Short-Term Weather Forecasting Using Spatial Feature Attention Based LSTM Model

Suleman, Masooma Ali Raza; Shridevi, S

doi:10.1109/access.2022.3196381

Cited by 29 publications

(13 citation statements)

References 47 publications

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“…From Table 7, it can be seen that the BiLSTM [34] tends to have better prediction accuracy than the LSTM [22], which also indicates that the bidirectional network structure can more fully consider the complete information of the sequence data in the forward and backward directions, thus improving the prediction accuracy of the model. At the same time, it can be seen that the improved network based on the attention mechanism [23] has better accuracy, which also indicates that the attention mechanism can assign different attention weights to different stages of temperature change during the training process of the model, so that the model can focus on the key sequence information as much as possible, thus achieving the purpose of enhancing the improvement of temperature prediction accuracy.…”

Section: Discussionmentioning

confidence: 93%

“…Qiu et al, used LSTM models to predict daily river temperatures and, through experimental analysis of data from the Three Gorges reservoir system, captured the daily average variation of the thermal system more accurately, demonstrating that the LSTM outperformed other methods in predicting the daily average water temperature of rivers [22]. MASOOMA et al used an LSTM model based on a spatial attention mechanism to accurately capture the space and time of multiple meteorological features to predict temperature, and discovered that spatial feature attention captured the interaction of input features on target features, and the study maintained a better prediction accuracy [23]. Song et al, proposed a temporal prediction model, based on LSTM and Kalman filtering, for predicting observations in atmospheric quality datasets, and found that the LSTM-Kalman model had better prediction results when compared with the LSTM model [24].…”

Section: Literature Reviewmentioning

confidence: 99%

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Atmospheric Temperature Prediction Based on a BiLSTM-Attention Model

et al. 2022

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To address the problem that traditional models are not effective in predicting atmospheric temperature, this paper proposes an atmospheric temperature prediction model based on symmetric BiLSTM (bidirectional long short-term memory)-Attention model. Firstly, the meteorological data from five major stations in Beijing were integrated, cleaned, and normalized to build an atmospheric temperature prediction dataset containing multiple feature dimensions; then, a BiLSTM memory network was used to construct with forward and backward information in the time dimension. And the limitations of the traditional LSTM method in long-term time series analysis were solved by introducing the attention mechanism to achieve the prediction analysis of atmospheric temperature. Finally, by comparing the prediction results with those of BiLSTM, LSTM-Attention, and LSTM, it is revealed that the proposed model has the best prediction effect, with a MAE value of 0.013, which is 0.72%, 0.41%, and 1.24% lower than those of BiLSTM, LSTM-Attention, and LSTM, respectively; the R2 value reaches 0.9618, which is 2.73%, 1.23%, and 4.98% higher than BiLSTM, LSTM-Attention, and LSTM, respectively. The results show that the symmetrical BiLSTM-Attention atmospheric temperature prediction model can effectively improve the prediction accuracy of temperature data, and the model can also be used to predict other time series data.

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Section: Discussionmentioning

confidence: 93%

Section: Literature Reviewmentioning

confidence: 99%

Atmospheric Temperature Prediction Based on a BiLSTM-Attention Model

et al. 2022

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“…One of the most popular methods is using Artificial Neural Networks (ANN) [6]- [8]. The recent success of Deep Learning (DL) methods in other topics, such as image classification [9]- [12], medical signal analysis [13]- [16], and weather forecasting [17], [18] has inspired researchers to apply Deep Learning methods to KWS problems. The examples of the most useful DL methods to perform KWS are Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN).…”

Section: A Abstractpotting Methodsmentioning

confidence: 99%

Efficient Real-Time Smart Keyword Spotting Using Spectrogram-Based Hybrid CNN-LSTM for Edge System

