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合作机构同一篇文章里的其他机构

1. Thu Dau Mot University4
2. Institut Supérieur des Études Technologiques de Nabeul突尼斯4
3. Ton Duc Thang University越南3
4. Tarbiat Modares University伊朗2
5. Aligarh Muslim University印度2
6. Constantine the Philosopher University in Nitra斯洛伐克2
7. Duy Tan University越南2
8. Luleå University of Technology瑞典2

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逐年被引

最新文献

1. 1
   Influence of Frequency, Electric Field Strength, and Nonthermal Factors on the Inactivation of Escherichia coli O157 : H7 During the Ohmic Heating Processing of Pomelo JuiceJournal of Food Safety20252
2. 2
   An ensemble random forest tree with SVM, ANN, NBT, and LMT for landslide susceptibility mapping in the Rangit River watershed, IndiaNatural Hazards202266
3. 3
   Sentinel‐1 remote sensing data and Hydrologic Engineering Centres River Analysis System two‐dimensional integration for flash flood detection and modelling in New Cairo City, EgyptJournal of Flood Risk Management202150
4. 4
   Evaluation of various boosting ensemble algorithms for predicting flood hazard susceptibility areasGeomatics, Natural Hazards and Risk202142
5. 5
   Identification and characterization the sources of aerosols over Jharkhand state and surrounding areas, India using AHP modelGeomatics, Natural Hazards and Risk202123
6. 6
   A comparison among fuzzy multi-criteria decision making, bivariate, multivariate and machine learning models in landslide susceptibility mappingGeomatics, Natural Hazards and Risk2021149
7. 7
   Comparison of multi-criteria-analytical hierarchy process and machine learning-boosted tree models for regional flood susceptibility mapping: a case study from SlovakiaGeomatics, Natural Hazards and Risk202196
8. 8
   Head-cut gully erosion susceptibility modelling based on ensemble Random Forest with oblique decision trees in Fareghan watershed, IranGeomatics, Natural Hazards and Risk202024

高被引文献

1. 1
   A comparison among fuzzy multi-criteria decision making, bivariate, multivariate and machine learning models in landslide susceptibility mappingGeomatics, Natural Hazards and Risk2021149
2. 2
   Comparison of multi-criteria-analytical hierarchy process and machine learning-boosted tree models for regional flood susceptibility mapping: a case study from SlovakiaGeomatics, Natural Hazards and Risk202196
3. 3
   An ensemble random forest tree with SVM, ANN, NBT, and LMT for landslide susceptibility mapping in the Rangit River watershed, IndiaNatural Hazards202266
4. 4
   Sentinel‐1 remote sensing data and Hydrologic Engineering Centres River Analysis System two‐dimensional integration for flash flood detection and modelling in New Cairo City, EgyptJournal of Flood Risk Management202150
5. 5
   Evaluation of various boosting ensemble algorithms for predicting flood hazard susceptibility areasGeomatics, Natural Hazards and Risk202142
6. 6
   Head-cut gully erosion susceptibility modelling based on ensemble Random Forest with oblique decision trees in Fareghan watershed, IranGeomatics, Natural Hazards and Risk202024
7. 7
   Identification and characterization the sources of aerosols over Jharkhand state and surrounding areas, India using AHP modelGeomatics, Natural Hazards and Risk202123
8. 8
   Influence of Frequency, Electric Field Strength, and Nonthermal Factors on the Inactivation of Escherichia coli O157 : H7 During the Ohmic Heating Processing of Pomelo JuiceJournal of Food Safety20252

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