Comparison of Adaline and Multiple Linear Regression Methods for Rainfall Forecasting

被引：3

作者：

Sutawinaya, I. P. ^{[1
]}

Astawa, I. N. G. A. ^{[1
]}

Hariyanti, N. K. D. ^{[2
]}

机构：

[1] Politekn Negeri Bali, Dept Elect Engn, Kabupaten Badung, Bali, Indonesia

[2] Politekn Negeri Bali, Dept Business Adm, Kabupaten Badung, Bali, Indonesia

来源：

2ND INTERNATIONAL JOINT CONFERENCE ON SCIENCE AND TECHNOLOGY (IJCST) 2017 | 2018年 / 953卷

关键词：

D O I：

10.1088/1742-6596/953/1/012046

中图分类号：

O59 [应用物理学];

学科分类号：

摘要：

Heavy rainfall can cause disaster, therefore need a forecast to predict rainfall intensity. Main factor that cause flooding is there is a high rainfall intensity and it makes the river become overcapacity. This will cause flooding around the area. Rainfall factor is a dynamic factor, so rainfall is very interesting to be studied. In order to support the rainfall forecasting, there are methods that can be used from Artificial Intelligence (AI) to statistic. In this research, we used Adaline for AI method and Regression for statistic method. The more accurate forecast result shows the method that used is good for forecasting the rainfall. Through those methods, we expected which is the best method for rainfall forecasting. here.

引用

页数：7

共 50 条

[31] Simple Multiple Regression Model for long range forecasting of Indian Summer Monsoon Rainfall
Y. Sadhuram
T. V. Ramana Murthy
[J]. Meteorology and Atmospheric Physics, 2008, 99 : 17 - 24
[32] Seasonal forecasting of Bangladesh summer monsoon rainfall using simple multiple regression model
MD MIZANUR RAHMAN
M RAFIUDDIN
MD MAHBUB ALAM
[J]. Journal of Earth System Science, 2013, 122 : 551 - 558
[33] Simple Multiple Regression Model for long range forecasting of Indian Summer Monsoon Rainfall
Sadhuram, Y.
Murthy, T. V. Ramana
[J]. METEOROLOGY AND ATMOSPHERIC PHYSICS, 2008, 99 (1-2) : 17 - 24
[34] A Support Vector Regression Model for Forecasting Rainfall
Hasan, Nasimul
Nath, Nayan Chandra
Rasel, Risul Islam
[J]. 2015 2ND INTERNATIONAL CONFERENCE ON ELECTRICAL INFORMATION AND COMMUNICATION TECHNOLOGY (EICT), 2015, : 554 - 559
[35] Methods and Applications of Clusterwise Linear Regression: A Survey and Comparison
Long, Qiang
Bagirov, Adil
Taheri, Sona
Sultanova, Nargiz
Wu, Xue
[J]. ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA, 2022, 17 (03)
[36] Comparison of the robust methods in the general linear regression model
Sazak, Hakan Savas
Mutlu, Nalan
[J]. COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, 2023, 52 (07) : 3163 - 3182
[37] A Comparison of Three Estimation Methods In Linear Regression Analysis
Luo, Xianghong
[J]. PROCEEDINGS OF THE 2016 4TH INTERNATIONAL CONFERENCE ON MACHINERY, MATERIALS AND INFORMATION TECHNOLOGY APPLICATIONS, 2016, 71 : 498 - 502
[38] Forecasting Uncertainty of Thailand's Electricity Consumption Compare with Using Artificial Neural Network and Multiple Linear Regression Methods
Jaisumroum, Nattapon
Teeravaraprug, Jirarat
[J]. PROCEEDINGS OF THE 2017 12TH IEEE CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS (ICIEA), 2017, : 308 - 313
[39] A comparison of multiple outlier detection methods for regression data
Billor, Nedret
Kiral, Gulsen
[J]. COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, 2008, 37 (03) : 521 - 545
[40] Application of penalized linear regression and ensemble methods for drought forecasting in Northeast China
Li, Zeng
Chen, Taotao
Wu, Qi
Xia, Guimin
Chi, Daocai
[J]. METEOROLOGY AND ATMOSPHERIC PHYSICS, 2020, 132 (01) : 113 - 130

← 1 2 3 4 5 →