Zobrazeno 1 - 10
of 120
pro vyhledávání: '"Haoyuan, Hong"'
Autor:
Haoyuan Hong
Publikováno v:
Ecological Indicators, Vol 147, Iss , Pp 109968- (2023)
Landslide susceptibility mapping is a meaningful method to avoid and reduce the loss from landslide hazard. The main goal of current paper is to propose a hybrid model method to explore the effect of combining the Best-first decision tree (BFT) model
Externí odkaz:
https://doaj.org/article/6d1bd38da9374e82908ada75d66f4c10
Autor:
Paraskevas Tsangaratos, Ioanna Ilia, Aikaterini-Alexandra Chrysafi, Ioannis Matiatos, Wei Chen, Haoyuan Hong
Publikováno v:
Remote Sensing, Vol 15, Iss 14, p 3471 (2023)
The main scope of the study is to evaluate the prognostic accuracy of a one-dimensional convolutional neural network model (1D-CNN), in flood susceptibility assessment, in a selected test site on the island of Euboea, Greece. Logistic regression (LR)
Externí odkaz:
https://doaj.org/article/2ebaa199b4c04e8c8419718f29d03cc9
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 13, Pp 4642-4662 (2020)
Ensemble learning methods have been widely used due to their remarkable generalized performance, but their potential in landslide spatial prediction application is not fully studied. To take full advantage of ensemble learning techniques, the classif
Externí odkaz:
https://doaj.org/article/d90909fc663e47849f11ab37a18ac7b6
Publikováno v:
Frontiers in Earth Science, Vol 9 (2021)
For the issue of collapse susceptibility prediction (CSP), minimal attention has been paid to explore the uncertainty characteristics of different machine learning models predicting collapse susceptibility. In this study, six kinds of typical machine
Externí odkaz:
https://doaj.org/article/9b80f0d1596745388aa8d03c981eb747
Publikováno v:
Geomatics, Natural Hazards & Risk, Vol 10, Iss 1, Pp 1-25 (2019)
A landslide susceptibility map, which describes the quantitative relationship between known landslides and control factors, is essential to link the theoretical prediction with practical disaster reduction measures. In this work, the artificial neura
Externí odkaz:
https://doaj.org/article/af6d75378b274d3e8ddac7aec15f6ba0
Autor:
Wei Chen, Ataollah Shirzadi, Himan Shahabi, Baharin Bin Ahmad, Shuai Zhang, Haoyuan Hong, Ning Zhang
Publikováno v:
Geomatics, Natural Hazards & Risk, Vol 8, Iss 2, Pp 1955-1977 (2017)
The main objective of this study was to produce landslide susceptibility maps for Langao County, China, using a novel hybrid artificial intelligence method based on rotation forest ensembles (RFEs) and naïve Bayes tree (NBT) classifiers labeled the
Externí odkaz:
https://doaj.org/article/4affa5f7eebe43bc8b36387e707ee861
Publikováno v:
Geomatics, Natural Hazards & Risk, Vol 8, Iss 2, Pp 544-569 (2017)
Suichuan is a mountainous area at the Jiangxi province in Central China, where rainfall-induced landslides occur frequently. The purpose of this study is to assess landslide susceptibility of this region using support vector machine (SVM) with four k
Externí odkaz:
https://doaj.org/article/d3b3b40b9baf48f5a77cdd4ba33bfff0
Autor:
Mahyat Shafapour Tehrany, Farzin Shabani, Mustafa Neamah Jebur, Haoyuan Hong, Wei Chen, Xiaoshen Xie
Publikováno v:
Geomatics, Natural Hazards & Risk, Vol 8, Iss 2, Pp 1538-1561 (2017)
The aim of this research was to evaluate the predictive performances of frequency ratio (FR), logistic regression (LR) and weight of evidence (WoE), in flood susceptibility mapping in China. In addition, the ensemble WoE and LR and ensemble FR and LR
Externí odkaz:
https://doaj.org/article/d315a5c163ff48f89536f55c088a2972
Publikováno v:
Geomatics, Natural Hazards & Risk, Vol 8, Iss 2, Pp 950-973 (2017)
The main purpose of this paper is to explore some potential applications of sophisticated machine learning techniques such as the kernel logistic regression, Naïve-Bayes tree and alternating decision tree models for landslide susceptibility analysis
Externí odkaz:
https://doaj.org/article/d6860dcae07b4576960a9fc107c696dc
Publikováno v:
Geomatics, Natural Hazards & Risk, Vol 8, Iss 2, Pp 1997-2022 (2017)
This study evaluated the geographically weighted regression (GWR) model for landslide susceptibility mapping in Xing Guo County, China. In this study, 16 conditioning factors, such as slope, aspect, altitude, topographic wetness index, stream power i
Externí odkaz:
https://doaj.org/article/27a0bd78e93f48c888171a970470e192