Short-Term Rainfall Prediction Using Supervised Machine Learning

Prottasha, Nusrat Jahan; Tahabilder, Anik; Kowsher, Md.; Mia, Md Shanon; Kobra, Khadiza Tul

doi:10.46604/aiti.2023.8364

Cited by 2 publications

(1 citation statement)

References 15 publications

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“…The existing literature has proposed a repertoire of machine learning models on transmission line fault classification, encompassing the Artificial Neural Network (ANN) [12], Extreme Learning Model (ELM) [3], Radial Basis Function (RBF) neural network [13], Convolutional Neural Network (CNN) [7,8], Long Short-Term Memory (LSTM) [11], selfattention CNN (SAT-CNN) [10], Support Vector Machine (SVM) [14,16], and Decision Tree (DT) [9]. Furthermore, ensemble techniques have been extensively employed across various domains utilizing ML-based methodologies [17,18], and similarly, transmission line fault classification also utilizes these techniques [19]. In this study, our aim is to develop a model distinguished by its exceptional generalizability, thus significantly reducing the reliance on expert feature extraction.…”

Section: Introductionmentioning

confidence: 99%

Transmission Line Fault Classification Using Conformer Convolution-Augmented Transformer Model

Lee,

Huang,

Chang

et al. 2024

Applied Sciences

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Ensuring a consistently reliable power supply is paramount in power systems. Researchers are engaged in the pursuit of categorizing transmission line failures to design countermeasures for mitigating the associated financial losses. Our study employs a machine learning-based methodology, specifically the Conformer Convolution-Augmented Transformer model, to classify transmission line fault types. This model processes time series input data directly, eliminating the need for expert feature extraction. The training and validation datasets are generated through simulations conducted on a two-terminal transmission line, while testing is conducted on historical data consisting of 108 events that occurred in the Taiwan power system. Due to the limited availability of historical data, they are utilized solely for inference purposes. Our simulations are meticulously designed to encompass potential faults based on an analysis of historical data. A significant aspect of our investigation focuses on the impact of the sampling rate on input data, establishing that a rate of four samples per cycle is sufficient. This suggests that, for our specific classification tasks, relying on lower frequency data might be adequate, thereby challenging the conventional emphasis on high-frequency analysis. Eventually, our methodology achieves a validation accuracy of 100%, although the testing accuracy is lower at 88.88%. The discrepancy in testing accuracy can be attributed to the limited information and the small number of historical events, which pose challenges in bridging the gap between simulated data and real-world measurements. Furthermore, we benchmarked our method against the ELM model proposed in 2023, demonstrating significant improvements in testing accuracy.

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Section: Introductionmentioning

confidence: 99%

Transmission Line Fault Classification Using Conformer Convolution-Augmented Transformer Model

Lee,

Huang,

Chang

et al. 2024

Applied Sciences

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Forecasting High Resolution Precipitation Events With Logistic Echo State Networks

Moncada Morales,

Bonas,

Castruccio

et al. 2024

Journal of Geophysical Research: Machine Learning and Computati

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Accurately predicting hydroclimate events is crucial for understanding the impacts of climate change and effectively managing water resources, particularly for flood mitigation and timely warnings. Despite recent advances in machine learning, forecasting precipitation events continues to be challenging due to inherent data imbalances and the intricate dynamics governing these occurrences, rendering them difficult to model accurately. Echo State Networks (ESNs) offer a promising solution; their ability to model complex processes has been demonstrated throughout the field of environmental science. For this work, we propose a novel adaptation of ESNs, termed BinESN, to binary classification problems of precipitation occurrence. In particular, we extend the ESN to a generalized linear model framework, we leverage the ESN's ability to recognize complex dynamics while maintaining interpretability of the predicted output. Through simulation studies and an application to numerically simulated precipitation, we show that BinESN produces more accurate forecasts of sparse events in both short‐ and long‐range scenarios compared to other common machine learning approaches. Specifically, the proposed BinESN outperforms other reference methods by over 10% in terms of its area under the receiver operating characteristic curve.

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Short-Term Rainfall Prediction Using Supervised Machine Learning

Cited by 2 publications

References 15 publications

Transmission Line Fault Classification Using Conformer Convolution-Augmented Transformer Model

Transmission Line Fault Classification Using Conformer Convolution-Augmented Transformer Model

Forecasting High Resolution Precipitation Events With Logistic Echo State Networks

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