A Deep Auto Encoder Semi Convolution Neural Network for Yearly Rainfall Prediction

Putra, Arief Bramanto Wicaksono; Malani, Rheo; Suprapty, Bedi; Gaffar, Achmad Fanany Onnilita

doi:10.1109/isitia49792.2020.9163775

Cited by 3 publications

(2 citation statements)

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“…To enhance the performance of traditional ANNs, Putra et al [9] presented a new DNN model as deep auto encoder semi convolutional neural network (DAESCNN). Tests were conducted using rainfall datasets collected annually between 2006 and 2016 from meteorological stations in the city of Samarinda, Indonesia.…”

Section: Literature Reviewmentioning

confidence: 99%

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Rainfall analysis and prediction using ensemble learning for Karnataka State

Meti,

Krishnegowda,

Swamy

2023

IJEECS

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<span lang="EN-US">Accurate <span lang="EN-US">prediction of rainfall can save farmers from crop damage as erratic rains in India have caused agricultural losses. Due to continuous climate change, rainfall and weather prediction has become essential. Rain causes damage to people and property. So, rainfall forecasting is crucial for crop as our country’s economy is still heavily dependent on agriculture. Using machine learning (ML) and deep learning (DL) models, we can train these models for the rainfall dataset and make predictions. In this paper, we estimate rainfall over the Karnataka region using a stacking ensemble model on a rainfall dataset collected between 1901 and 2015.</span></span>

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Section: Literature Reviewmentioning

confidence: 99%

“…32, No. 2, November 2023: 1187-1198 1188 classification or regression problems improves when more layers are used [9]. Since deep learning approaches such as LSTM and RNN can be used to learn non-linear relationships, it is particularly useful for predicting rainfall [10], [11].…”

Section: Introductionmentioning

confidence: 99%