Syafalni,

Amadeus,

Sutisna

et al. 2024

IEEE Access

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Keyword Spotting (KWS) is the task of recognizing spoken command words from a database. With recent application human-machine interactions, KWS systems require real-time performance, where edge computing is a preferable option. To allow KWS systems to work on fast and real-time implementation, a low-complexity yet high-accurate AI model is mandatory. In this paper, we propose a comprehensive voice command recognition system design and its hardware implementation. The proposed AI model considered in this system is SpectroNet-based and an efficient hybrid CNN-LSTM architecture with low complexity. Jetson Xavier NX is an edge device because of its strong computational power as an embedded device. The implementation result shows the proposed method offers quite good in terms of accuracy, indicated by no accuracy drop between the model implemented in PC and Jetson Xavier. However, the inference time is quite high, which is 180 ms/step. To improve the speed of the system, the TensorRT library is used to further optimize the model. Optimization of the model is found effective, reducing 59.35% of the total operation performed in SpectroNet when FP32 precision is used, and 59.63% when FP16 precision is used. The model is also sped up by 45% if FP32 precision mode is used and 62% if FP16 precision mode is used. However, there is a slight accuracy drop of 2.68% if FP32 precision mode is used and 4.84% if FP16 precision mode is used. This slight drop in accuracy is considered negligible compared to the performance boost that TensorRT gives. The work is useful for intelligent control systems such as smart vehicles, smartphones, computers, and smart communications.

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“…RF 2019 (19) Mango: Weather parameters MSE: 0.007 LSTM 2020 (20) Rice: Weather parameters MSE: 0.135 LSTM 2022 (21) Weather Forecasting MSE: 0.08 Hybrid ARIMA-Bi-LSTM2022 (1) Rice:…”

Section: Contributionmentioning

confidence: 99%

i-PomDiagnoser: A Real-Time Pomegranate Disease Management System

Nirgude,

Rathi

2024

IJST

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Objectives: Designing and developing i-PomDiagnoser: a real-time pomegranate disease management system for disease detection, classification, prediction, recommending preventive measures, and analyzing abrupt climatic changes and their impact on pomegranates. Methods: A data collection framework has been designed and developed using an agriculture drone, sensors, camera, and other equipment to collect real field pomegranate images and micro-level parameters. Comprehensive Exploratory Data Analysis (EDA) and Feature Selection (FS) processes were carried out to improve the accuracy of disease classification and forecasting models. ML-based Binary, Multimodel, and Multilabel classifiers were implemented for disease classification. The models were trained on 11 years of historical data and tested on 5 months of actual field data. A hybrid pomegranate disease forecasting model has been developed for accurately forecasting micro-level parameters for the next 45 days to predict diseases. Findings: Micro-level (weather, soil, water) parameters specific to the agro-climatic zone were collected. The five most prominent distinct diseases are considered for experimentation namely Bacterial Blight (Telya), Anthracnose, Fruit spot, Fusarium Wilt, and Fruit borer. The proposed Improved Ensemble Multilabel Classifier (i-Ensemble-MLC) with a modified voting scheme has achieved a high classification accuracy of 95.82%, addressing model overfitting and data imbalance. Moreover, the hybrid pomegranate disease forecasting model, combining LSTM and i-Ensemble-MLC, demonstrated better performance with minimal error rates (MSE: 0.003, RMSE: 0.071, MAE: 0.048, R2: 0.7) compared to the existing model1 (MSE:0.037, MAE:0.028). Novelty: The novelty lies in the creation of the all-in-one model, i-PomDiagnoser. This innovative system helps the farmers to correctly detect and predict the most prominent diseases of pomegranate. Keywords: Pomegranate, Agriculture, Disease Forecasting, Machine Learning, Deep Learning

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Short-Term Weather Forecasting Using Spatial Feature Attention Based LSTM Model

Cited by 29 publications

References 47 publications

Atmospheric Temperature Prediction Based on a BiLSTM-Attention Model

Atmospheric Temperature Prediction Based on a BiLSTM-Attention Model

Efficient Real-Time Smart Keyword Spotting Using Spectrogram-Based Hybrid CNN-LSTM for Edge System

i-PomDiagnoser: A Real-Time Pomegranate Disease Management System

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