Flash-Flood Potential Mapping Using Deep Learning, Alternating Decision Trees and Data Provided by Remote Sensing Sensors Article Swipe
Romulus Costache
,
Alireza Arabameri
,
Thomas Blaschke
,
Quoc Bao Pham
,
Binh Thai Pham
,
Manish Pandey
,
Aman Arora
,
Nguyễn Thị Thùy Linh
,
Iulia Costache
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/s21010280
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/s21010280
There is an evident increase in the importance that remote sensing sensors play in the monitoring and evaluation of natural hazards susceptibility and risk. The present study aims to assess the flash-flood potential values, in a small catchment from Romania, using information provided remote sensing sensors and Geographic Informational Systems (GIS) databases which were involved as input data into a number of four ensemble models. In a first phase, with the help of high-resolution satellite images from the Google Earth application, 481 points affected by torrential processes were acquired, another 481 points being randomly positioned in areas without torrential processes. Seventy percent of the dataset was kept as training data, while the other 30% was assigned to validating sample. Further, in order to train the machine learning models, information regarding the 10 flash-flood predictors was extracted in the training sample locations. Finally, the following four ensembles were used to calculate the Flash-Flood Potential Index across the Bâsca Chiojdului river basin: Deep Learning Neural Network–Frequency Ratio (DLNN-FR), Deep Learning Neural Network–Weights of Evidence (DLNN-WOE), Alternating Decision Trees–Frequency Ratio (ADT-FR) and Alternating Decision Trees–Weights of Evidence (ADT-WOE). The model’s performances were assessed using several statistical metrics. Thus, in terms of Sensitivity, the highest value of 0.985 was achieved by the DLNN-FR model, meanwhile the lowest one (0.866) was assigned to ADT-FR ensemble. Moreover, the specificity analysis shows that the highest value (0.991) was attributed to DLNN-WOE algorithm, while the lowest value (0.892) was achieved by ADT-FR. During the training procedure, the models achieved overall accuracies between 0.878 (ADT-FR) and 0.985 (DLNN-WOE). K-index shows again that the most performant model was DLNN-WOE (0.97). The Flash-Flood Potential Index (FFPI) values revealed that the surfaces with high and very high flash-flood susceptibility cover between 46.57% (DLNN-FR) and 59.38% (ADT-FR) of the study zone. The use of the Receiver Operating Characteristic (ROC) curve for results validation highlights the fact that FFPIDLNN-WOE is characterized by the most precise results with an Area Under Curve of 0.96.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s21010280
- https://www.mdpi.com/1424-8220/21/1/280/pdf?version=1609747484
- OA Status
- gold
- Cited By
- 79
- References
- 83
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3120702290
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- OpenAlex ID
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https://openalex.org/W3120702290Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/s21010280Digital Object Identifier
- Title
-
Flash-Flood Potential Mapping Using Deep Learning, Alternating Decision Trees and Data Provided by Remote Sensing SensorsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-01-04Full publication date if available
- Authors
-
Romulus Costache, Alireza Arabameri, Thomas Blaschke, Quoc Bao Pham, Binh Thai Pham, Manish Pandey, Aman Arora, Nguyễn Thị Thùy Linh, Iulia CostacheList of authors in order
- Landing page
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https://doi.org/10.3390/s21010280Publisher landing page
- PDF URL
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https://www.mdpi.com/1424-8220/21/1/280/pdf?version=1609747484Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/1424-8220/21/1/280/pdf?version=1609747484Direct OA link when available
- Concepts
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Flash flood, Artificial neural network, Sample (material), Decision tree, Random forest, Flood myth, Remote sensing, Artificial intelligence, Computer science, Ensemble learning, Deep learning, Machine learning, Data mining, Environmental science, Cartography, Geography, Chemistry, Archaeology, ChromatographyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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79Total citation count in OpenAlex
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2025: 14, 2024: 14, 2023: 19, 2022: 22, 2021: 10Per-year citation counts (last 5 years)
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83Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.natural | 19 |
