scholarly journals URBAN FIRE SPREAD MODELLING AND SIMULATION USING CELLULAR AUTOMATON WITH EXTREME LEARNING MACHINE

2019 ◽  
Vol XLII-4/W19 ◽  
pp. 319-326
Author(s):  
J. C. J. Patac ◽  
A. J. O. Vicente
Keyword(s):  
Cellular Automaton ◽  
Extreme Learning Machine ◽  
Confusion Matrix ◽  
Fire Spread ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Ignition Probability ◽  
Urban Fire ◽  
Data Driven Approach ◽  
Learning Machine

Abstract. Urban fire continues to be a persistent disaster, especially with the proliferation of highly dense urban settlements. As a response, several measures were established to help mitigate the losses caused by fire including simulating the fire spread. The cellular automaton system has been widely used to simulate the complex process of fire development along with Physics-based models. A data-driven approach has been rarely employed. This paper presents the result of incorporating machine learning techniques to the existing cellular automaton based urban fire spread models. Specifically, instead of manually calculating the ignition probability of each cell in the automaton, the Extreme Learning Machine (ELM) was used to learn the ignition probability from the historical data. After building the model, its performance was evaluated using the data collected from the four fires in Basak, Lapu-Lapu City. By using a confusion matrix to compare the actual and the predicted values, the Burned Actual – Burned Predicted relationship was derived. Results suggest that the proposed method can effectively describe the development of fire, and the model accuracy is quite good (i.e., the Burned Actual - Burned Predicted relationship ranges from 78% to 83%). Lastly, the study was able to demonstrate the possibility of using a data-driven approach in creating a simple cellular automaton fire spread simulation model for urban areas. Further studies utilizing more fire incident data on with varying properties is recommended.

scholarly journals Forest fire spread simulating model using cellular automaton with extreme learning machine

2017 ◽  
Vol 348 ◽  
pp. 33-43 ◽  
Author(s):  
Zhong Zheng ◽  
Wei Huang ◽  
Songnian Li ◽  
Yongnian Zeng
Keyword(s):  
Cellular Automaton ◽  
Extreme Learning Machine ◽  
Forest Fire ◽  
Fire Spread ◽  
Learning Machine

scholarly journals Flood Forecasting Based on an Improved Extreme Learning Machine Model Combined with the Backtracking Search Optimization Algorithm

Water ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 1362 ◽  
Author(s):  
Lu Chen ◽  
Na Sun ◽  
Chao Zhou ◽  
Jianzhong Zhou ◽  
Yanlai Zhou ◽  
...  
Keyword(s):  
Extreme Learning Machine ◽  
Flood Control ◽  
Search Algorithm ◽  
Flood Forecasting ◽  
Data Driven ◽  
General Regression Neural Network ◽  
Promising Alternative ◽  
Backtracking Search ◽  
Learning Machine ◽  
Population Based Algorithm

Flood forecasting plays an important role in flood control and water resources management. Recently, the data-driven models with a simpler model structure and lower data requirement attract much more attentions. An extreme learning machine (ELM) method, as a typical data-driven method, with the advantages of a faster learning process and stronger generalization ability, has been taken as an effective tool for flood forecasting. However, an ELM model may suffer from local minima in some cases because of its random generation of input weights and hidden layer biases, which results in uncertainties in the flood forecasting model. Therefore, we proposed an improved ELM model for short-term flood forecasting, in which an emerging dual population-based algorithm, named backtracking search algorithm (BSA), was applied to optimize the parameters of ELM. Thus, the proposed method is called ELM-BSA. The upper Yangtze River was selected as a case study. Several performance indexes were used to evaluate the efficiency of the proposed ELM-BSA model. Then the proposed model was compared with the currently used general regression neural network (GRNN) and ELM models. Results show that the ELM-BSA can always provide better results than the GRNN and ELM models in both the training and testing periods. All these results suggest that the proposed ELM-BSA model is a promising alternative technique for flood forecasting.

scholarly journals Aircraft Engines Remaining Useful Life Prediction with an Improved Online Sequential Extreme Learning Machine

2020 ◽  
Vol 10 (3) ◽  
pp. 1062 ◽  
Author(s):  
Tarek Berghout ◽  
Leïla-Hayet Mouss ◽  
Ouahab Kadri ◽  
Lotfi Saïdi ◽  
Mohamed Benbouzid
Keyword(s):  
Extreme Learning Machine ◽  
Life Prediction ◽  
Remaining Useful Life ◽  
Data Driven ◽  
Selection Strategy ◽  
Mapping Technique ◽  
Model Parameters ◽  
Aircraft Engines ◽  
Useful Life ◽  
Learning Machine

