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An Intelligent System For Effective Forest Fire Detection Using Spatial Data [article]

K. Angayarkkani, N. Radhakrishnan
2010 arXiv   pre-print
Subsequently, Radial Basis Function Neural Network is employed in the design of the intelligent system, which is trained with the color space values of the segmented fire regions.  ...  This paper presents an intelligent system to detect the presence of forest fires in the forest spatial data using Artificial Neural Networks.  ...  Radial Basis Function Neural Network (RBFNN) is based on supervised learning.  ... 
arXiv:1002.2199v1 fatcat:zxjqwb6j3jhevbxqeyv34vd5ra

Research on Electronic-nose Application Based on Wireless Sensor Networks

A Zhao, L Wang, C H Yao
2006 Journal of Physics, Conference Series  
In the study, the authors researched a data processing algorithm: fuzzy neural network based on RBF(Radial Basis Function) network model, to quantitatively analyze the gas ingredient and put forward a  ...  The paper proposed a structure of Wireless Sensor Networks based Electronic-nose system to monitors air quality in the building.  ...  The paper put forwards an algorithm of fuzzy neural network based on RBF (Radial Basis Function) network model and it adapts to quantitative analysis of ingredient consistency in the air.  ... 
doi:10.1088/1742-6596/48/1/046 fatcat:qhwq4vtytrft7g7uelefpncyfa

Radial and Sigmoid Basis Function Neural Networks in Wireless Sensor Routing Topology Control in Underground Mine Rescue Operation Based on Particle Swarm Optimization

Mary Opokua Ansong, Hong-Xing Yao, Jun Steed Huang
2013 International Journal of Distributed Sensor Networks  
The performance of a proposed compact radial basis function was compared with the sigmoid basis function and the gaussian-radial basis function neural networks in 3D wireless sensor routing topology control  ...  Using Matlab, the optimised vectors with high survival rate and fault tolerant, based on rock type, were generated as inputs for the neural networks.  ...  Acknowledgments The authors would like to appreciate the immense contribution of the mining companies where the study was undertaken.  ... 
doi:10.1155/2013/376931 fatcat:i37hawfmhjh7td6emtq5igoh2e

Non-Gaussian Hybrid Transfer Functions: Memorizing Mine Survivability Calculations

Mary Opokua Ansong, Jun Steed Huang, Mary Ann Yeboah, Han Dun, Hongxing Yao
2015 Mathematical Problems in Engineering  
The speed and simplicity of the non-Gaussian type with the accuracy and simplicity of radial basis function are used to produce fast and accurate on-the-fly model for survivability of emergency mine rescue  ...  Different from existing methods in radial basis transfer function construction, this study proposes a novel nonlinear-weight hybrid algorithm involving the non-Gaussian type radial basis transfer functions  ...  The authors would like to appreciate the immense contribution of the mining companies where the study was undertaken.  ... 
doi:10.1155/2015/623720 fatcat:qheulgwezvewrjdcda5baut3km

Advances in Remote Sensing-Based Disaster Monitoring and Assessment

Jungho Im, Haemi Park, Wataru Takeuchi
2019 Remote Sensing  
Extreme weather/climate events have been increasing partly due to on-going climate change [...]  ...  The highest model performance in terms of accuracy was achieved by the LSSVM with a radial basis function (RBF) kernel.  ...  Signals were detected using a state-of-the-art machine learning approach, convolutional neural networks.  ... 
doi:10.3390/rs11182181 fatcat:4dhm4m7njvhi5mbvypjh56yoka

ARTIFICIAL INTELLIGENCE AND GEOSPATIAL ANALYSIS IN DISASTER MANAGEMENT

M. Ivić
2019 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
Using AI in the data analysis can identify risk areas and determine future needs. This paper presents an overview of the use of AI in geospatial analysis in disaster management.</p>  ...  </strong> For quick and efficient response, as well as for recovery after any natural or artificial catastrophe, one of the most important things are accurate and reliable spatial data in real or near  ...  Syifa et al. (2019) presented a methodology for post-earthquake damage mapping using artificial neural network (the back propagation algorithm), support vector machine (radial basis function -RBF) classifiers  ... 
doi:10.5194/isprs-archives-xlii-3-w8-161-2019 fatcat:uhqreow4qvc6fg7iamccy4q6y4

Evaluating structural safety of trusses using Machine Learning

Tran-Hieu Nguyen, Anh-Tuan Vu
2021 Frattura ed Integrità Strutturale  
For the 47-bar truss, the accuracies of the Support Vector Machine model and the Deep Neural Network model are lower than 70% but the Adaptive Boosting model still retains the high accuracy of approximately  ...  In addition, an investigation is carried out to show the influence of the parameters on the performance of the Adaptive Boosting model.  ...  Subsequently, a hybrid approach that combines the proper orthogonal decomposition with radial basis function (POD-RBF) and the cuckoo search optimization algorithm to determine the position and dimensions  ... 
doi:10.3221/igf-esis.58.23 fatcat:jixi2p6fpzgrfdu2u5qk2r3kl4

Special Issue: Fuzzy logic systems for transportation engineering

Dalin Zhang, Sabah Mohammed, Alessandro Calvi
2021 Journal of Intelligent & Fuzzy Systems  
On the basis of a platform 87 network effect perspective, this study constructed 88 an evolutionary game model of value co-creation 89 behavior for a shared supply chain platform and 90 manufacturers,  ...  Based on the traffic data of Beijing, this 101 paper used the principal component analysis method 102 to establish a reliable indicator system of efficiency 103 evaluation.  ...  Analysis 398 the constraint conditions between prefabricated con-399 struction projects. Then the radial basis function 400 (RBF) fuzzy logic neural network algorithm was 401 introduced.  ... 
doi:10.3233/jifs-189957 fatcat:bizv5ygq4zhgteetjcsaug3scy

