scholarly journals BNDNN: Batch Normalization Based Deep Neural Network for Predicting Flood in Urban Areas

2021 ◽  
Author(s):  
Vinay Dubey ◽  
Rahul Katarya
Keyword(s):  
Neural Network ◽  
Natural Disasters ◽  
Urban Areas ◽  
Deep Neural Network ◽  
Developed Countries ◽  
Support Vector ◽  
Human Society ◽  
Batch Normalization ◽  
Proposed Model ◽  
The Impact

Abstract Disaster is a very serious dissipation that arises for a short time period, but the impact of that disaster on human society is very dangerous and very long-lasting. Disasters are categorized into two types like natural disasters and manmade disasters. Among all disasters, of all the natural disasters, flood is the commonplace natural disaster. Flood disaster that causes huge loss of human life, diversity as well as economic loss, which is very dangerous for the developing countries and developed countries also. Nowadays during the monsoon season flood is dangerous for all the geographical areas located nearby water bodies. Much research has been done for flood detection. Machine Learning and many other recent technologies are playing a vital role in predicting the occurrence of floods. For prediction purposes, a huge amount of data is requiring collected from sensors deployed in various locations. In this paper, we used the Batch normalization with Deep Neural Network (BNDNN) technique for the classification of data in three classes as Low, Moderate, and High. The result obtained from our proposed model is compared with some other models like Decision Tree (DT), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Deep Neural Network (DNN). In this our proposed BNDNN provides 89% accuracy which is higher among all existing models. Models are compared based on some parameters like Accuracy, Precision, Recall, F –Score. The compression among all the models used in this paper shows that our proposed model provides better results.

scholarly journals Data Driven Leakage Detection and Classification of a Boiler Tube

2019 ◽  
Vol 9 (12) ◽  
pp. 2450 ◽  
Author(s):  
Muhammad Sohaib ◽  
Jong-Myon Kim
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Wavelet Packet ◽  
Thermal Power ◽  
Leak Detection ◽  
Classification Model ◽  
Support Vector ◽  
Boiler Tube ◽  
Proposed Model ◽  
Ae Signals

Boiler heat exchange in thermal power plants involves tubes to transfer heat from the fuel to the water. Boiler tube leakage can cause outages and huge power generation loss. Therefore, early detection of leaks in boiler tubes is necessary to avoid such accidents. In this study, a boiler tube leak detection and classification mechanism was designed using wavelet packet transform (WPT) analysis of the acoustic emission (AE) signals acquired from the boiler tube and a fully connected deep neural network (FC-DNN). WPT analysis of the AE signals enabled the extraction of features associated with the different conditions of the boiler tube, that is, normal and leak conditions. The deep neural network (DNN) effectively explores the salient information from the wavelet packet features through a deep architecture instead of considering shallow networks, such as k-nearest neighbors (k-NN) and support vector machines (SVM). This enhances the classification performance of the leak identification and classification model developed. The proposed model yielded a 99.2 % average classification accuracy when tested with AE signals from the boiler tube. The experimental results prove the efficacy of the proposed model for boiler tube leak detection and classification.

Research on behavior recognition based on feature fusion of automatic coder and recurrent neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8927-8935
Author(s):  
Bing Zheng ◽  
Dawei Yun ◽  
Yan Liang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Behavior Pattern ◽  
Rapid Development ◽  
Video Data ◽  
Support Vector ◽  
Behavior Recognition ◽  
Learning Methods ◽  
The Impact ◽  
Internet Of Things Technology

Under the impact of COVID-19, research on behavior recognition are highly needed. In this paper, we combine the algorithm of self-adaptive coder and recurrent neural network to realize the research of behavior pattern recognition. At present, most of the research of human behavior recognition is focused on the video data, which is based on the video number. At the same time, due to the complexity of video image data, it is easy to violate personal privacy. With the rapid development of Internet of things technology, it has attracted the attention of a large number of experts and scholars. Researchers have tried to use many machine learning methods, such as random forest, support vector machine and other shallow learning methods, which perform well in the laboratory environment, but there is still a long way to go from practical application. In this paper, a recursive neural network algorithm based on long and short term memory (LSTM) is proposed to realize the recognition of behavior patterns, so as to improve the accuracy of human activity behavior recognition.

