data and applying machine learning algorithms including Logistic Regression, K-Nearest Neighbors, Decision Trees, Random Forests, and Support Vector Machine. Although these models have shown high accuracy prediction of the occurrence of flood in a particular year, they do not quantitatively and qualitatively explain the prediction decision. This paper shows how the background features are learned that contributed to the prediction decision and further extended to explain the inner workings with the development of explainable artificial intelligence modules. The obtained results have confirmed the validity of the findings uncovered by the explainer modules basing on the historical flood monthly rainfall data in Kerala.
