Supervised SVM Classification of Rainfall Datasets

Raju, K. Hari Prasada; Sandhya, N.; Mehra, Raghav

doi:10.17485/ijst/2017/v10i15/106115

Cited by 2 publications

(2 citation statements)

References 23 publications

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“…Compared with other neural network models, the CNN model can better extract image feature information, reduce information loss, and swiftly reduce the dimension of images [13]- [16]. Numerous neural network models are generated based on CNN, such as LeNet, AlexNet, VGG and others.…”

Section: B Cnn-svm Model Constructionmentioning

confidence: 99%

Fine-grained Image Classification using Convolutional Neural Network and Support Vector Machine

Yu¹,

Lin²,

Wang³

et al. 2022

J-NaNA

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Although vast neural network models can classify images, sub-classification for each image class requires retraining the exisiting model with new fine-grained data, subject to high training cost and uncertain classification accuracy. To solve the aforementioned problems, a method using Convolution Neural Network and Support Vector Machine is proposed, where the former extracts general features from images for sub-classification while the latter categorizes these features. The method can be rapidly deployed in most Internet of Things systems to identify various targets; its effectiveness in reducing training cost and improving classification accuracy is verified through experiments.

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Section: B Cnn-svm Model Constructionmentioning

confidence: 99%

Fine-grained Image Classification using Convolutional Neural Network and Support Vector Machine

Yu¹,

Lin²,

Wang³

et al. 2022

J-NaNA

View full text Add to dashboard Cite

show abstract

“…Byakatonda et al [9] used ANN to model drought severity. Raju et al [10] compared the performance of RF, DTs SVM outperforming the k-NN, RF, and DT models. GNSS cloud data, along with other meteorological parameters, k-NN, and SVM for the classification of rainfall.…”

Section: Introductionmentioning

confidence: 99%

Binary classification of rainfall time-series using machine learning algorithms

Hudnurkar¹,

Rayavarapu²

2022

IJECE

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Summer monsoon rainfall contributes more than 75% of the annual rainfall in India. For the state of Maharashtra, India, this is more than 80% for almost all regions of the state. The high variability of rainfall during this period necessitates the classification of rainy and non-rainy days. While there are various approaches to rainfall classification, this paper proposes rainfall classification based on weather variables. This paper explores the use of support vector machine (SVM) and artificial neural network (ANN) algorithms for the binary classification of summer monsoon rainfall using common weather variables such as relative humidity, temperature, pressure. The daily data, for the summer monsoon months, for nineteen years, was collected for the Shivajinagar station of Pune in the state of Maharashtra, India. Classification accuracy of 82.1 and 82.8%, respectively, was achieved with SVM and ANN algorithms, for an imbalanced dataset. While performance parameters such as misclassification rate, F1 score indicate that better results were achieved with ANN, model parameter selection for SVM was less involved than ANN. Domain adaptation technique was used for rainfall classification at the other two stations of Maharashtra with the network trained for the Shivajinagar station. Satisfactory results for these two stations were obtained only after changing the training method for SVM and ANN.

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Supervised SVM Classification of Rainfall Datasets

Cited by 2 publications

References 23 publications

Fine-grained Image Classification using Convolutional Neural Network and Support Vector Machine

Fine-grained Image Classification using Convolutional Neural Network and Support Vector Machine

Binary classification of rainfall time-series using machine learning algorithms

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