Mapping of landslide susceptibility using the combination of neuro-fuzzy inference system (ANFIS), ant colony (ANFIS-ACOR), and differential evolution (ANFIS-DE) models

Razavi-Termeh, Seyed Vahid; Shirani, Kourosh; Pasandi, Mehrdad

doi:10.1007/s10064-020-02048-7

Cited by 40 publications

(13 citation statements)

References 93 publications

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“…There are sensitivity axes ( x -axis) and a transparency axis ( y -axis) in the ROC curve. The x -axis and the y -axis for the ROC curve are calculated using Equations (11) and (12) [ 47 , 48 ].

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Section: Methodsmentioning

confidence: 99%

COVID-19 Risk Mapping with Considering Socio-Economic Criteria Using Machine Learning Algorithms

Razavi-Termeh

Sadeghi‐Niaraki

Farhangi

et al. 2021

IJERPH

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The reduction of population concentration in some urban land uses is one way to prevent and reduce the spread of COVID-19 disease. Therefore, the objective of this study is to prepare the risk mapping of COVID-19 in Tehran, Iran, using machine learning algorithms according to socio-economic criteria of land use. Initially, a spatial database was created using 2282 locations of patients with COVID-19 from 2 February 2020 to 21 March 2020 and eight socio-economic land uses affecting the disease—public transport stations, supermarkets, banks, automated teller machines (ATMs), bakeries, pharmacies, fuel stations, and hospitals. The modeling was performed using three machine learning algorithms that included random forest (RF), adaptive neuro-fuzzy inference system (ANFIS), and logistic regression (LR). Feature selection was performed using the OneR method, and the correlation between land uses was obtained using the Pearson coefficient. We deployed 70% and 30% of COVID-19 patient locations for modeling and validation, respectively. The results of the receiver operating characteristic (ROC) curve and the area under the curve (AUC) showed that the RF algorithm, which had a value of 0.803, had the highest modeling accuracy, which was followed by the ANFIS algorithm with a value of 0.758 and the LR algorithm with a value of 0.747. The results showed that the central and the eastern regions of Tehran are more at risk. Public transportation stations and pharmacies were the most correlated with the location of COVID-19 patients in Tehran, according to the results of the OneR technique, RF, and LR algorithms. The results of the Pearson correlation showed that pharmacies and banks are the most incompatible in distribution, and the density of these land uses in Tehran has caused the prevalence of COVID-19.

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…”

Section: Methodsmentioning

confidence: 99%

COVID-19 Risk Mapping with Considering Socio-Economic Criteria Using Machine Learning Algorithms

Razavi-Termeh

Sadeghi‐Niaraki

Farhangi

et al. 2021

IJERPH

Self Cite

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“…By employing different number of inputs and membership functions, twelve models were designed and analyzed. The data set which includes the monthly number of tourist arrivals in Cyprus over 60 months was divided into the training data set (70%) and testing data set (30%) following other studies [32][33][34]. The training datasets were used to build the models, and the testing datasets were used to validate the models.…”

Section: Numerical Example and Resultsmentioning

confidence: 99%

Employing a Time Series Forecasting Model for Tourism Demand Using ANFIS

Salehi¹

2022

JIOS

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Forecasting the future trends is of utmost importance for managers and decision makers in different sectors. Scholars thus have introduced various techniques to the service industry aiming at employing a prediction model with ultimate accuracy and high efficiency. The literature proves that adaptive neuro-fuzzy inference systems (ANFIS) are the most efficiency models. However, the literature lacks reports on how ANFIS parameters may affect the accuracy of the system. Employing tourist arrival records to Cyprus between 2015 and 2019, this study has developed an ANFIS system to evaluate the accuracy performance of different prediction models with varied number of inputs and number or type of membership functions. Results show that the forecasting accuracy of a model with four inputs and four membership functions when the type of membership functions is Gaussian is relatively better than other models. In other words, it can be concluded that the forecast model with four inputs and four Gaussian membership functions is ultimate with the most accurate prediction record with reference to MAE, RMSE, and MAPE. The results of this study may be significant for senior managers and decision-makers of the tourism industry.

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“…In each cycle, an ant makes a series of moves between web pages until it finds the target resource and returns to the source point. After each crawling cycle, the ant colony updated the pheromone quantity on each path [16].…”

Section: Collection Of College English Teaching Information Resourcesmentioning

confidence: 99%

Construction of College English Teaching Information Resource Integration Model Based on Fuzzy Clustering Algorithm

Hua

2022

Mobile Information Systems

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The existing college English teaching information resource integration methods have problems of low recall rate, low accuracy rate, and long integration time. Therefore, this paper constructs a college English teaching information resource integration model based on fuzzy clustering algorithm. The ant colony algorithm is used to optimize the topic crawler, the optimized topic crawler is used to collect the college English teaching information resources in the mixed teaching mode, and the weight and naive Bayes algorithm are combined to classify the collected resources. According to the results of resource classification, fuzzy clustering algorithm is used to construct the integration model of college English teaching information resources. The experimental results show that the recall rate of the proposed model is more than 95%, the accuracy rate is more than 94%, and the average resource integration time is only 0.71 s, indicating that the model has relatively reliable performance.

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Mapping of landslide susceptibility using the combination of neuro-fuzzy inference system (ANFIS), ant colony (ANFIS-ACOR), and differential evolution (ANFIS-DE) models

Cited by 40 publications

References 93 publications

COVID-19 Risk Mapping with Considering Socio-Economic Criteria Using Machine Learning Algorithms

COVID-19 Risk Mapping with Considering Socio-Economic Criteria Using Machine Learning Algorithms

Employing a Time Series Forecasting Model for Tourism Demand Using ANFIS

Construction of College English Teaching Information Resource Integration Model Based on Fuzzy Clustering Algorithm

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