Estimation of Hourly Rainfall during Typhoons Using Radar Mosaic-Based Convolutional Neural Networks

Wei, Chih-Chiang; Hsieh, Po-Yu

doi:10.3390/rs12050896

Cited by 18 publications

(14 citation statements)

References 49 publications

(48 reference statements)

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“…The convolution and pooling processes of the FCN in GRI_FCNs and GRI-RRI_MCNNs were identical to those of the CNN. The net architecture of the CNN has been described by [ 27 , 44 ]. In general, CNNs are constructed by stacking two types of interweaved layers: convolutional and pooling (subsampling) layers [ 45 ].…”

Section: Model Developmentmentioning

confidence: 99%

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Modular Neural Networks with Fully Convolutional Networks for Typhoon-Induced Short-Term Rainfall Predictions

Wei

Huang

2021

Sensors

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Taiwan is located at the edge of the northwestern Pacific Ocean and within a typhoon zone. After typhoons are generated, strong winds and heavy rains come to Taiwan and cause major natural disasters. This study employed fully convolutional networks (FCNs) to establish a forecast model for predicting the hourly rainfall data during the arrival of a typhoon. An FCN is an advanced technology that can be used to perform the deep learning of image recognition through semantic segmentation. FCNs deepen the neural net layers and perform upsampling on the feature map of the final convolution layer. This process enables FCN models to restore the size of the output results to that of the raw input image. In this manner, the classification of each raw pixel becomes feasible. The study data were radar echo images and ground station rainfall information for typhoon periods during 2013–2019 in southern Taiwan. Two model cases were designed. The ground rainfall image-based FCN (GRI_FCN) involved the use of the ground rain images to directly forecast the ground rainfall. The GRI combined with rain retrieval image-based modular convolutional neural network (GRI-RRI_MCNN) involved the use of radar echo images to determine the ground rainfall before the prediction of future ground rainfall. Moreover, the RMMLP, a conventional multilayer perceptron neural network, was used to a benchmark model. Forecast horizons varying from 1 to 6 h were evaluated. The results revealed that the GRI-RRI_MCNN model enabled a complete understanding of the future rainfall variation in southern Taiwan during typhoons and effectively improved the accuracy of rainfall forecasting during typhoons.

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Section: Model Developmentmentioning

confidence: 99%

“…Wei and Hsieh [ 44 ] presented a radar mosaic-based multilayer perceptron (RMMLP) model, which is a conventional type of artificial neural networks that includes input, hidden, and output layers. The additional fully connected layer directly receives the cropped radar mosaic images to be flattened to a 1-D array.…”

Section: Simulation Of Typhoonsmentioning

confidence: 99%

Modular Neural Networks with Fully Convolutional Networks for Typhoon-Induced Short-Term Rainfall Predictions

Wei

Huang

2021

Sensors

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“…Weather radar is one of the most effective instruments for monitoring the occurrence of lightning. It can be used to indirectly identify the electrification process within a developing thunderstorm because grapples and hail particles return large reflectivity echoes (Wei and Hsieh, 2020). As highly reliable data in the field of meteorological detection, radar data have been widely considered by meteorologists, and many explorations and practices have been carried out.…”

Section: Instructionmentioning

confidence: 99%

Lightning Strike Location Identification Based on 3D Weather Radar Data

Zhang

et al. 2021

Front. Environ. Sci.

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Lightning is an instantaneous, intense, and convective weather phenomenon that can produce great destructive power and easily cause serious economic losses and casualties. It always occurs in convective storms with small spatial scales and short life cycles. Weather radar is one of the best operational instruments that can monitor the detailed 3D structures of convective storms at high spatial and temporal resolutions. Thus, extracting the features related to lightning automatically from 3D weather radar data to identify lightning strike locations would significantly benefit future lightning predictions. This article makes a bold attempt to apply three-dimensional radar data to identify lightning strike locations, thereby laying the foundation for the subsequent accurate and real-time prediction of lightning locations. First, that issue is transformed into a binary classification problem. Then, a suitable dataset for the recognition of lightning strike locations based on 3D radar data is constructed for system training and evaluation purposes. Furthermore, the machine learning methods of a convolutional neural network, logistic regression, a random forest, and k-nearest neighbors are employed to carry out experiments. The results show that the convolutional neural network has the best performance in identifying lightning strike locations. This technique is followed by the random forest and k-nearest neighbors, and the logistic regression produces the worst manifestation.

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“…As revealed in Figure 4b, in the first stage (2-D feature extraction, which is marked by red dotted lines), a network structure comprising convolutional and pooling layers are generally adopted for feature extraction. For convolutional layers, input images are implemented through convolution by using various kernels [54]. Convolution is implemented in two steps: sliding and calculating dot products; that is, using filters that slide onto input images and continuing to calculate matrices to obtain dot products.…”

Section: Mifnnsmentioning

confidence: 99%

Comparison of River Basin Water Level Forecasting Methods: Sequential Neural Networks and Multiple-Input Functional Neural Networks

Wei

2020

Remote Sensing

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To precisely forecast downstream water levels in catchment areas during typhoons, the deep learning artificial neural networks were employed to establish two water level forecasting models using sequential neural networks (SNNs) and multiple-input functional neural networks (MIFNNs). SNNs, which have a typical neural network structure, are network models constructed using sequential methods. To develop a network model capable of flexibly consolidating data, MIFNNs are employed for processing data from multiple sources or with multiple dimensions. Specifically, when images (e.g., radar reflectivity images) are used as input attributes, feature extraction is required to provide effective feature maps for model training. Therefore, convolutional layers and pooling layers were adopted to extract features. Long short-term memory (LSTM) layers adopted during model training enabled memory cell units to automatically determine the memory length, providing more useful information. The Hsintien River basin in northern Taiwan was selected as the research area and collected relevant data from 2011 to 2019. The input attributes comprised one-dimensional data (e.g., water levels at river stations, rain rates at rain gauges, and reservoir release) and two-dimensional data (i.e., radar reflectivity mosaics). Typhoons Saola, Soudelor, Dujuan, and Megi were selected, and the water levels 1 to 6 h after the typhoons struck were forecasted. The results indicated that compared with linear regressions (REG), SNN using dense layers (SNN-Dense), and SNN using LSTM layers (SNN-LSTM) models, superior forecasting results were achieved for the MIFNN model. Thus, the MIFNN model, as the optimal model for water level forecasting, was identified.

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Estimation of Hourly Rainfall during Typhoons Using Radar Mosaic-Based Convolutional Neural Networks

Cited by 18 publications

References 49 publications

Modular Neural Networks with Fully Convolutional Networks for Typhoon-Induced Short-Term Rainfall Predictions

Modular Neural Networks with Fully Convolutional Networks for Typhoon-Induced Short-Term Rainfall Predictions

Lightning Strike Location Identification Based on 3D Weather Radar Data

Comparison of River Basin Water Level Forecasting Methods: Sequential Neural Networks and Multiple-Input Functional Neural Networks

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