Hybrid Deep Learning Approach for Multi-Step-Ahead Daily Rainfall Prediction Using GCM Simulations

2020 ◽

Vol 9 (6) ◽

pp. 293-297

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Deep Learning ◽

Prediction Model ◽

Loss Function ◽

Historical Data ◽

Daily Rainfall ◽

Cross Entropy ◽

Learning Approach ◽

Rainfall Prediction

India is an agricultural country, and rainfall is the main source of irrigation for agriculture. Prediction of rainfall is very crucial for farmers to make decisions. In this research paper, the prediction model has been developed through deep learning using historical data of 10 years of rainfall. A deep learning approach used Keras API with an artificial neural network technique to predict the daily rainfall. The prediction model has been assessed by four-loss function, i.e., MSE, MAE, Hinge, and Binary Cross-Entropy.

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Rainfall Prediction: A Deep Learning Approach

Lecture Notes in Computer Science - Hybrid Artificial Intelligent Systems ◽

10.1007/978-3-319-32034-2_13 ◽

2016 ◽

pp. 151-162 ◽

Cited By ~ 24

Author(s):

Emilcy Hernández ◽

Victor Sanchez-Anguix ◽

Vicente Julian ◽

Javier Palanca ◽

Néstor Duque

Keyword(s):

Deep Learning ◽

Learning Approach ◽

Rainfall Prediction

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Rainfall Prediction in Flood Prone Area Using Deep Learning Approach

10.1007/978-981-16-7334-4_6 ◽

2021 ◽

pp. 71-88

Author(s):

Siti Zuhairah Ramlan ◽

Sayang Mohd Deni

Keyword(s):

Deep Learning ◽

Learning Approach ◽

Rainfall Prediction ◽

Prone Area ◽

Flood Prone Area

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Comparison of various Activation Functions A Deep Learning Approach

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v6i3.122126 ◽

2018 ◽

Vol 6 (3) ◽

pp. 122-126

Author(s):

Mohammed Ibrahim Khan ◽

◽

Akansha Singh ◽

Anand Handa ◽

◽

...

Keyword(s):

Deep Learning ◽

Learning Approach ◽

Activation Functions

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A Deep Learning based Arabic Script Recognition System: Benchmark on KHAT

The International Arab Journal of Information Technology ◽

10.34028/iajit/17/3/3 ◽

2020 ◽

Vol 17 (3) ◽

pp. 299-305 ◽

Cited By ~ 1

Author(s):

Riaz Ahmad ◽

Saeeda Naz ◽

Muhammad Afzal ◽

Sheikh Rashid ◽

Marcus Liwicki ◽

...

Keyword(s):

Deep Learning ◽

Character Recognition ◽

Data Augmentation ◽

Short Term Memory ◽

Recognition System ◽

Learning Approach ◽

Arabic Text ◽

Data Set ◽

Processing Step ◽

Handwritten Arabic

This paper presents a deep learning benchmark on a complex dataset known as KFUPM Handwritten Arabic TexT (KHATT). The KHATT data-set consists of complex patterns of handwritten Arabic text-lines. This paper contributes mainly in three aspects i.e., (1) pre-processing, (2) deep learning based approach, and (3) data-augmentation. The pre-processing step includes pruning of white extra spaces plus de-skewing the skewed text-lines. We deploy a deep learning approach based on Multi-Dimensional Long Short-Term Memory (MDLSTM) networks and Connectionist Temporal Classification (CTC). The MDLSTM has the advantage of scanning the Arabic text-lines in all directions (horizontal and vertical) to cover dots, diacritics, strokes and fine inflammation. The data-augmentation with a deep learning approach proves to achieve better and promising improvement in results by gaining 80.02% Character Recognition (CR) over 75.08% as baseline.

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