Weather Forecasting Using Artificial Neural Networks

Rahul, G; Singh, Saumya; Dubey, Saumya

doi:10.1109/icrito48877.2020.9197993

Cited by 10 publications

(5 citation statements)

References 2 publications

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“…CNN models in two and three dimensions, as well as a two-dimensional framework via the attention layer, and a two-dimensional system with upscaling with distance-wise separable convolutions are all compared to the developed framework. In [33] showed that environmental conditions including minimum temperature, rainfall rate, air pressure, highest temperature, strain, and others play a significant role in agriculture. Getting accurate weather prediction technology in a country such as India will enable farmers to increase crop productivity.…”

Section: Literature Reviewmentioning

confidence: 99%

An evaluation of CNN and ANN in prediction weather forecasting: A review

Kareem

Hamad

Askar

2021

Sustainable Engineering and Innovation

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Artificial intelligence through deep neural networks is now widely used in a variety of applications that have profoundly altered human livelihoods in a variety of ways. People's daily lives have become much more convenient. Image recognition, smart recommendations, self-driving vehicles, voice translation, and a slew of other neural network innovations have had a lot of success in their respective fields. The authors present the ANN applied in weather forecasting. The prediction technique relies solely upon learning previous input values from intervals in order to forecast future values. And also, Convolutional Neural Networks (CNNs) are a form of deep learning technique that can help classify, recognize, and predict trends in climate change and environmental data. However, due to the inherent difficulties of such results, which are often independently identified, non-stationary, and unstable CNN algorithms should be built and tested with each dataset and system separately. On the other hand, to eradicate error and provides us with data that is virtually identical to the real value we need Artificial Neural Networks (ANN) algorithms or benefit from it. The presented CNN model's forecasting efficiency was compared to some state-of-the-art ANN algorithms. The analysis shows that weather prediction applications become more efficient when using ANN algorithms because it is really easy to put into practice.

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Section: Literature Reviewmentioning

confidence: 99%

An evaluation of CNN and ANN in prediction weather forecasting: A review

Kareem

Hamad

Askar

2021

Sustainable Engineering and Innovation

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“…Yapay Sinir Ağları (YSA) hava tahmini benzeri kaotik sorunları çözmek için uygun yöntemlerden biridir [3]. Bunun sebebi YSA insan beyni çalışmasından esinlenerek gerçekleştirilmiş ve doğrusal olmayan problemlerde de etkin sonuçlar vermektedir [1].…”

Section: Literatür Taraması (Related Work)unclassified

Radyosonde rasatları ile makine öğrenmesi tabanlı hava durumu kestirimi

Gogen,

Güney

2024

Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi

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Geçmişten günümüze hava tahmini insanlık için önem arz etmektedir. Hava tahmininin hassas gerçekleştirilebilmesi sel, tsunami vb. doğal afetlere karşı önlemler alınarak oluşacak olumsuz etkileri en düşük seviyeye indirmeyi sağlayabilmektedir. Bu çalışma kapsamında radyosonde verilerini kullanarak hava durumu kestirimi yapılmaktadır. Bu kestirimde en yüksek ve en düşük sıcaklık tahmini yapılmaktadır. Makine Öğrenmesi Algoritmaları kullanarak kestirim gerçekleştirilmiştir. Daha önce literatürde bulunan sıcaklık tahmini çalışmalardan farklı olarak 3 yıllık Radyosonde rasat verileri kullanılmıştır. Bu sayede yerden 40 km yüksekliğe kadar 1mbar aralıklarla ölçülmüş veriler ile atmosfer, literatürdeki diğer çalışmalara göre çok daha hassas olarak modellenmiştir. Bu modelde ertesi güne ait en yüksek ve en düşük sıcaklık değerleri kestirilmiştir. Bu aşamada normalizasyon ve öznitelik çıkarma veya seçmenin sonuçlara etkileri analiz edilerek tahmin için en uygun model belirlenmiştir. MATLAB ortamında gerçekleştirilen yazılım ile faklı regresyon yöntemleri karşılaştırılmıştır. Bu analizler sonucunda Gauss Süreci Regresyonu yöntemini kullanarak 1,2 Ortalama Karekök Sapması ile ertesi güne ait en yüksek sıcaklık tahmini en yüksek doğrulukla elde edilmiştir. Aynı yöntem kullanarak 2,4 Ortalama Karekök Sapması oranı ile en düşük sıcaklık tahmini yapılmıştır. Sonuçlar literatürdeki çalışmalardan daha başarılı sıcaklı tahmini yapıldığını göstermektedir.

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“…e application of ANN, a computational intelligence approach, was proposed by Rahul et al [78] as a significant step forward in the creation of an intuitive framework capable of comprehending and predicting nonlinear weather phenomena. e suggested research focused on creating a user-friendly framework that can accurately forecast the weather with the least amount of mistake and a more appropriate design [78].…”

Section: Weather Forecastingmentioning

confidence: 99%

A Review on Deep Sequential Models for Forecasting Time Series Data

Ahmed

Hassan

Mstafa

2022

Applied Computational Intelligence and Soft Computing

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Deep sequential (DS) models are extensively employed for forecasting time series data since the dawn of the deep learning era, and they provide forecasts for the values required in subsequent time steps. DS models, unlike other traditional statistical models for forecasting time series data, can learn hidden patterns in temporal sequences and have the memorizing data from prior time points. Given the widespread usage of deep sequential models in several domains, a comprehensive study describing their applications is necessary. This work presents a comprehensive review of contemporary deep learning time series models, their performance in diverse domains, and an investigation of the models that were employed in various applications. Three deep sequential models, namely, artificial neural network (ANN), long short-term memory (LSTM), and temporal-conventional neural network (TCNN) along with their applications for forecasting time series data, are elaborated. We showed a comprehensive comparison between such models in terms of application fields, model structure and activation functions, optimizers, and implementation, with a goal of learning more about the optimal model used. Furthermore, the challenges and perspectives of future development of deep sequential models are presented and discussed. We conclude that the LSTM model is widely employed, particularly in the form of a hybrid model, in which the most accurate predictions are made when the shape of hybrids is used as the model.

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Weather Forecasting Using Artificial Neural Networks

Cited by 10 publications

References 2 publications

An evaluation of CNN and ANN in prediction weather forecasting: A review

An evaluation of CNN and ANN in prediction weather forecasting: A review

Radyosonde rasatları ile makine öğrenmesi tabanlı hava durumu kestirimi

A Review on Deep Sequential Models for Forecasting Time Series Data

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