scholarly journals Natural Disasters Intensity Analysis and Classification Based on Multispectral Images Using Multi-Layered Deep Convolutional Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2648
Author(s):  
Muhammad Aamir ◽  
Tariq Ali ◽  
Muhammad Irfan ◽  
Ahmad Shaf ◽  
Muhammad Zeeshan Azam ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Natural Disasters ◽  
Deep Convolutional Neural Network ◽  
Multispectral Images ◽  
Learning Techniques ◽  
Proposed Model ◽  
Disaster Intensity ◽  
And Performance

Natural disasters not only disturb the human ecological system but also destroy the properties and critical infrastructures of human societies and even lead to permanent change in the ecosystem. Disaster can be caused by naturally occurring events such as earthquakes, cyclones, floods, and wildfires. Many deep learning techniques have been applied by various researchers to detect and classify natural disasters to overcome losses in ecosystems, but detection of natural disasters still faces issues due to the complex and imbalanced structures of images. To tackle this problem, we propose a multilayered deep convolutional neural network. The proposed model works in two blocks: Block-I convolutional neural network (B-I CNN), for detection and occurrence of disasters, and Block-II convolutional neural network (B-II CNN), for classification of natural disaster intensity types with different filters and parameters. The model is tested on 4428 natural images and performance is calculated and expressed as different statistical values: sensitivity (SE), 97.54%; specificity (SP), 98.22%; accuracy rate (AR), 99.92%; precision (PRE), 97.79%; and F1-score (F1), 97.97%. The overall accuracy for the whole model is 99.92%, which is competitive and comparable with state-of-the-art algorithms.

Control loop oscillation detection and quantification using PRONY method of IIR filter design and deep neural network

2021 ◽  
pp. 1-14
Author(s):  
Sachin Sharma ◽  
Vineet Kumar ◽  
K.P.S. Rana
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Filter Design ◽  
Support Vector ◽  
Control Loop ◽  
External Interference ◽  
Iir Filter ◽  
Prony Method ◽  
Oscillation Detection ◽  
Detection And Quantification

Generally, the process industry is affected by unwanted fluctuations in control loops arising due to external interference, components with inherent nonlinearities or aggressively tuned controllers. These oscillations lead to production of substandard products and thus affect the overall profitability of a plant. Hence, timely detection of oscillations is desired for ensuring safety and profitability of the plant. In order to achieve this, a control loop oscillation detection and quantification algorithm using Prony method of infinite impulse response (IIR) filter design and deep neural network (DNN) has been presented in this work. Denominator polynomial coefficients of the obtained IIR filter using Prony method were used as the feature vector for DNN. Further, DNN is used to confirm the existence of oscillations in the process control loop data. Furthermore, amplitude and frequency of oscillations are also estimated with the help of cross-correlation values, computed between the original signal and estimated error signal. Experimental results confirm that the presented algorithm is capable of detecting the presence of single or multiple oscillations in the control loop data. The proposed algorithm is also able to estimate the frequency and amplitude of detected oscillations with high accuracy. The Proposed method is also compared with support vector machine (SVM) and empirical mode decomposition (EMD) based approach and it is found that proposed method is faster and more accurate than the later.

Validating the impact of accounting disclosures on stock market: A deep neural network approach

2021 ◽  
Vol 170 ◽  
pp. 120903
Author(s):  
Prajwal Eachempati ◽  
Praveen Ranjan Srivastava ◽  
Ajay Kumar ◽  
Kim Hua Tan ◽  
Shivam Gupta
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Deep Neural Network ◽  
Network Approach ◽  
Neural Network Approach ◽  
Accounting Disclosures ◽  
The Impact

scholarly journals Zonation of Landslide Susceptibility in Ruijin, Jiangxi, China

2021 ◽  
Vol 18 (11) ◽  
pp. 5906
Author(s):  
Xiaoting Zhou ◽  
Weicheng Wu ◽  
Ziyu Lin ◽  
Guiliang Zhang ◽  
Renxiang Chen ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Landslide Susceptibility ◽  
Urban Areas ◽  
Support Vector ◽  
Susceptibility Map ◽  
Human Society ◽  
Learning Approaches ◽  
Prevention Measures ◽  
Landslide Occurrence ◽  
Better Than

