Rainfall study based on ARIMA-RBF combined model

Zhao, Jialu; Chen, Ruyun; Xin, Haiyuan

doi:10.1088/1742-6596/2294/1/012029

Cited by 2 publications

(1 citation statement)

References 10 publications

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“…Inspired by the salp swarm behaviour, the SSA algorithm optimized the SVM parameters, like kernel width, penalty factor, etc., by minimizing the misclassification rate. J. Zhao, R. Chen and H. Xin (2022) [16] proposed a combined autoregressive intergraded moving average model with a radius bias function (ARIMA-RBF) model for rainfall prediction. They use monthly rainfall information from January 1951 to May 2015 in Nanchang City, China.…”

Section: Dependency On Seasonal Conditions Formentioning

confidence: 99%

A Hybrid Feature Selection Gradient Recurrent Neural Network (HFSGRNN) Model for Rainfall Prediction in India Regions

2024

IJIES

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In current studies, India is a farming country, and the accomplishment or disappointment of the crop mainly depends on the country's rainfall design. Generally, India's farming production is primarily based on the nature of the precipitation of the rainy season rainfall. The rainy season is the primary source of water in India. Regular rainfall forecasting is the primary source for crop development. Several analyses have defined the direct effect of rainwater on harvests. The main motive of this research work is proper and early rainfall prediction, which is helpful to people who live in northeast regions inclined to natural disasters like floods, etc. It helps agriculture with decision-making in their crop and water management (WM) using extensive dataset analysis that generates maximum terms of production for farmers and profits. This proposed work introduced an improved rainfall forecasting framework, a hybrid feature selection gradient-based RNN (HFSGRNN) model with an RNN algorithm. The research uses the HFSGRNN model steps, such as initial data preprocessing steps, which are used for forecasting rainfall, handling missing value outliers, and typecasting the rainfall dataset collected from the government site. After that, an HFSGRNN method is implemented to select the valuable using stochastic gradient descent (SGD) and optimal solutions calculated by particle swarm optimization (PSO) from the preprocessed data. The hybrid optimized feature sets are fed to the rainfall forecasting of the RNN classifier. Lastly, the valuable feature sets are forecasted using decision-making, and the simulation outcome shows that the research approach performed better in rainfall forecasting. The simulation results define that the HFSGRNN model delivered the minimum value of Root means square error (RMSE= 0.10) and maximum value of accuracy rate (acc = 98.1%) compared with existing methods, such as logistic regression (LR), Long Short Term -Convolutional Neural Network (LSTM-CNN), etc. The outcomes of the research analysis will help the farmers accept efficient modeling methods for forecasting long-term seasonal rainfall.

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Section: Dependency On Seasonal Conditions Formentioning

confidence: 99%

A Hybrid Feature Selection Gradient Recurrent Neural Network (HFSGRNN) Model for Rainfall Prediction in India Regions

2024

IJIES

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Predicción de la precipitación pluvial para prevención aplicando modelos ARIMA, Optimización Bayesiana y Modelo XGBoost

Tapia Arévalo,

Galvis Correa

2024

IDEAS

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Este estudio se enfoca en proporcionar un modelo estadístico para las precipitaciones en la Estación Meteorológica de la Universidad Politécnica Estatal del Carchi, cantón Tulcán, con el propósito de perfeccionar la capacidad de pronóstico y la resiliencia del sector agrícola regional. En consecuencia, este trabajo analiza las vulnerabilidades causadas por las variaciones meteorológicas en distintas regiones del Ecuador y resalta la importancia de la agricultura en este contexto. Para ello se propone un modelo estadístico que combina método ARIMA, optimización bayesiana y modelos XGBoost para predecir el comportamiento de la lluvia. Sobre la base de los resultados obtenidos en el estudio podemos prever el comportamiento adyacente de las precipitaciones en un margen temporal que colinda hasta el mes de diciembre del año 2024. Esto constituye un recurso imprescindible para la elección de decisiones más favorables y así beneficiar al área agrícola en la capacidad de adaptarse a eventos climáticos desfavorables.

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Rainfall study based on ARIMA-RBF combined model

Cited by 2 publications

References 10 publications

A Hybrid Feature Selection Gradient Recurrent Neural Network (HFSGRNN) Model for Rainfall Prediction in India Regions

A Hybrid Feature Selection Gradient Recurrent Neural Network (HFSGRNN) Model for Rainfall Prediction in India Regions

Predicción de la precipitación pluvial para prevención aplicando modelos ARIMA, Optimización Bayesiana y Modelo XGBoost

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