Neural Network Forecasting of Precipitation Volumes Using Patterns

Gorshenin, Andrey; Kuzmin, Victor

doi:10.1134/s1054661818030069

Cited by 5 publications

(3 citation statements)

References 19 publications

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“…Daily -For accumulated precipitation of the next day, Hernández et al (2016) trained a neural network comprising an autoencoder followed by dense layers for Manizales, Colombia. Without the autoencoders abstracting the data beforehand, Gorshenin and Kuzmin (2018) explored ANNs for two-step daily forecasts for two cities in Europe, Potsdam and Elista. In a study with a more complex network, Lohani (2019) forecasted daily precipitation for Punjab, India with LSTMs.…”

Section: D Rainfall Forecastingmentioning

confidence: 99%

A Systematic Review of Deep Learning Applications in Interpolation and Extrapolation of Precipitation Data

Sit¹,

Demiray²,

Demir³

2022

Preprint

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With technological enhancements, the volume, velocity, and variety (3Vs) of the raw digital Earth data have increased in recent years. Due to the increased availability of computer resources and the growing popularity of deep learning applications, this data has been a crucial source for data-driven studies that have transformed the fields of climate and earth science. One of the critical data sources is precipitation supporting climate and earth science studies on modeling, forecasting, and preparedness for extreme events (i.e., floods, droughts, pollution transport). In this study, we worked on an extensive review of manuscripts focusing on use of deep learning methods to tackle challenges either to improve the quality or extrapolate (forecast) rainfall datasets. The purpose of this study is to summarize the most recent developments in deep learning approaches for forecasting rainfall or improving rainfall datasets, as well as highlighting issues, shortcomings, and open questions with insightful recommendations for future directions.

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Section: D Rainfall Forecastingmentioning

confidence: 99%

A Systematic Review of Deep Learning Applications in Interpolation and Extrapolation of Precipitation Data

Sit¹,

Demiray²,

Demir³

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…En cualquier instante t, los resultados del modelo se ponderan en función de los resultados del instante anterior t-1. Los resultados pronosticados correctamente reciben una ponderación más baja y los que no se clasifican correctamente reciben una ponderación más alta, es de señalar que un aprendizaje débil es ligeramente mejor que adivinar al azar [29].…”

Section: B Métodosunclassified

“…El propósito de una red neuronal artificial es imitar cómo funciona el cerebro humano con la esperanza de que podamos construir una máquina que se comporte como un ser humano [29]. Una neurona artificial es el bloque de construcción central de una red neuronal artificial.…”

Section: B Métodosunclassified

Supervised Learning Techniques for the Optimization of Diagnosis Processes of Diabetes in Public Health Centers

Chavez¹,

Patiño²,

García³

et al. 2022

Proceedings of the 20th LACCEI International Multi-Conference for Engineering, Education and Technology: “Education, Research A

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One of the problems in public health care services in developing countries such as Ecuador, is the delay in the diagnosis of one of the main diseases that afflict the population such as diabetes, among public policies in the sector of health in this country is the optimization of diagnostic processes without using resources that affect the state budget in the short or long term. Through supervised learning techniques within the field of artificial intelligence, models can be created that allow the optimization of the diagnosis without the intervention of specialists for the interpretation of the results of patients who show signs of diabetes.

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Application of Machine Learning Algorithms to Handle Missing Values in Precipitation Data

Gorshenin

Lebedeva

Lukina

et al. 2019

Distributed Computer and Communication Networks

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Neural Network Forecasting of Precipitation Volumes Using Patterns

Cited by 5 publications

References 19 publications

A Systematic Review of Deep Learning Applications in Interpolation and Extrapolation of Precipitation Data

A Systematic Review of Deep Learning Applications in Interpolation and Extrapolation of Precipitation Data

Supervised Learning Techniques for the Optimization of Diagnosis Processes of Diabetes in Public Health Centers

Application of Machine Learning Algorithms to Handle Missing Values in Precipitation Data

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