scholarly journals Flood Prediction and warning system using SVM and ELM models.

2019 ◽  
Vol 8 (4) ◽  
pp. 5366-5369
Keyword(s):  
Tamil Nadu ◽  
Prediction Models ◽  
Warning System ◽  
Flood Management ◽  
Support Vector ◽  
Flood Prediction ◽  
Live Feed ◽  
Svm Model ◽  
Learning Machine ◽  
Better Than

Seeing the rising amount of flood calamities worldwide flood management system are recently in limelight and are receiving the much needed attention. However the technologies used in determining and predicting the occurrence of a flood is somewhat inaccurate. Taking into consideration the number of lives at stake this project is aimed at introducing newer and possibly, more effective methods and techniques than its previously used flood prediction models. The proposed system seeks to implement machine learning by gathering the previously existing data along with a periodic live feed update so as to predict the chances of flood occurrence and so as to implement the necessary counteractive measures that can be deployed so as to evade such a mishap. The area taken into consideration for testing this new system is based on Chennai; capital of Tamil Nadu which spans over an area of 426 km2 .The study illustrates how a hybrid model is generated by taking all the data and using the Support Vector Machine (SVM) model and Extreme Learning Machine (ELM) model on it. The experimental results show that the integrated algorithm performs much better than other benchmarks. Moreover, testing the algorithm with live data makes it even more efficient and precise compared to other algorithms and proposed systems helping us to counteract real time fiascos. The main application of this system is to enable the user to warn and evacuate a mass population in case of a mishap.

scholarly journals Applications of Support Vector Machine in Genomic Prediction in Pig and Maize Populations

2020 ◽  
Vol 11 ◽  
Author(s):  
Wei Zhao ◽  
Xueshuang Lai ◽  
Dengying Liu ◽  
Zhenyang Zhang ◽  
Peipei Ma ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Plant Breeding ◽  
Genomic Prediction ◽  
Prediction Models ◽  
Prediction Performance ◽  
Kernel Functions ◽  
Support Vector ◽  
Data Sets ◽  
Svm Model ◽  
Better Than

Genomic prediction (GP) has revolutionized animal and plant breeding. However, better statistical models that can improve the accuracy of GP are required. For this reason, in this study, we explored the genomic-based prediction performance of a popular machine learning method, the Support Vector Machine (SVM) model. We selected the most suitable kernel function and hyperparameters for the SVM model in eight published genomic data sets on pigs and maize. Next, we compared the SVM model with RBF and the linear kernel functions to the two most commonly used genome-enabled prediction models (GBLUP and BayesR) in terms of prediction accuracy, time, and the memory used. The results showed that the SVM model had the best prediction performance in two of the eight data sets, but in general, the predictions of both models were similar. In terms of time, the SVM model was better than BayesR but worse than GBLUP. In terms of memory, the SVM model was better than GBLUP and worse than BayesR in pig data but the same with BayesR in maize data. According to the results, SVM is a competitive method in animal and plant breeding, and there is no universal prediction model.

scholarly journals Three-Dimensional Site Characterization Model of Bangalore Using Support Vector Machine

2012 ◽  
Vol 2012 ◽  
pp. 1-10
Author(s):  
Pijush Samui
Keyword(s):  
Support Vector Machine ◽  
Three Dimensional ◽  
Site Characterization ◽  
Standard Penetration Test ◽  
Support Vector ◽  
Characterization Model ◽  
Svm Model ◽  
Input Variables ◽  
Learning Machine ◽  
A Site

The main objective of site characterization is the prediction of in situ soil properties at any half-space point at a site based on limited tests. In this study, the Support Vector Machine (SVM) has been used to develop a three dimensional site characterization model for Bangalore, India based on large amount of Standard Penetration Test. SVM is a novel type of learning machine based on statistical learning theory, uses regression technique by introducing ε-insensitive loss function. The database consists of 766 boreholes, with more than 2700 field SPT values () spread over 220 sq km area of Bangalore. The model is applied for corrected () values. The three input variables (, , and , where , , and are the coordinates of the Bangalore) were used for the SVM model. The output of SVM was the data. The results presented in this paper clearly highlight that the SVM is a robust tool for site characterization. In this study, a sensitivity analysis of SVM parameters (σ, , and ε) has been also presented.