Landslides are one of the major geohazards threatening human society. The objective of this study was to conduct a landslide hazard susceptibility assessment for Ruijin, Jiangxi, China, and to provide technical support to the local government for implementing disaster reduction and prevention measures. Machine learning approaches, e.g., random forests (RFs) and support vector machines (SVMs) were employed and multiple geo-environmental factors such as land cover, NDVI, landform, rainfall, lithology, and proximity to faults, roads, and rivers, etc., were utilized to achieve our purposes. For categorical factors, three processing approaches were proposed: simple numerical labeling (SNL), weight assignment (WA)-based and frequency ratio (FR)-based. Then 19 geo-environmental factors were respectively converted into raster to constitute three 19-band datasets, i.e., DS1, DS2, and DS3 from three different processes. Then, 155 observed landslides that occurred in the past decades were vectorized, among which 70% were randomly selected to compose a training set (TS1) and the remaining 30% to form a validation set (VS1). A number of non-landslide (no-risk) samples distributed in the whole study area were identified in low slope (<1–3°) zones such as urban areas and croplands, and also added to the TS1 and VS1 in the same ratio. For comparison, we used the FR approach to identify the no-risk samples in both flat and non-flat areas, and merged them into the field-observed landslides to constitute another pair of training and validation sets (TS2 and VS2) using the same ratio of 7:3. The RF algorithm was applied to model the probability of the landslide occurrence using DS1, DS2, and DS3 as predictive variables and TS1 and TS2 for training to obtain the SNL-based, WA-based, and FR-based RF models, respectively. Verified against VS1 and VS2, the three models have similar overall accuracy (OA) and Kappa coefficient (KC), which are 89.61%, 91.47%, and 94.54%, and 0.7926, 0.8299, and 0.8908, respectively. All of them are much better than the three models obtained by SVM algorithm with OA of 81.79%, 82.86%, and 83%, and KC of 0.6337, 0.655, and 0.660. New case verification with the recent 26 landslide events of 2017–2020 revealed that the landslide susceptibility map from WA-based RF modeling was able to properly identify the high and very high susceptibility zones where 23 new landslides had occurred, and performed better than the SNL-based and FR-based RF modeling, though the latter has a slightly higher OA and KC. Hence, we concluded that all three RF models achieve reasonable risk prediction, but WA-based and FR-based RF modeling deserves a recommendation for application elsewhere. The results of this study may serve as reference for the local authorities in prevention and early warning of landslide hazards.

Impact of COVID-19 pandemic on community lifelines

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Naeem Abas ◽  
Esmat Kalair ◽  
Saad Dilshad ◽  
Nasrullah Khan
Keyword(s):  
Natural Disasters ◽  
Health Sector ◽  
Ghg Emissions ◽  
Developed Countries ◽  
World Population ◽  
Water Safety ◽  
Content Type ◽  
Essential Services ◽  
The World ◽  
The Impact

PurposeThe authors present the impact of the coronavirus disease 2019 (COVID-19) pandemic on community lifelines. The state machinery has several departments to secure essential lifelines during disasters and epidemics. Many countries have formed national disaster management authorities to deal with manmade and natural disasters. Typical lifelines include food, water, safety and security, continuity of services, medicines and healthcare equipment, gas, oil and electricity supplies, telecommunication services, transportation means and education system. Supply chain systems are often affected by disasters, which should have alternative sources and routes. Doctors, nurses and medics are front-line soldiers against diseases during pandemics.Design/methodology/approachThe COVID-19 pandemic has revealed how much we all are connected yet unprepared for natural disasters. Political leaders prioritize infrastructures, education but overlook the health sector. During the recent pandemic, developed countries faced more mortalities, fatalities and casualties than developing countries. This work surveys the impact of the COVID-19 pandemic on health, energy, environment, industry, education and food supply lines.FindingsThe COVID-19 pandemic caused 7% reductions in greenhouse gas (GHG) emissions during global lockdowns. In addition, COVID-19 has affected social fabric, behaviors, cultures and official routines. Around 2.84 bn doses have been administrated, with approximately 806 m people (10.3% of the world population) are fully vaccinated around the world to date. Most developed vaccines are being evaluated for new variants like alpha, beta, gamma, epsilons and delta first detected in the UK, South Africa, Brazil, USA and India. The COVID-19 pandemic has affected all sectors in society, yet this paper critically reviews the impact of COVID-19 on health and energy lifelines.Practical implicationsThis paper critically reviews the health and energy lifelines during pandemic COVID-19 and explains how these essential services were interrupted.Originality/valueThis paper critically reviews the health and energy lifelines during pandemic COVID-19 and explains how these essential services were interrupted.

