Pandey et al., 2021 - Google Patents
Flood susceptibility modeling in a subtropical humid low-relief alluvial plain environment: application of novel ensemble machine learning approachPandey et al., 2021
View HTML- Document ID
- 4929481534747351323
- Author
- Pandey M
- Arora A
- Arabameri A
- Costache R
- Kumar N
- Mishra V
- Nguyen H
- Mishra J
- Siddiqui M
- Ray Y
- Soni S
- Shukla U
- Publication year
- Publication venue
- Frontiers in Earth Science
External Links
Snippet
This study has developed a new ensemble model and tested another ensemble model for flood susceptibility mapping in the Middle Ganga Plain (MGP). The results of these two models have been quantitatively compared for performance analysis in zoning flood …
- 238000010801 machine learning 0 title abstract description 27
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10032—Satellite or aerial image; Remote sensing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/00624—Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
- G06K9/0063—Recognising patterns in remote scenes, e.g. aerial images, vegetation versus urban areas
- G06K9/00657—Recognising patterns in remote scenes, e.g. aerial images, vegetation versus urban areas of vegetation
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in preceding groups
- G01C21/26—Navigation; Navigational instruments not provided for in preceding groups specially adapted for navigation in a road network
- G01C21/28—Navigation; Navigational instruments not provided for in preceding groups specially adapted for navigation in a road network with correlation of data from several navigational instruments
- G01C21/30—Map- or contour-matching
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in preceding groups
- G01C21/20—Instruments for performing navigational calculations
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
- G01V9/00—Prospecting or detecting by methods not provided for in groups G01V1/00 - G01V8/00
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30241—Information retrieval; Database structures therefor; File system structures therefor in geographical information databases
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
- G06T17/05—Geographic models
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Pandey et al. | Flood susceptibility modeling in a subtropical humid low-relief alluvial plain environment: application of novel ensemble machine learning approach | |
Sahana et al. | A comparison of frequency ratio and fuzzy logic models for flood susceptibility assessment of the lower Kosi River Basin in India | |
Avand et al. | DEM resolution effects on machine learning performance for flood probability mapping | |
Shahri et al. | Landslide susceptibility hazard map in southwest Sweden using artificial neural network | |
Allafta et al. | GIS-based multi-criteria analysis for flood prone areas mapping in the trans-boundary Shatt Al-Arab basin, Iraq-Iran | |
Samanta et al. | Flood susceptibility mapping using geospatial frequency ratio technique: a case study of Subarnarekha River Basin, India | |
Gayen et al. | Application of weights-of-evidence (WoE) and evidential belief function (EBF) models for the delineation of soil erosion vulnerable zones: a study on Pathro river basin, Jharkhand, India | |
Rahmati et al. | Evaluating the influence of geo-environmental factors on gully erosion in a semi-arid region of Iran: An integrated framework | |
Zare et al. | Landslide susceptibility mapping at Vaz Watershed (Iran) using an artificial neural network model: a comparison between multilayer perceptron (MLP) and radial basic function (RBF) algorithms | |
Pourghasemi et al. | A comparative assessment of prediction capabilities of Dempster–Shafer and weights-of-evidence models in landslide susceptibility mapping using GIS | |
Cama et al. | Exploring relationships between grid cell size and accuracy for debris-flow susceptibility models: a test in the Giampilieri catchment (Sicily, Italy) | |
Franci et al. | Satellite remote sensing and GIS-based multi-criteria analysis for flood hazard mapping | |
Dou et al. | An integrated artificial neural network model for the landslide susceptibility assessment of Osado Island, Japan | |
Rabby et al. | Landslide susceptibility mapping in three Upazilas of Rangamati hill district Bangladesh: application and comparison of GIS-based machine learning methods | |
Hong et al. | Spatial prediction of landslide hazard at the Yihuang area (China) using two-class kernel logistic regression, alternating decision tree and support vector machines | |
Pradhan et al. | Landslide hazard and risk analyses at a landslide prone catchment area using statistical based geospatial model | |
Youssef et al. | Flood-hazard assessment modeling using multicriteria analysis and GIS: a case study—Ras Gharib Area, Egypt | |
Bui et al. | A novel hybrid evidential belief function-based fuzzy logic model in spatial prediction of rainfall-induced shallow landslides in the Lang Son city area (Vietnam) | |
Park et al. | Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea | |
Mukherjee et al. | Evaluation of topographic index in relation to terrain roughness and DEM grid spacing | |
Pourghasemi et al. | Assessment of urban infrastructures exposed to flood using susceptibility map and Google Earth Engine | |
Harmouzi et al. | Landslide susceptibility mapping of the Mediterranean coastal zone of Morocco between Oued Laou and El Jebha using artificial neural networks (ANN) | |
Mohammadi et al. | Flood Detection and Susceptibility Mapping Using Sentinel‐1 Time Series, Alternating Decision Trees, and Bag‐ADTree Models | |
Kachouri et al. | Soil erosion hazard mapping using Analytic Hierarchy Process and logistic regression: a case study of Haffouz watershed, central Tunisia | |
Lee et al. | Detection of landslides using web-based aerial photographs and landslide susceptibility mapping using geospatial analysis |