scholarly journals Flood Prediction and Warning System using Dam Data Monitoring

2020 ◽  
Vol 9 (9) ◽  
pp. 294-298
Keyword(s):  
Machine Learning ◽  
Flood Hazard ◽  
Warning System ◽  
Support Vector ◽  
Flood Prediction ◽  
Loss Of Life ◽  
Related Data ◽  
Dam Management ◽  
Weather Parameters ◽  
The Impact

Flood is one of the most devastating natural calamities affecting parts of the state from past few years. The recurring calamity necessitates an efficient early warning system since anticipation and preparedness play a key role in mitigating the impact. Though heavy and erratic rainfall has been marked as one of the main reasons for flood in several places, flood witnessed by various regions of Kerala was the result of sudden opening of reservoirs indicating poor dam management. The unforeseen flow of water often provided less time for evacuation. Prediction thus plays key role in avoiding loss of life and property, followed by such calamities. The vast benefits and potentials offered by Machine Learning makes it the most promising approach. The developed system is a model by taking Malampuzha Dam as reference. Support Vector Machine (SVM) is used as machine learning method for prediction and is programmed in python. The idea has been to create early flood prediction and warning system by monitoring different weather parameters and dam-related data. The feature vectors include current live storage, current reservoir level, rainfall and relative humidity from the period 2016-2019. Based on the analysis of these parameters, the open/closure of shutters of the dam is predicted. Release of shutters has varied impacts in the nearby regions and is measured by succeeding prediction, by mapping regions on grounds of level warning to be issued. Warning is issued through Flask-based server, by identifying vulnerable areas based on flood hazard reference for regions. The dam status prediction model delivered highest prediction accuracy of 99.14% and associated levels of warning has been generated in the development server, thus preventing unexpected release.

Multivariate information fusion for identifying antifungal peptides with Hilbert-Schmidt Independence Criterion

2021 ◽  
Vol 16 ◽  
Author(s):  
Haohao Zhou ◽  
Hao Wang ◽  
Yijie Ding ◽  
Jijun Tang
Keyword(s):  
Fungal Infections ◽  
Practical Significance ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Sequence Information ◽  
Kernel Support Vector Machine ◽  
Svm Model ◽  
Antifungal Peptides ◽  
Independence Criterion ◽  
Better Than

Background: Antifungal peptides (AFP) have been found to be effective against many fungal infections. Objective: However, it is difficult to identify AFP. Therefore, it is great practical significance to identify AFP via machine learning methods (with sequence information). Method: In this study, a Multi-Kernel Support Vector Machine (MKSVM) with Hilbert-Schmidt Independence Criterion (HSIC) is proposed. Proteins are encoded with five types of features (188-bit, AAC, ASDC, CKSAAP, DPC), and then construct kernels using Gaussian kernel function. HSIC are used to combine kernels and multi-kernel SVM model is built. Results: Our model performed well on three AFPs datasets and the performance is better than or comparable to other state-of-art predictive models. Conclusion: Our method will be a useful tool for identifying antifungal peptides.

scholarly journals Machine Learning for Prediction of Energy in Wheat Production

Agriculture ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 517
Author(s):  
Ali Mostafaeipour ◽  
Mohammad Bagher Fakhrzad ◽  
Sajad Gharaat ◽  
Mehdi Jahangiri ◽  
Joshuva Arockia Dhanraj ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Prediction Models ◽  
Prediction Method ◽  
Output Energy ◽  
Support Vector ◽  
Fars Province ◽  
Wheat Production ◽  
Total Input ◽  
Learning Machine ◽  
Production Period

The global population growth has led to a considerable rise in demand for wheat. Today, the amount of energy consumption in agriculture has also increased due to the need for sufficient food for the growing population. Thus, agricultural policymakers in most countries rely on prediction models to influence food security policies. This research aims to predict and reduce the amount of energy consumption in wheat production. Data were collected from the farms of Estahban city in Fars province of Iran by the Jihad Agricultural Department’s experts for 20 years from 1994 to 2013. In this study, a novel prediction method based on consumed energy in the production period is proposed. The model is developed based on artificial intelligence to forecast the output energy in wheat production and uses extreme learning machine (ELM) and support vector regression (SVR). In the experimental stage, the value of elevation metrics for the EVM and ELM was reported to be equal to 0.000000409 and 0.9531, respectively. Total input energy (consumed) is found to be 1,460,503.1 Mega Joules (MJ), and output energy (produced wheat) is 1,401,011.945 MJ for the Estahban. The result indicates the superiority of the ELM model to enhance the decisions of the agricultural policymakers.