Geospatial Multicriteria Analysis for Earthquake Risk Assessment: Case Study of Fujairah City in the UAE

Diena Al-Dogom, Rami Al-Ruzouq, Bahareh Kalantar, Karen Schuckman, Saeed Al-Mansoori, Sunanda Mukherjee, Hussain Al-Ahmad, Naonori Ueda, Carmine Granata
2021 Journal of Sensors  
Experts' opinions and literature reviews were the basis of the AHP ranking and weighting system.  ...  This study builds on previous research on the seismic hazard that extracted the eastern part of the UAE as the most hazard-prone zone.  ...  Conflicts of Interest The authors declare that there is no conflict of interest regarding the publication of this paper. Authors' Contributions  ... 
doi:10.1155/2021/6638316 fatcat:7uymfqwlifelxgeit7ahdhvcfy

Application of Artificial Intelligence in Predicting Earthquakes: State-of-the-Art and Future Challenges

Md. Hasan Al Banna, Kazi Abu Taher, M. Shamim Kaiser, Mufti Mahmud, Md. Sazzadur Rahman, A. S. M. Sanwar Hosen, Gi Hwan Cho
2020 IEEE Access  
For the error range of ±0.5, this model predicted 84.21% samples correctly. Wang et al. [42] proposed an LSTM based model for the prediction of earthquakes.  ...  DEEP LEARNING APPROACHES Here we will discuss the outcome of DL-based researches. Kanarachos et al.  ...  function PHMM poisson hidden Markov model PNN probabilistic neural network PR polynomial regression PRNN pattern recognition neural network PSO particle swarm optimization RBFNN radial basis function  ... 
doi:10.1109/access.2020.3029859 fatcat:m53zn4ulq5c2neezhqa53qirca

A New Machine-Learning Prediction Model for Slope Deformation of an Open-Pit Mine: An Evaluation of Field Data

Sunwen Du, Guorui Feng, Jianmin Wang, Shizhe Feng, Reza Malekian, Zhixiong Li
2019 Energies  
The evaluation of the field data acquired from the Anjialing open-pit mine demonstrates that the proposed slope deformation model was able to precisely predict the slope deformation of the monitored mine  ...  Ground-based interferometric radar (GB-SAR) was employed to collect the slope deformation data from an open-pit mine.  ...  Conflicts of Interest: The authors declare no conflicts of interest.  ... 
doi:10.3390/en12071288 fatcat:eo5lfykscfcuznhz4dz4aznf5a

AEGIS: a wildfire prevention and management information system

Kostas Kalabokidis, Alan Ager, Mark Finney, Nikos Athanasis, Palaiologos Palaiologou, Christos Vasilakos
2016 Natural Hazards and Earth System Sciences  
Artificial neural networks (ANNs) were utilized for wildfire ignition risk assessment based on various parameters, training methods, activation functions, pre-processing methods and network structures.  ...  AEGIS offers three types of simulations, i.e., single-fire propagation, point-scale calculation of potential fire behavior, and burn probability analysis, similar to the FlamMap fire behavior modeling  ...  The research project "AEGIS: Wildfire Prevention and Management Information System" (Code Number 1862) is implemented within the framework of the Action ARISTEIA of the Operational Program "Education and  ... 
doi:10.5194/nhess-16-643-2016 fatcat:znhnn67xhvcbdboebxnmoas4b4

AEGIS: a wildfire prevention and management information system

K. Kalabokidis, A. Ager, M. Finney, N. Athanasis, P. Palaiologou, C. Vasilakos
2015 NHESSD  
Artificial neural networks (ANN) were utilized for wildfire ignition risk assessment based on various parameters, training methods, activation functions, pre-processing methods and network structures.  ...  AEGIS offers three types of simulations; i.e. single-fire propagations, conditional burn probabilities and at the landscape-level, similar to the FlamMap fire behavior modeling software.  ...  The research project "AEGIS: Wildfire Prevention and Management Information System" (Code Number 1862) is implemented within the framework of the Action ARISTEIA of the Operational Program "Education and  ... 
doi:10.5194/nhessd-3-6185-2015 fatcat:wz576qx6kbh3tgmsusqtuogxf4

An Initial Machine Learning-Based Victim's Scream Detection Analysis for Burning Sites

Fairuz Samiha Saeed, Abdullah Al Bashit, Vishu Viswanathan, Damian Valles
2021 Applied Sciences  
The performance of these two ML techniques has been evaluated based on a variety of performance metrics.  ...  Fire incidents are responsible for severe damage and thousands of deaths every year all over the world.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/app11188425 fatcat:modpqxd5zfditaerh6stl2kr3u

Chaotic Time Series Forecasting Approaches Using Machine Learning Techniques: A Review

Bhukya Ramadevi, Kishore Bingi
2022 Symmetry  
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY  ...  correlation BP R 2 Backpropagation Coefficient of determination BPNN RBF Backpropagation neural network Radial basis function CBAM RBFNN Radial basis function neural network Convolutional block attention  ...  However, in the hidden layer, wavelet basis functions are used as activation functions instead of the conventional function of the FFNN. Antonis K.  ... 
doi:10.3390/sym14050955 dblp:journals/symmetry/RamadeviB22 fatcat:3oa3go7rdzdurjl4yxcivjsbf4
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