The efficient data investigation for fast and accurate remaining useful life prediction of aircraft engines can be considered as a very important task for maintenance operations. In this context, the key issue is how an appropriate investigation can be conducted for the extraction of important information from data-driven sequences in high dimensional space in order to guarantee a reliable conclusion. In this paper, a new data-driven learning scheme based on an online sequential extreme learning machine algorithm is proposed for remaining useful life prediction. Firstly, a new feature mapping technique based on stacked autoencoders is proposed to enhance features representations through an accurate reconstruction. In addition, to attempt into addressing dynamic programming based on environmental feedback, a new dynamic forgetting function based on the temporal difference of recursive learning is introduced to enhance dynamic tracking ability of newly coming data. Moreover, a new updated selection strategy was developed in order to discard the unwanted data sequences and to ensure the convergence of the training model parameters to their appropriate values. The proposed approach is validated on the C-MAPSS dataset where experimental results confirm that it yields satisfactory accuracy and efficiency of the prediction model compared to other existing methods.

scholarly journals Forecasting of Landslide Displacement Using a Probability-Scheme Combination Ensemble Prediction Technique

2020 ◽  
Vol 17 (13) ◽  
pp. 4788 ◽  
Author(s):  
Junwei Ma ◽  
Xiao Liu ◽  
Xiaoxu Niu ◽  
Yankun Wang ◽  
Tao Wen ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Quantile Regression ◽  
Extreme Learning Machine ◽  
Data Driven ◽  
Ensemble Prediction ◽  
Three Gorges Reservoir Area ◽  
Support Vector ◽  
Landslide Displacement Prediction ◽  
Learning Machine

Data-driven models have been extensively employed in landslide displacement prediction. However, predictive uncertainty, which consists of input uncertainty, parameter uncertainty, and model uncertainty, is usually disregarded in deterministic data-driven modeling, and point estimates are separately presented. In this study, a probability-scheme combination ensemble prediction that employs quantile regression neural networks and kernel density estimation (QRNNs-KDE) is proposed for robust and accurate prediction and uncertainty quantification of landslide displacement. In the ensemble model, QRNNs serve as base learning algorithms to generate multiple base learners. Final ensemble prediction is obtained by integration of all base learners through a probability combination scheme based on KDE. The Fanjiaping landslide in the Three Gorges Reservoir area (TGRA) was selected as a case study to explore the performance of the ensemble prediction. Based on long-term (2006–2018) and near real-time monitoring data, a comprehensive analysis of the deformation characteristics was conducted for fully understanding the triggering factors. The experimental results indicate that the QRNNs-KDE approach can perform predictions with perfect performance and outperform the traditional backpropagation (BP), radial basis function (RBF), extreme learning machine (ELM), support vector machine (SVM) methods, bootstrap-extreme learning machine-artificial neural network (bootstrap-ELM-ANN), and Copula-kernel-based support vector machine quantile regression (Copula-KSVMQR). The proposed QRNNs-KDE approach has significant potential in medium-term to long-term horizon forecasting and quantification of uncertainty.

scholarly journals Perbandingan Algoritma ELM Dan Backpropagation Terhadap Prestasi Akademik Mahasiswa

2019 ◽  
Vol 3 (2) ◽  
pp. 282
Author(s):  
Heny Pratiwi ◽  
Kusno Harianto
Keyword(s):  
Extreme Learning Machine ◽  
Error Rate ◽  
Confusion Matrix ◽  
Evaluation Process ◽  
First Grade ◽  
Entrance Examination ◽  
Testing Stage ◽  
New Students ◽  
Learning Machine

Extreme Learning Machine and Backpropagation Algorithms are used in this study to find out which algorithm is most suitable for knowing student academic achievement. The data about students are explored to get a pattern so that the characteristics of new students can be known every year. The evaluation process of this study uses confusion matrix for the introduction of correctly recognized data and unknown data. Comparison of this algorithm uses student data at the beginning of the lecture as early detection of students who have problems with academics to be anticipated. The variables used are the value of the entrance examination for new students, the first grade IP value, Gender, and Working Status, while the output variable is the quality value as a classification of academic performance. The results of this study state that the Extreme Learning Machine algorithm has a 14.84% error rate lower than Backpropagation 28.20%. From the model testing stage, the most accurate result is the Extreme Learning Machine algorithm because it has the highest accuracy and the lowest error rate.