scholarly journals Map of Impact of Natural Hazards

2016 ◽  
pp. 69-77
Author(s):  
Maria BOSTENARU DAN ◽  
◽  
Cristina Olga Gociman ◽  
Keyword(s):  
Natural Disasters ◽  
Natural Hazards ◽  
19Th Century ◽  
Urban Areas ◽  
General Assembly ◽  
German Research ◽  
History Of ◽  
Historical Photographs ◽  
The 19Th Century ◽  
The Impact

This paper investigates the mapping of the impact of natural hazards as included in several databases reviewed or created by the author. These are: - The database of the contribution of the session series “Natural hazards’ impact on urban areas and infrastructure”, convened and co-convened by the first author over 15 years at the European Geosciences General Assembly. - A database created from reviews of students supervised by the authors in frame of the course “Protection of settlements against risks” at the home university. - A collection of historical photographs from the 19th century on different natural and man-made hazards from the Canadian Centre for Architecture, the archive review of which has been performed by the first author and which will be subject of a book to be published about the time of the conference. -Two reviewed collections, one from the exhibition and book on “Images of disasters” (German research) and one on the book “Illustrated history of natural disasters” which include major disasters from the beginning of the mankind. In frame of the paper maps of the spread of data will be presented, created using both arcGIS online and GoogleMaps (see https://www.google.com/maps/d/edit?mid=zpbbz3WgVMBs.k-3vhGj- -l1M&usp=sharing), comparing the source and the type of hazard, to see eventual overlappings between the databases.

Production Forecasting with the Interwell Interference by Integrating Graph Convolutional and Long Short-Term Memory Neural Network

2021 ◽  
pp. 1-17
Author(s):  
Enda Du ◽  
Yuetian Liu ◽  
Ziyan Cheng ◽  
Liang Xue ◽  
Jing Ma ◽  
...  
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Production Forecasting ◽  
Temporal Correlations ◽  
Proposed Model ◽  
The Mean ◽  
Long Short Term Memory ◽  
The Impact

Summary Accurate production forecasting is an essential task and accompanies the entire process of reservoir development. With the limitation of prediction principles and processes, the traditional approaches are difficult to make rapid predictions. With the development of artificial intelligence, the data-driven model provides an alternative approach for production forecasting. To fully take the impact of interwell interference on production into account, this paper proposes a deep learning-based hybrid model (GCN-LSTM), where graph convolutional network (GCN) is used to capture complicated spatial patterns between each well, and long short-term memory (LSTM) neural network is adopted to extract intricate temporal correlations from historical production data. To implement the proposed model more efficiently, two data preprocessing procedures are performed: Outliers in the data set are removed by using a box plot visualization, and measurement noise is reduced by a wavelet transform. The robustness and applicability of the proposed model are evaluated in two scenarios of different data types with the root mean square error (RMSE), the mean absolute error (MAE), and the mean absolute percentage error (MAPE). The results show that the proposed model can effectively capture spatial and temporal correlations to make a rapid and accurate oil production forecast.

Patient visit forecasting in an emergency department using a deep neural network approach

Kybernetes ◽  
2019 ◽  
Vol 49 (9) ◽  
pp. 2335-2348 ◽  
Author(s):  
Milad Yousefi ◽  
Moslem Yousefi ◽  
Masood Fathi ◽  
Flavio S. Fogliatto
Keyword(s):  
Neural Network ◽  
Emergency Department ◽  
Linear Regression ◽  
Deep Neural Network ◽  
Short Term Memory ◽  
Demand Forecasting ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Neural Network Approach ◽  
Content Type

Purpose This study aims to investigate the factors affecting daily demand in an emergency department (ED) and to provide a forecasting tool in a public hospital for horizons of up to seven days. Design/methodology/approach In this study, first, the important factors to influence the demand in EDs were extracted from literature then the relevant factors to the study are selected. Then, a deep neural network is applied to constructing a reliable predictor. Findings Although many statistical approaches have been proposed for tackling this issue, better forecasts are viable by using the abilities of machine learning algorithms. Results indicate that the proposed approach outperforms statistical alternatives available in the literature such as multiple linear regression, autoregressive integrated moving average, support vector regression, generalized linear models, generalized estimating equations, seasonal ARIMA and combined ARIMA and linear regression. Research limitations/implications The authors applied this study in a single ED to forecast patient visits. Applying the same method in different EDs may give a better understanding of the performance of the model to the authors. The same approach can be applied in any other demand forecasting after some minor modifications. Originality/value To the best of the knowledge, this is the first study to propose the use of long short-term memory for constructing a predictor of the number of patient visits in EDs.

Sign in / Sign up

Export Citation Format

Share Document