Disaster prediction model based on support vector machine for regression and improved differential evolution

Yu, Xiaobing

doi:10.1007/s11069-016-2613-5

Cited by 19 publications

(9 citation statements)

References 46 publications

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“…However, recently, ML approaches have achieved fairly good results in various natural hazard susceptibility mapping studies. Some common ML approaches were applied in a wide range of studies in the field of wildfire modelling and susceptibility mapping such as an artificial neural network (ANN) [17], support vector machines (SVM) [18][19][20], and random forest (RF) [21,22], all three of which we evaluated in this study for the spatial prediction of wildfire susceptibility. We used the capabilities of these three ML approaches using sixteen wildfire conditioning factors based on five main factors-topographic, meteorological, anthropological, vegetation, and hydrological.…”

Section: Introductionmentioning

confidence: 99%

Spatial Prediction of Wildfire Susceptibility Using Field Survey GPS Data and Machine Learning Approaches

Ghorbanzadeh

Kamran

Blaschke

et al. 2019

Fire

122

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Recently, global climate change discussions have become more prominent, and forests are considered as the ecosystems most at risk by the consequences of climate change. Wildfires are among one of the main drivers leading to losses in forested areas. The increasing availability of free remotely sensed data has enabled the precise locations of wildfires to be reliably monitored. A wildfire data inventory was created by integrating global positioning system (GPS) polygons with data collected from the moderate resolution imaging spectroradiometer (MODIS) thermal anomalies product between 2012 and 2017 for Amol County, northern Iran. The GPS polygon dataset from the state wildlife organization was gathered through extensive field surveys. The integrated inventory dataset, along with sixteen conditioning factors (topographic, meteorological, vegetation, anthropological, and hydrological factors), was used to evaluate the potential of different machine learning (ML) approaches for the spatial prediction of wildfire susceptibility. The applied ML approaches included an artificial neural network (ANN), support vector machines (SVM), and random forest (RF). All ML approaches were trained using 75% of the wildfire inventory dataset and tested using the remaining 25% of the dataset in the four-fold cross-validation (CV) procedure. The CV method is used for dealing with the randomness effects of the training and testing dataset selection on the performance of applied ML approaches. To validate the resulting wildfire susceptibility maps based on three different ML approaches and four different folds of inventory datasets, the true positive and false positive rates were calculated. In the following, the accuracy of each of the twelve resulting maps was assessed through the receiver operating characteristics (ROC) curve. The resulting CV accuracies were 74%, 79% and 88% for the ANN, SVM and RF, respectively.

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Section: Introductionmentioning

confidence: 99%

Spatial Prediction of Wildfire Susceptibility Using Field Survey GPS Data and Machine Learning Approaches

Ghorbanzadeh

Kamran

Blaschke

et al. 2019

Fire

122

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“…The selection operation generates the next generation population by comparing the fitness values between the current evolutionary individuals and the trial individuals. Although it is simple and has few control parameters, DE has good convergence and has been successfully applied in many scientific and engineering fields (Cavalini et al, 2016;Dhaliwal, 2017;Panigrahi, 2017;Yu, 2017;Zhao et al, 2015). The operation process about the standard differential evolution will be described as follows.…”

Section: Differential Evolutionmentioning

confidence: 99%

Application of an Encoding Revision Algorithm in Overlapping Coalition Formation

Gui

Zhao

et al. 2021

International Journal of Cognitive Informatics and Natural Intelligence

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Overlapping coalition formation is a very active research field in multi-agent systems (MAS). In overlapping coalition, each agent can participate in different coalitions corresponding to multiple tasks at the same time. As each agent has limited resources, resource conflicts will occur. In order to resolve resource conflicts, we develop an improved encoding revision algorithm in this paper which can revise an invalid two-dimensional binary encoding into a valid one by checking the encoding for each row. To verify the effectiveness of the algorithm, differential evolution was used as the experimental platform and compared with Zhang et al. The experimental results show that the algorithm in this paper is superior to Zhang et al. in both solution quality and encoding revision time.

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“…represent mutually exclusive indices [39,40]. F shows the mutation scale factor that controls the amplification of DE [36].…”

Section: Mutationmentioning

confidence: 99%

Prediction of drug synergy score using ensemble based differential evolution

Singh

Rana

Singh

2019

IET Systems Biology

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Prediction of drug synergy score is an ill-posed problem. It plays an efficient role in the medical field for inhibiting specific cancer agents. An efficient regression-based machine learning technique has an ability to minimise the drug synergy prediction errors. Therefore, in this study, an efficient machine learning technique for drug synergy prediction technique is designed by using ensemble based differential evolution (DE) for optimising the support vector machine (SVM). Because the tuning of the attributes of SVM kernel regulates the prediction precision. The ensemble based DE employs two trial vector generation techniques and two control attributes settings. The initial generation technique has the best solution and the other is without the best solution. The proposed and existing competitive machine learning techniques are applied to drug synergy data. The extensive analysis demonstrates that the proposed technique outperforms others in terms of accuracy, root mean square error and coefficient of correlation.

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Disaster prediction model based on support vector machine for regression and improved differential evolution

Cited by 19 publications

References 46 publications

Spatial Prediction of Wildfire Susceptibility Using Field Survey GPS Data and Machine Learning Approaches

Spatial Prediction of Wildfire Susceptibility Using Field Survey GPS Data and Machine Learning Approaches

Application of an Encoding Revision Algorithm in Overlapping Coalition Formation

Prediction of drug synergy score using ensemble based differential evolution

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