scholarly journals Modeling the Maximum Magnetic Entropy Change of Doped Manganite Using a Grid Search-Based Extreme Learning Machine and Hybrid Gravitational Search-Based Support Vector Regression

Crystals ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 310 ◽  
Author(s):  
Sami M. Ibn Shamsah ◽  
Taoreed O. Owolabi
Keyword(s):  
Magnetocaloric Effect ◽  
Magnetic Entropy Change ◽  
Entropy Change ◽  
Support Vector ◽  
Grid Search ◽  
Magnetic Entropy ◽  
Ionic Model ◽  
Crystal Model ◽  
Learning Machine ◽  
Better Than

The thermal response of a magnetic solid to an applied magnetic field constitutes magnetocaloric effect. The maximum magnetic entropy change (MMEC) is one of the quantitative parameters characterizing this effect, while the magnetic solids exhibiting magnetocaloric effect have great potential in magnetic refrigeration technology as they offer a green solution to the known pollutant-based refrigerants. In order to determine the MMEC of doped manganite and the influence of dopants on the magnetocaloric effect of doped manganite compounds, this work developed a grid search (GS)-based extreme learning machine (ELM) and hybrid gravitational search algorithm (GSA)-based support vector regression (SVR) for estimating the MMEC of doped manganite compounds using ionic radii and crystal lattice parameters as descriptors. Based on the root-mean-square error (RMSE), the developed GSA-SVR-radii model performs better than the existing genetic algorithm (GA)-SVR-ionic model in the literature by 27.09%, while the developed GSA-SVR-crystal model performs better than the existing GA-SVR-lattice model in the literature by 38.34%. Similarly, the developed ELM-GS-crystal model performs better than the existing GA-SVR-ionic model with a performance enhancement of 14.39% and 20.65% using the mean absolute error (MAE) and RMSE, respectively, as performance measuring parameters. The developed models also perform better than the existing models using correlation coefficient as the performance measuring parameter when validated with experimentally measured MMEC. The superior performance of the present models coupled with easy accessibility of the descriptors definitely will facilitate the synthesis of doped manganite compounds with a high magnetocaloric effect without experimental stress.

scholarly journals Real-time flood forecast using the coupling support vector machine and data assimilation method

2013 ◽  
Vol 16 (5) ◽  
pp. 973-988 ◽  
Author(s):  
Xiao-Li Li ◽  
Haishen Lü ◽  
Robert Horton ◽  
Tianqing An ◽  
Zhongbo Yu
Keyword(s):  
Support Vector Machine ◽  
Real Time ◽  
Time Scale ◽  
Support Vector ◽  
Flood Forecast ◽  
Risk Minimization ◽  
Svm Model ◽  
The Impact ◽  
Assimilation Time ◽  
Better Than

An accurate and real-time flood forecast is a crucial nonstructural step to flood mitigation. A support vector machine (SVM) is based on the principle of structural risk minimization and has a good generalization capability. The ensemble Kalman filter (EnKF) is a proven method with the capability of handling nonlinearity in a computationally efficient manner. In this paper, a type of SVM model is established to simulate the rainfall–runoff (RR) process. Then, a coupling model of SVM and EnKF (SVM + EnKF) is used for RR simulation. The impact of the assimilation time scale on the SVM + EnKF model is also studied. A total of four different combinations of the SVM and EnKF models are studied in the paper. The Xinanjiang RR model is employed to evaluate the SVM and the SVM + EnKF models. The study area is located in the Luo River Basin, Guangdong Province, China, during a nine-year period from 1994 to 2002. Compared to SVM, the SVM + EnKF model substantially improves the accuracy of flood prediction, and the Xinanjiang RR model also performs better than the SVM model. The simulated result for the assimilation time scale of 5 days is better than the results for the other cases.

