Rainfall Prediction: A Deep Learning Approach

被引：68

作者：

Hernandez, Emilcy ^{[1
]}

Sanchez-Anguix, Victor ^{[2
]}

Julian, Vicente ^{[3
]}

Palanca, Javier ^{[3
]}

Duque, Nestor ^{[4
]}

机构：

[1] Univ Nacl Colombia, Dept Ingn Org, Carrera 80 65-223, Medellin, Colombia

[2] Coventry Univ, Sch Comp Elect & Math, Gulson Rd, Coventry CV1 2JH, W Midlands, England

[3] Univ Politecn Valencia, Dept Sistemas Informat & Computac, Cami Vera S-N, Valencia 46022, Spain

[4] Univ Nacl Colombia, Dept Informat & Computac, Campus La Nubia,Bloque Q,Piso 2, Manizales, Colombia

来源：

HYBRID ARTIFICIAL INTELLIGENT SYSTEMS | 2016年 / 9648卷

关键词：

Artificial neural networks; Deep learning; Meteorological data; Rainfall prediction;

D O I：

10.1007/978-3-319-32034-2_13

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Previous work has shown that the prediction of meteorological conditions through methods based on artificial intelligence can get satisfactory results. Forecasts of meteorological time series can help decision-making processes carried out by organizations responsible of disaster prevention. We introduce an architecture based on Deep Learning for the prediction of the accumulated daily precipitation for the next day. More specifically, it includes an autoencoder for reducing and capturing non-linear relationships between attributes, and a multilayer perceptron for the prediction task. This architecture is compared with other previous proposals and it demonstrates an improvement on the ability to predict the accumulated daily precipitation for the next day.

引用

页码：151 / 162

页数：12

共 50 条

[1] Early Prediction of Extreme Rainfall Events: A Deep Learning Approach
Gope, Sulagna
Sarkar, Sudeshna
Mitra, Pabitra
Ghosh, Subimal
[J]. ADVANCES IN DATA MINING: APPLICATIONS AND THEORETICAL ASPECTS, 2016, 9728 : 154 - 167
[2] Rainfall Prediction in Flood Prone Area Using Deep Learning Approach
Ramlan, Siti Zuhairah
Deni, Sayang Mohd
[J]. SOFT COMPUTING IN DATA SCIENCE, SCDS 2021, 2021, 1489 : 71 - 88
[3] Deep Learning Models for the Prediction of Rainfall
Aswin, S.
Geetha, P.
Vinayakumar, R.
[J]. PROCEEDINGS OF THE 2018 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATION AND SIGNAL PROCESSING (ICCSP), 2018, : 657 - 661
[4] TRU-NET: a deep learning approach to high resolution prediction of rainfall
Rilwan A. Adewoyin
Peter Dueben
Peter Watson
Yulan He
Ritabrata Dutta
[J]. Machine Learning, 2021, 110 : 2035 - 2062
[5] TRU-NET: a deep learning approach to high resolution prediction of rainfall
Adewoyin, Rilwan A.
Dueben, Peter
Watson, Peter
He, Yulan
Dutta, Ritabrata
[J]. MACHINE LEARNING, 2021, 110 (08) : 2035 - 2062
[6] A SURVEY OF RAINFALL PREDICTION USING DEEP LEARNING
Hussain, Jamal
Zoremsanga, Chawngthu
[J]. 2021 3RD INTERNATIONAL CONFERENCE ON ELECTRICAL, CONTROL AND INSTRUMENTATION ENGINEERING (IEEE ICECIE'2021), 2021,
[7] A Discussion on Machine Learning Approach of Rainfall Prediction
Ray, Ranjana
Chakraborty, Swastika
[J]. 2022 URSI REGIONAL CONFERENCE ON RADIO SCIENCE, USRI-RCRS, 2022, : 335 - 338
[8] A pragmatic ensemble learning approach for rainfall prediction
Ghosh S.
Gourisaria M.K.
Sahoo B.
Das H.
[J]. Discover Internet of Things, 2023, 3 (01):
[9] Application of the deep learning for the prediction of rainfall in Southern Taiwan
Yen, Meng-Hua
Liu, Ding-Wei
Hsin, Yi-Chia
Lin, Chu-En
Chen, Chii-Chang
[J]. SCIENTIFIC REPORTS, 2019, 9 (1)
[10] Application of the deep learning for the prediction of rainfall in Southern Taiwan
Meng-Hua Yen
Ding-Wei Liu
Yi-Chia Hsin
Chu-En Lin
Chii-Chang Chen
[J]. Scientific Reports, 9

← 1 2 3 4 5 →