scholarly journals Applying a Neural Network Ensemble to Intrusion Detection

2019 ◽  
Vol 9 (3) ◽  
pp. 177-188 ◽  
Author(s):  
Simone A. Ludwig
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
Detection System ◽  
Confusion Matrix ◽  
Ensemble Method ◽  
Machine Learning Techniques ◽  
Neural Network Ensemble ◽  
Data Set ◽  
The Neural Network ◽  
Learning Machine

Abstract An intrusion detection system (IDS) is an important feature to employ in order to protect a system against network attacks. An IDS monitors the activity within a network of connected computers as to analyze the activity of intrusive patterns. In the event of an ‘attack’, the system has to respond appropriately. Different machine learning techniques have been applied in the past. These techniques fall either into the clustering or the classification category. In this paper, the classification method is used whereby a neural network ensemble method is employed to classify the different types of attacks. The neural network ensemble method consists of an autoencoder, a deep belief neural network, a deep neural network, and an extreme learning machine. The data used for the investigation is the NSL-KDD data set. In particular, the detection rate and false alarm rate among other measures (confusion matrix, classification accuracy, and AUC) of the implemented neural network ensemble are evaluated.

scholarly journals Driving Drowsiness Detection with EEG Using a Modified Hierarchical Extreme Learning Machine Algorithm with Particle Swarm Optimization: A Pilot Study

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 775 ◽  
Author(s):  
Yuliang Ma ◽  
Songjie Zhang ◽  
Donglian Qi ◽  
Zhizeng Luo ◽  
Rihui Li ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Extreme Learning Machine ◽  
Particle Swarm ◽  
Machine Learning Techniques ◽  
Superior Performance ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Swarm Optimization ◽  
Eeg Data ◽  
Learning Machine

Driving fatigue accounts for a large number of traffic accidents in modern life nowadays. It is therefore of great importance to reduce this risky factor by detecting the driver’s drowsiness condition. This study aimed to detect drivers’ drowsiness using an advanced electroencephalography (EEG)-based classification technique. We first collected EEG data from six healthy adults under two different awareness conditions (wakefulness and drowsiness) in a virtual driving experiment. Five different machine learning techniques, including the K-nearest neighbor (KNN), support vector machine (SVM), extreme learning machine (ELM), hierarchical extreme learning machine (H-ELM), and the proposed modified hierarchical extreme learning machine algorithm with particle swarm optimization (PSO-H-ELM), were applied to classify the subject’s drowsiness based on the power spectral density (PSD) feature extracted from the EEG data. The mean accuracies of the five classifiers were 79.31%, 79.31%, 74.08%, 81.67%, and 83.12%, respectively, demonstrating the superior performance of our new PSO-H-ELM algorithm in detecting drivers’ drowsiness, compared to the other techniques.

scholarly journals An Optimized Brain-Based Algorithm for Classifying Parkinson’s Disease

2020 ◽  
Vol 10 (5) ◽  
pp. 1827 ◽  
Author(s):  
Rodrigo Olivares ◽  
Roberto Munoz ◽  
Ricardo Soto ◽  
Broderick Crawford ◽  
Diego Cárdenas ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Extreme Learning Machine ◽  
Neurodegenerative Disorder ◽  
Fine Tuning ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Classification Problems ◽  
Learning Machine

During the last years, highly-recognized computational intelligence techniques have been proposed to treat classification problems. These automatic learning approaches lead to the most recent researches because they exhibit outstanding results. Nevertheless, to achieve this performance, artificial learning methods firstly require fine tuning of their parameters and then they need to work with the best-generated model. This process usually needs an expert user for supervising the algorithm’s performance. In this paper, we propose an optimized Extreme Learning Machine by using the Bat Algorithm, which boosts the training phase of the machine learning method to increase the accuracy, and decreasing or keeping the loss in the learning phase. To evaluate our proposal, we use the Parkinson’s Disease audio dataset taken from UCI Machine Learning Repository. Parkinson’s disease is a neurodegenerative disorder that affects over 10 million people. Although its diagnosis is through motor symptoms, it is possible to evidence the disorder through variations in the speech using machine learning techniques. Results suggest that using the bio-inspired optimization algorithm for adjusting the parameters of the Extreme Learning Machine is a real alternative for improving its performance. During the validation phase, the classification process for Parkinson’s Disease achieves a maximum accuracy of 96.74% and a minimum loss of 3.27%.

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