Classification of Automobile Lubricant by Near-Infrared Spectroscopy Combined with Machine Classification

2011 ◽  
Vol 460-461 ◽  
pp. 667-672
Author(s):  
Yun Zhao ◽  
Xing Xu ◽  
Yong He
Keyword(s):  
Near Infrared ◽  
Prediction Models ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Support Vector ◽  
Training Set ◽  
Test Set ◽  
Svm Model

The main objective of this paper is to classify four kinds of automobile lubricant by near-infrared (NIR) spectral technology and to observe whether NIR spectroscopy could be used for predicting water content. Principle component analysis (PCA) was applied to reduce the information from the spectral data and first two PCs were used to cluster the samples. Partial least square (PLS), least square support vector machine (LS-SVM), and Gaussian processes classification (GPC) were employed to develop prediction models. There were 120 samples for training set and test set. Two LS-SVM models with first five PCs and first six PCs were built, respectively, and accuracy of the model with five PCs is adequate with less calculation. The results from the experiment indicate that the LS-SVM model outperforms the PLS model and GPC model outperforms the LS-SVM model.

scholarly journals COLLAPSE WARNING SYSTEM USING LSTM NEURAL NETWORKS FOR CONSTRUCTION DISASTER PREVENTION IN EXTREME WIND WEATHER

2021 ◽  
Vol 27 (4) ◽  
pp. 230-245
Author(s):  
Chih-Chiang Wei
Keyword(s):  
Neural Networks ◽  
Prediction Models ◽  
Short Term Memory ◽  
Meteorological Data ◽  
Warning System ◽  
Weather Conditions ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Strong Wind ◽  
Wind Force

Strong wind during extreme weather conditions (e.g., strong winds during typhoons) is one of the natural factors that cause the collapse of frame-type scaffolds used in façade work. This study developed an alert system for use in determining whether the scaffold structure could withstand the stress of the wind force. Conceptually, the scaffolds collapsed by the warning system developed in the study contains three modules. The first module involves the establishment of wind velocity prediction models. This study employed various deep learning and machine learning techniques, namely deep neural networks, long short-term memory neural networks, support vector regressions, random forest, and k-nearest neighbors. Then, the second module contains the analysis of wind force on the scaffolds. The third module involves the development of the scaffold collapse evaluation approach. The study area was Taichung City, Taiwan. This study collected meteorological data from the ground stations from 2012 to 2019. Results revealed that the system successfully predicted the possible collapse time for scaffolds within 1 to 6 h, and effectively issued a warning time. Overall, the warning system can provide practical warning information related to the destruction of scaffolds to construction teams in need of the information to reduce the damage risk.

scholarly journals Analysis of Model Performance and Implementation of an Optimized Flood Prediction Model Using Data Mining Techniques

2021 ◽  
Vol 10 (2) ◽  
pp. 888-893
Keyword(s):  
Prediction Model ◽  
Model Performance ◽  
Warning System ◽  
Support Vector ◽  
Ann Model ◽  
Flood Prediction ◽  
River Region ◽  
Input Variables ◽  
Using Data ◽  
Artificial Neural Network Ann

Flooding is a major problem globally, and especially in SuratThani province, Thailand. Along the lower Tapeeriver in SuratThani, the population density is high. Implementing an early warning system can benefit people living along the banks here. In this study, our aim was to build a flood prediction model using artificial neural network (ANN), which would utilize water and stream levels along the lower Tapeeriver to predict floods. This model was used to predict flood using a dataset of rainfall and stream levels measured at local stations. The developed flood prediction model consisted of 4 input variables, namely, the rainfall amounts and stream levels at stations located in the PhraSeang district (X.37A), the Khian Sa district (X.217), and in the Phunphin district (X.5C). Model performance was evaluated using input data spanning a period of eight years (2011–2018). The model performance was compared with support vector machine (SVM), and ANN had better accuracy. The results showed an accuracy of 97.91% for the ANN model; however, for SVM it was 97.54%. Furthermore, the recall (42.78%) and f-measure (52.24%) were better for our model, however, the precision was lower. Therefore, the designed flood prediction model can estimate the likelihood of floods around the lower Tapee river region

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