A Spatiotemporal Ensemble Approach to Rainfall Forecasting

Souto, Yania Molina; Porto, Fábio; Moura, Ana Maria de Carvalho; Bezerra, Eduardo

doi:10.1109/ijcnn.2018.8489693

Cited by 10 publications

(3 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The model consistently outperformed FC-LSTM when evaluated on both the moving MNIST dataset and the radar echo dataset. Souto Y M et al [19] proposed a solution that combines recurrent networks with convolutional networks, using different channels to obtain the weights input to each prediction model. Their method improved accuracy by 50% for real weather datasets.…”

Section: Related Workmentioning

confidence: 99%

GraphAT Net: A Deep Learning Approach Combining TrajGRU and Graph Attention for Accurate Cumulonimbus Distribution Prediction

Zhang,

Liew,

et al. 2023

Atmosphere

View full text Add to dashboard Cite

In subtropical regions, heavy rains from cumulonimbus clouds can cause disasters such as flash floods and mudslides. The accurate prediction of cumulonimbus cloud distribution is crucial for mitigating such losses. Traditional machine learning approaches have been used on radar echo data generated by constant altitude plan position indicator (CAPPI) radar systems for predicting cumulonimbus cloud distribution. However, the results are often too foggy and fuzzy. This paper proposes a novel approach that integrates graph convolutional networks (GCN) and trajectory gated recurrent units (TrajGRU) with an attention mechanism to predict cumulonimbus cloud distribution from radar echo data. Experiments were conducted using the moving modified National Institute of Standards and Technology (moving MNIST) dataset and real-world radar echo data, and the proposed model showed a 59.12% improvement in mean square error (MSE) and a 16.26% improvement in structure similarity index measure (SSIM) on average in the moving MNIST dataset, a 65.40% improvement in MSE, and an 10.29% improvement in SSIM on average in the radar echo dataset. These results demonstrate the effectiveness of the proposed approach for improving the prediction accuracy of cumulonimbus cloud distribution.

show abstract

Section: Related Workmentioning

confidence: 99%

GraphAT Net: A Deep Learning Approach Combining TrajGRU and Graph Attention for Accurate Cumulonimbus Distribution Prediction

Zhang,

Liew,

et al. 2023

Atmosphere

View full text Add to dashboard Cite

show abstract

“…Depending on the type of architecture selected, the precision is accomplished by appropriate training algorithms and parameters. The variety of the ensemble members can be accomplished by distinct techniques and most commonly used by controlling the set of original random weights, varying topologies, varying the training algorithm and manipulating the training, such as mixing trained networks with different sample information [14]. Stacking generalization combines the predictions of multiple base learners using a meta-learner is a model that trained on the output of the base models.…”

Section: Ensemble Neural Network With Stacking Generalizationmentioning

confidence: 99%

Rainfall Intensity Forecast Using Ensemble Artificial Neural Network and Data Fusion for Tropical Climate

Safar

Ndzi

Mahdin

et al. 2019

Advances in Intelligent Systems and Computing

View full text Add to dashboard Cite

This paper proposes an ensemble method based on neural network architecture and stacking generalization. This research objective is to develop a novel ensemble of Artificial Neural Network models with back propagation network and dynamic Recurrent Neural Network of nonlinear autoregressive network with exogenous inputs to improve prediction accuracy. Historical meteorological parameters and rainfall intensity have been used for predicting the rainfall intensity forecast. Hourly predicted rainfall intensity forecast are compared and analyzed for all models. The outcome shows that for 1 hour of prediction, the neural network ensemble forecast model returns 94 percent precision. The study concludes that the ensemble neural network model shows significant improvement in prediction performance as compared to the individual models.

show abstract

“…Successfully predicting the behavior of spatio-temporal phenomena based on past observations is essential for a wide range of scientific studies and real-life applications like precipitation nowcasting [Souto et al 2018], and climate alert systems [Murat et al 2018]. In support of these applications, traditional data processing and time series analysis approaches generate predictive models that aim for predictive accuracy at the cost of high execution time and utilization of computational resources [Hassani and Silva 2015].…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven Model Selection Approach to Spatio-Temporal Prediction

Zorrilla

Ogasawara

Valduriez

et al. 2022

Anais Do XXXVII Simpósio Brasileiro De Banco De Dados (SBBD 2022)

View full text Add to dashboard Cite

Spatio-temporal Predictive Queries encompass a spatio-temporal constraint, defining a region, a target variable, and an evaluation metric. The output of such queries presents the future values for the target variable computed by predictive models at each point of the spatio-temporal region. Unfortunately, especially for large spatio-temporal domains with millions of points, training temporal models at each spatial domain point is prohibitive. In this work, we propose a data-driven approach for selecting pre-trained temporal models to be applied at each query point. The chosen approach applies a model to a point according to the training and input time series similarity. The approach avoids training a different model for each domain point, saving model training time. Moreover, it provides a technique to decide on the best-trained model to be applied to a point for prediction. In order to assess the applicability of the proposed strategy, we evaluate a case study for temperature forecasting using historical data and auto-regressive models. Computational experiments show that the proposed approach, compared to the baseline, achieves equivalent predictive performance using a composition of pre-trained models at a fraction of the total computational cost.

show abstract

A Spatiotemporal Ensemble Approach to Rainfall Forecasting

Cited by 10 publications

References 9 publications

GraphAT Net: A Deep Learning Approach Combining TrajGRU and Graph Attention for Accurate Cumulonimbus Distribution Prediction

GraphAT Net: A Deep Learning Approach Combining TrajGRU and Graph Attention for Accurate Cumulonimbus Distribution Prediction

Rainfall Intensity Forecast Using Ensemble Artificial Neural Network and Data Fusion for Tropical Climate

A Data-Driven Model Selection Approach to Spatio-Temporal Prediction

Contact Info

Product

Resources

About