| abstract_inverted_index.overall | 251 |
| abstract_inverted_index.percent | 101 |
| abstract_inverted_index.precise | 320 |
| abstract_inverted_index.present | 25 |
| abstract_inverted_index.results | 308, 321 |
| abstract_inverted_index.sample. | 118 |
| abstract_inverted_index.sensing | 10, 44 |
| abstract_inverted_index.sensors | 11, 45 |
| abstract_inverted_index.several | 191 |
| abstract_inverted_index.values, | 33 |
| abstract_inverted_index.without | 97 |
| abstract_inverted_index.(ADT-FR) | 177, 255, 293 |
| abstract_inverted_index.DLNN-WOE | 233, 268 |
| abstract_inverted_index.Decision | 174, 180 |
| abstract_inverted_index.Evidence | 171, 183 |
| abstract_inverted_index.Finally, | 141 |
| abstract_inverted_index.Further, | 119 |
| abstract_inverted_index.Learning | 161, 167 |
| abstract_inverted_index.Receiver | 302 |
| abstract_inverted_index.Romania, | 39 |
| abstract_inverted_index.achieved | 205, 241, 250 |
| abstract_inverted_index.affected | 83 |
| abstract_inverted_index.analysis | 223 |
| abstract_inverted_index.assessed | 189 |
| abstract_inverted_index.assigned | 115, 216 |
| abstract_inverted_index.ensemble | 63 |
| abstract_inverted_index.increase | 4 |
| abstract_inverted_index.involved | 54 |
| abstract_inverted_index.learning | 126 |
| abstract_inverted_index.metrics. | 193 |
| abstract_inverted_index.provided | 42 |
| abstract_inverted_index.randomly | 93 |
| abstract_inverted_index.revealed | 276 |
| abstract_inverted_index.surfaces | 279 |
| abstract_inverted_index.training | 108, 138, 246 |
| abstract_inverted_index.(DLNN-FR) | 290 |
| abstract_inverted_index.Moreover, | 220 |
| abstract_inverted_index.Operating | 303 |
| abstract_inverted_index.Potential | 152, 272 |
| abstract_inverted_index.acquired, | 88 |
| abstract_inverted_index.calculate | 149 |
| abstract_inverted_index.catchment | 37 |
| abstract_inverted_index.databases | 51 |
| abstract_inverted_index.ensemble. | 219 |
| abstract_inverted_index.ensembles | 145 |
| abstract_inverted_index.extracted | 135 |
| abstract_inverted_index.following | 143 |
| abstract_inverted_index.meanwhile | 210 |
| abstract_inverted_index.model’s | 186 |
| abstract_inverted_index.potential | 32 |
| abstract_inverted_index.processes | 86 |
| abstract_inverted_index.regarding | 129 |
| abstract_inverted_index.satellite | 74 |
| abstract_inverted_index.(ADT-WOE). | 184 |
| abstract_inverted_index.(DLNN-FR), | 165 |
| abstract_inverted_index.Chiojdului | 157 |
| abstract_inverted_index.Geographic | 47 |
| abstract_inverted_index.accuracies | 252 |
| abstract_inverted_index.algorithm, | 234 |
| abstract_inverted_index.attributed | 231 |
| abstract_inverted_index.evaluation | 17 |
| abstract_inverted_index.highlights | 310 |
| abstract_inverted_index.importance | 7 |
| abstract_inverted_index.locations. | 140 |
| abstract_inverted_index.monitoring | 15 |
| abstract_inverted_index.performant | 265 |
| abstract_inverted_index.positioned | 94 |
| abstract_inverted_index.predictors | 133 |
| abstract_inverted_index.procedure, | 247 |
| abstract_inverted_index.processes. | 99 |
| abstract_inverted_index.torrential | 85, 98 |
| abstract_inverted_index.validating | 117 |
| abstract_inverted_index.validation | 309 |
| abstract_inverted_index.(DLNN-WOE), | 172 |
| abstract_inverted_index.(DLNN-WOE). | 258 |
| abstract_inverted_index.Alternating | 173, 179 |
| abstract_inverted_index.Flash-Flood | 151, 271 |
| abstract_inverted_index.flash-flood | 31, 132, 285 |
| abstract_inverted_index.information | 41, 128 |
| abstract_inverted_index.specificity | 222 |
| abstract_inverted_index.statistical | 192 |
| abstract_inverted_index.FFPIDLNN-WOE | 314 |
| abstract_inverted_index.Sensitivity, | 198 |
| abstract_inverted_index.application, | 80 |
| abstract_inverted_index.performances | 187 |
| abstract_inverted_index.Informational | 48 |
| abstract_inverted_index.characterized | 316 |
| abstract_inverted_index.Characteristic | 304 |
| abstract_inverted_index.susceptibility | 21, 286 |
| abstract_inverted_index.Trees–Weights | 181 |
| abstract_inverted_index.high-resolution | 73 |
| abstract_inverted_index.Network–Weights | 169 |
| abstract_inverted_index.Trees–Frequency | 175 |
| abstract_inverted_index.Network–Frequency | 163 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 99 |
| corresponding_author_ids | https://openalex.org/A5042425451, https://openalex.org/A5063208791 |
| countries_distinct_count | 5 |
| institutions_distinct_count | 9 |
| corresponding_institution_ids | https://openalex.org/I141445968, https://openalex.org/I1516879 |
| citation_normalized_percentile.value | 0.97758209 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |