Prediction of synoptic-scale sea level pressure over the Indian monsoon region using deep learning

<div><div><div><p>The synoptic-scale (3 - 7 days) variability is a dominant contributor to the Indian summer monsoon (ISM) seasonal precipitation. An accurate prediction of ISM precipitation by dynamical or statistical models remains a challenge. Here we show that the sea level pressure (SLP) can be used as a proxy to predict the active-break cycle as well as the genesis of low- pressure-systems (LPS), using a deep learning model, namely, convolutional long short-term memory (ConvLSTM) networks. The deep learning model is able to reliably predict the daily SLP anomalies over Central India and the Bay of Bengal at a lead time of 7 days. As the fluctuations in SLP drive the changes in the strength of the atmospheric circulation, the prediction of SLP anomalies is useful in predicting the intensity of ISM. It is demonstrated that the ConvLSTM possesses better prediction skill compared to a conventional numerical weather prediction model, indicating the usefulness of a physics guided deep learning model in medium range weather forecasting.</p></div></div></div>

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Prediction of Synoptic-Scale Sea Level Pressure Over the Indian Monsoon Region Using Deep Learning

IEEE Geoscience and Remote Sensing Letters ◽

10.1109/lgrs.2021.3100899 ◽

2021 ◽

pp. 1-5

Author(s):

Aryaman Sinha ◽

Mayuna Gupta ◽

K. S. S. Sai Srujan ◽

Hariprasad Kodamana ◽

S. Sandeep

Keyword(s):

Deep Learning ◽

Sea Level ◽

Indian Monsoon ◽

Monsoon Region ◽

Synoptic Scale ◽

Sea Level Pressure ◽

Level Pressure ◽

Indian Monsoon Region

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Improving Sentiment Analysis using Hybrid Deep Learning Model

Recent Advances in Computer Science and Communications ◽

10.2174/2213275912666190328200012 ◽

2020 ◽

Vol 13 (4) ◽

pp. 627-640 ◽

Cited By ~ 1

Author(s):

Avinash Chandra Pandey ◽

Dharmveer Singh Rajpoot

Keyword(s):

Neural Network ◽

Deep Learning ◽

Sentiment Analysis ◽

Classification Accuracy ◽

Short Term Memory ◽

Computational Cost ◽

Extraction Process ◽

Learning Model ◽

Sentiment Classification ◽

Deep Learning Model

Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods. Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost. Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced. Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated. Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.

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Application of Rough and Fuzzy Set Theory for Prediction of Stochastic Wind Speed Data Using Long Short-Term Memory

Atmosphere ◽

10.3390/atmos12070924 ◽

2021 ◽

Vol 12 (7) ◽

pp. 924

Author(s):

Moslem Imani ◽

Hoda Fakour ◽

Wen-Hau Lan ◽

Huan-Chin Kao ◽

Chi Ming Lee ◽

...

Keyword(s):

Deep Learning ◽

Wind Speed ◽

Set Theory ◽

Rough Set ◽

Fuzzy Set Theory ◽

Short Term Memory ◽

Learning Model ◽

Short Term ◽

Long Short Term Memory ◽

Deep Learning Model

Despite the great significance of precisely forecasting the wind speed for development of the new and clean energy technology and stable grid operators, the stochasticity of wind speed makes the prediction a complex and challenging task. For improving the security and economic performance of power grids, accurate short-term wind power forecasting is crucial. In this paper, a deep learning model (Long Short-term Memory (LSTM)) has been proposed for wind speed prediction. Knowing that wind speed time series is nonlinear stochastic, the mutual information (MI) approach was used to find the best subset from the data by maximizing the joint MI between subset and target output. To enhance the accuracy and reduce input characteristics and data uncertainties, rough set and interval type-2 fuzzy set theory are combined in the proposed deep learning model. Wind speed data from an international airport station in the southern coast of Iran Bandar-Abbas City was used as the original input dataset for the optimized deep learning model. Based on the statistical results, the rough set LSTM (RST-LSTM) model showed better prediction accuracy than fuzzy and original LSTM, as well as traditional neural networks, with the lowest error for training and testing datasets in different time horizons. The suggested model can support the optimization of the control approach and the smooth procedure of power system. The results confirm the superior capabilities of deep learning techniques for wind speed forecasting, which could also inspire new applications in meteorology assessment.

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Tropical cyclone simulations over Bangladesh at convection permitting 4.4 km & 1.5 km resolution

Scientific Data ◽

10.1038/s41597-021-00847-5 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Hamish Steptoe ◽

Nicholas Henry Savage ◽

Saeed Sadri ◽

Kate Salmon ◽

Zubair Maalick ◽

...

Keyword(s):

Wind Speed ◽

Tropical Cyclone ◽

Sea Level ◽

Tropical Cyclones ◽

Weather Forecasting ◽

Sea Level Pressure ◽

Mean Sea Level ◽

Level Pressure ◽

Medium Range ◽

Gust Speed

AbstractHigh resolution simulations at 4.4 km and 1.5 km resolution have been performed for 12 historical tropical cyclones impacting Bangladesh. We use the European Centre for Medium-Range Weather Forecasting 5th generation Re-Analysis (ERA5) to provide a 9-member ensemble of initial and boundary conditions for the regional configuration of the Met Office Unified Model. The simulations are compared to the original ERA5 data and the International Best Track Archive for Climate Stewardship (IBTrACS) tropical cyclone database for wind speed, gust speed and mean sea-level pressure. The 4.4 km simulations show a typical increase in peak gust speed of 41 to 118 knots relative to ERA5, and a deepening of minimum mean sea-level pressure of up to −27 hPa, relative to ERA5 and IBTrACS data. The downscaled simulations compare more favourably with IBTrACS data than the ERA5 data suggesting tropical cyclone hazards in the ERA5 deterministic output may be underestimated. The dataset is freely available from 10.5281/zenodo.3600201.

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AOHDL: Archimedes Optimization Based Hybrid Deep Learning Model for Soybean Plant Disease Classification

10.21203/rs.3.rs-302084/v1 ◽

2021 ◽

Author(s):

J. Annrose ◽

N. Herald Anantha Rufus ◽

C. R. Edwin Selva Rex ◽

D. Godwin Immanuel

Keyword(s):

Deep Learning ◽

Optimization Algorithm ◽

Short Term Memory ◽

Learning Model ◽

Disease Classification ◽

Training Data ◽

Machine Learning Techniques ◽

Soybean Plant ◽

Lower Accuracy ◽

Deep Learning Model

Abstract Bean which is botanically called Phaseolus vulgaris L belongs to the Fabaceae family.During bean disease identification, unnecessary economical losses occur due to the delay of the treatment period, incorrect treatment, and lack of knowledge. The existing deep learning and machine learning techniques met few issues such as high computational complexity, higher cost associated with the training data, more execution time, noise, feature dimensionality, lower accuracy, low speed, etc. To tackle these problems, we have proposed a hybrid deep learning model with an Archimedes optimization algorithm (HDL-AOA) for bean disease classification. In this work, there are five bean classes of which one is a healthy class whereas the remaining four classes indicate different diseases such as Bean halo blight, Pythium diseases, Rhizoctonia root rot, and Anthracnose abnormalities acquired from the Soybean (Large) Data Set.The hybrid deep learning technique is the combination of wavelet packet decomposition (WPD) and long short term memory (LSTM). Initially, the WPD decomposes the input images into four sub-series. For these sub-series, four LSTM networks were developed. During bean disease classification, an Archimedes optimization algorithm (AOA) enhances the classification accuracy for multiple single LSTM networks. MATLAB software implements the HDL-AOA model for bean disease classification. The proposed model accomplishes lower MAPE than other exiting methods. Finally, the proposed HDL-AOA model outperforms excellent classification results using different evaluation measures such as accuracy, specificity, sensitivity, precision, recall, and F-score.

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Soybean Plant Disease Classification using Archimedes Optimization Algorithm based Hybrid Deep Learning Model

10.21203/rs.3.rs-281525/v1 ◽

2021 ◽

Author(s):

J. Annrose ◽

N. Herald Anantha Rufus ◽

C. R. Edwin Selva Rex ◽

D. Godwin Immanuel

Keyword(s):

Deep Learning ◽

Optimization Algorithm ◽

Short Term Memory ◽

Learning Model ◽

Disease Classification ◽

Training Data ◽

Machine Learning Techniques ◽

Soybean Plant ◽

Lower Accuracy ◽

Deep Learning Model

Abstract Bean which is botanically called Phaseolus vulgaris L belongs to the Fabaceae family.During bean disease identification, unnecessary economical losses occur due to the delay of the treatment period, incorrect treatment, and lack of knowledge. The existing deep learning and machine learning techniques met few issues such as high computational complexity, higher cost associated with the training data, more execution time, noise, feature dimensionality, lower accuracy, low speed, etc. To tackle these problems, we have proposed a hybrid deep learning model with an Archimedes optimization algorithm (HDL-AOA) for bean disease classification. In this work, there are five bean classes of which one is a healthy class whereas the remaining four classes indicate different diseases such as Bean halo blight, Pythium diseases, Rhizoctonia root rot, and Anthracnose abnormalities acquired from the Soybean (Large) Data Set.The hybrid deep learning technique is the combination of wavelet packet decomposition (WPD) and long short term memory (LSTM). Initially, the WPD decomposes the input images into four sub-series. For these sub-series, four LSTM networks were developed. During bean disease classification, an Archimedes optimization algorithm (AOA) enhances the classification accuracy for multiple single LSTM networks. MATLAB software implements the HDL-AOA model for bean disease classification. The proposed model accomplishes lower MAPE than other exiting methods. Finally, the proposed HDL-AOA model outperforms excellent classification results using different evaluation measures such as accuracy, specificity, sensitivity, precision, recall, and F-score.

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Spatio-temporal deep learning model for distortion classification in laparoscopic video

F1000Research ◽

10.12688/f1000research.72980.1 ◽

2021 ◽

Vol 10 ◽

pp. 1010

Author(s):

Nouar AlDahoul ◽

Hezerul Abdul Karim ◽

Abdulaziz Saleh Ba Wazir ◽

Myles Joshua Toledo Tan ◽

Mohammad Faizal Ahmad Fauzi

Keyword(s):

Deep Learning ◽

Large Scale ◽

Spatial Information ◽

Short Term Memory ◽

Learning Model ◽

Motion Blur ◽

Video Frame ◽

Video Enhancement ◽

Deep Learning Model ◽

Laparoscopic Videos

Background: Laparoscopy is a surgery performed in the abdomen without making large incisions in the skin and with the aid of a video camera, resulting in laparoscopic videos. The laparoscopic video is prone to various distortions such as noise, smoke, uneven illumination, defocus blur, and motion blur. One of the main components in the feedback loop of video enhancement systems is distortion identification, which automatically classifies the distortions affecting the videos and selects the video enhancement algorithm accordingly. This paper aims to address the laparoscopic video distortion identification problem by developing fast and accurate multi-label distortion classification using a deep learning model. Current deep learning solutions based on convolutional neural networks (CNNs) can address laparoscopic video distortion classification, but they learn only spatial information. Methods: In this paper, utilization of both spatial and temporal features in a CNN-long short-term memory (CNN-LSTM) model is proposed as a novel solution to enhance the classification. First, pre-trained ResNet50 CNN was used to extract spatial features from each video frame by transferring representation from large-scale natural images to laparoscopic images. Next, LSTM was utilized to consider the temporal relation between the features extracted from the laparoscopic video frames to produce multi-label categories. A novel laparoscopic video dataset proposed in the ICIP2020 challenge was used for training and evaluation of the proposed method. Results: The experiments conducted show that the proposed CNN-LSTM outperforms the existing solutions in terms of accuracy (85%), and F1-score (94.2%). Additionally, the proposed distortion identification model is able to run in real-time with low inference time (0.15 sec). Conclusions: The proposed CNN-LSTM model is a feasible solution to be utilized in laparoscopic videos for distortion identification.

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A Hybrid Deep Learning Model for Predicting Protein Hydroxylation Sites

International Journal of Molecular Sciences ◽

10.3390/ijms19092817 ◽

2018 ◽

Vol 19 (9) ◽

pp. 2817 ◽

Cited By ~ 9

Author(s):

Haixia Long ◽

Bo Liao ◽

Xingyu Xu ◽

Jialiang Yang

Keyword(s):

Deep Learning ◽

Short Term Memory ◽

Learning Model ◽

New Drugs ◽

Post Translational Modifications ◽

Novel Approach ◽

Benchmark Datasets ◽

Memory Network ◽

Scoring Matrix ◽

Deep Learning Model

Protein hydroxylation is one type of post-translational modifications (PTMs) playing critical roles in human diseases. It is known that protein sequence contains many uncharacterized residues of proline and lysine. The question that needs to be answered is: which residue can be hydroxylated, and which one cannot. The answer will not only help understand the mechanism of hydroxylation but can also benefit the development of new drugs. In this paper, we proposed a novel approach for predicting hydroxylation using a hybrid deep learning model integrating the convolutional neural network (CNN) and long short-term memory network (LSTM). We employed a pseudo amino acid composition (PseAAC) method to construct valid benchmark datasets based on a sliding window strategy and used the position-specific scoring matrix (PSSM) to represent samples as inputs to the deep learning model. In addition, we compared our method with popular predictors including CNN, iHyd-PseAAC, and iHyd-PseCp. The results for 5-fold cross-validations all demonstrated that our method significantly outperforms the other methods in prediction accuracy.

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Diagnosing Santa Ana Winds in Southern California with Synoptic-Scale Analysis

Weather and Forecasting ◽

10.1175/waf-d-13-00002.1 ◽

2013 ◽

Vol 28 (3) ◽

pp. 704-710 ◽

Cited By ~ 30

Author(s):

John T. Abatzoglou ◽

Renaud Barbero ◽

Nicholas J. Nauslar

Keyword(s):

Sea Level ◽

Southern California ◽

The United States ◽

Fire Weather ◽

Synoptic Scale ◽

Sea Level Pressure ◽

Mean Sea Level ◽

Santa Ana ◽

Level Pressure ◽

Santa Ana Winds

Abstract Santa Ana winds (SAW) are among the most notorious fire-weather conditions in the United States and are implicated in wildfire and wind hazards in Southern California. This study employs large-scale reanalysis data to diagnose SAW through synoptic-scale dynamic and thermodynamic factors using mean sea level pressure gradient and lower-tropospheric temperature advection, respectively. A two-parameter threshold model of these factors exhibits skill in identifying surface-based characteristics of SAW featuring strong offshore winds and extreme fire weather as viewed through the Fosberg fire weather index across Remote Automated Weather Stations in southwestern California. These results suggest that a strong northeastward gradient in mean sea level pressure aligned with strong cold-air advection in the lower troposphere provide a simple, yet effective, means of diagnosing SAW from synoptic-scale reanalysis. This objective method may be useful for medium- to extended-range forecasting when mesoscale model output may not be available, as well as being readily applied retrospectively to better understand connections between SAW and wildfires in Southern California.

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