Supervised Rainfall Learning Model Using Machine Learning Algorithms

被引：5

作者：

Sharma, Amit Kumar ^{[1
]}

Chaurasia, Sandeep ^{[1
]}

Srivastava, Devesh Kumar ^{[1
]}

机构：

[1] Manipal Univ, Jaipur, Rajasthan, India

来源：

INTERNATIONAL CONFERENCE ON ADVANCED MACHINE LEARNING TECHNOLOGIES AND APPLICATIONS (AMLTA2018) | 2018年 / 723卷

关键词：

Supervised learning; Normalization; Mean Thresholds; Rainfall dataset; Feature selection; Machine learning; Naive Bayes; Random forest; MLP; SMO;

D O I：

10.1007/978-3-319-74690-6_27

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Unpredictable and uncertain volume of the rainfall is the serious nature disaster. In current, available rainfall forecasting model predict rainfall volume hourly, weekly or monthly. This work proposed a supervised learning model which is based on machine leaning algorithms of data mining. This approach classify the low, mid and high volume of rainfall. Proposed approach is practically implemented on different uncertain heavy rainfall regions and compare the accuracy and measured the accuracy by ROC area of classifiers such as Random Forest, SMO, Naive Bayes and Multilayer Perceptron (MLP).

引用

页码：275 / 283

页数：9

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