Utility of Certain AI Models in Climate-Induced Disasters Article Swipe
Ritusnata Mishra
,
Sanjeev Kumar
,
Himangshu Sarkar
,
C. S. P. Ojha
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/world5040045
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/world5040045
To address the current challenge of climate change at the local and global levels, this article discusses a few important water resources engineering topics, such as estimating the energy dissipation of flowing waters over hilly areas through the provision of regulated stepped channels, predicting the removal of silt deposition in the irrigation canal, and predicting groundwater level. Artificial intelligence (AI) in water resource engineering is now one of the most active study topics. As a result, multiple AI tools such as Random Forest (RF), Random Tree (RT), M5P (M5 model trees), M5Rules, Feed-Forward Neural Networks (FFNNs), Gradient Boosting Machine (GBM), Adaptive Boosting (AdaBoost), and Support Vector Machines kernel-based model (SVM-Pearson VII Universal Kernel, Radial Basis Function) are tested in the present study using various combinations of datasets. However, in various circumstances, including predicting energy dissipation of stepped channels and silt deposition in rivers, AI techniques outperformed the traditional approach in the literature. Out of all the models, the GBM model performed better than other AI tools in both the field of energy dissipation of stepped channels with a coefficient of determination (R2) of 0.998, root mean square error (RMSE) of 0.00182, and mean absolute error (MAE) of 0.0016 and sediment trapping efficiency of vortex tube ejector with an R2 of 0.997, RMSE of 0.769, and MAE of 0.531 during testing. On the other hand, the AI technique could not adequately understand the diversity in groundwater level datasets using field data from various stations. According to the current study, the AI tool works well in some fields of water resource engineering, but it has difficulty in other domains in capturing the diversity of datasets.
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Metadata
- Type
- article
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- en
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- https://doi.org/10.3390/world5040045
- https://www.mdpi.com/2673-4060/5/4/45/pdf?version=1728531405
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https://doi.org/10.3390/world5040045Digital Object Identifier
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Utility of Certain AI Models in Climate-Induced DisastersWork title
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articleOpenAlex work type
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enPrimary language
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2024Year of publication
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2024-10-08Full publication date if available
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Ritusnata Mishra, Sanjeev Kumar, Himangshu Sarkar, C. S. P. OjhaList of authors in order
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https://doi.org/10.3390/world5040045Publisher landing page
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https://www.mdpi.com/2673-4060/5/4/45/pdf?version=1728531405Direct link to full text PDF
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2673-4060/5/4/45/pdf?version=1728531405Direct OA link when available
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Climate change, Environmental science, Climatology, Computer science, Geology, OceanographyTop concepts (fields/topics) attached by OpenAlex
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| abstract_inverted_index.data | 239 |
| abstract_inverted_index.from | 240 |
| abstract_inverted_index.mean | 185, 192 |
| abstract_inverted_index.most | 69 |
| abstract_inverted_index.over | 33 |
| abstract_inverted_index.root | 184 |
| abstract_inverted_index.silt | 47, 139 |
| abstract_inverted_index.some | 254 |
| abstract_inverted_index.such | 24, 79 |
| abstract_inverted_index.than | 162 |
| abstract_inverted_index.this | 14 |
| abstract_inverted_index.tool | 250 |
| abstract_inverted_index.tube | 204 |
| abstract_inverted_index.well | 252 |
| abstract_inverted_index.with | 176, 206 |
| abstract_inverted_index.(MAE) | 195 |
| abstract_inverted_index.(RF), | 83 |
| abstract_inverted_index.(RT), | 86 |
| abstract_inverted_index.0.531 | 217 |
| abstract_inverted_index.Basis | 114 |
| abstract_inverted_index.areas | 35 |
| abstract_inverted_index.could | 227 |
| abstract_inverted_index.error | 187, 194 |
| abstract_inverted_index.field | 169, 238 |
| abstract_inverted_index.hand, | 223 |
| abstract_inverted_index.hilly | 34 |
| abstract_inverted_index.level | 235 |
| abstract_inverted_index.local | 10 |
| abstract_inverted_index.model | 89, 108, 159 |
| abstract_inverted_index.other | 163, 222, 265 |
| abstract_inverted_index.study | 71, 121 |
| abstract_inverted_index.tools | 78, 165 |
| abstract_inverted_index.using | 122, 237 |
| abstract_inverted_index.water | 20, 61, 257 |
| abstract_inverted_index.works | 251 |
| abstract_inverted_index.(GBM), | 99 |
| abstract_inverted_index.(RMSE) | 188 |
| abstract_inverted_index.0.0016 | 197 |
| abstract_inverted_index.0.769, | 213 |
| abstract_inverted_index.0.997, | 210 |
| abstract_inverted_index.0.998, | 183 |
| abstract_inverted_index.Forest | 82 |
| abstract_inverted_index.Neural | 93 |
| abstract_inverted_index.Radial | 113 |
| abstract_inverted_index.Random | 81, 84 |
| abstract_inverted_index.Vector | 105 |
| abstract_inverted_index.active | 70 |
| abstract_inverted_index.better | 161 |
| abstract_inverted_index.canal, | 52 |
| abstract_inverted_index.change | 7 |
| abstract_inverted_index.during | 218 |
| abstract_inverted_index.energy | 28, 133, 171 |
| abstract_inverted_index.fields | 255 |
| abstract_inverted_index.global | 12 |
| abstract_inverted_index.level. | 56 |
| abstract_inverted_index.square | 186 |
| abstract_inverted_index.study, | 247 |
| abstract_inverted_index.tested | 117 |
| abstract_inverted_index.vortex | 203 |
| abstract_inverted_index.waters | 32 |
| abstract_inverted_index.Kernel, | 112 |
| abstract_inverted_index.Machine | 98 |
| abstract_inverted_index.Support | 104 |
| abstract_inverted_index.address | 1 |
| abstract_inverted_index.article | 15 |
| abstract_inverted_index.climate | 6 |
| abstract_inverted_index.current | 3, 246 |
| abstract_inverted_index.domains | 266 |
| abstract_inverted_index.ejector | 205 |
| abstract_inverted_index.flowing | 31 |
| abstract_inverted_index.levels, | 13 |
| abstract_inverted_index.models, | 156 |
| abstract_inverted_index.present | 120 |
| abstract_inverted_index.removal | 45 |
| abstract_inverted_index.result, | 75 |
| abstract_inverted_index.rivers, | 142 |
| abstract_inverted_index.stepped | 41, 136, 174 |
| abstract_inverted_index.through | 36 |
| abstract_inverted_index.topics, | 23 |
| abstract_inverted_index.topics. | 72 |
| abstract_inverted_index.trees), | 90 |
| abstract_inverted_index.various | 123, 129, 241 |
| abstract_inverted_index.(FFNNs), | 95 |
| abstract_inverted_index.0.00182, | 190 |
| abstract_inverted_index.Adaptive | 100 |
| abstract_inverted_index.Boosting | 97, 101 |
| abstract_inverted_index.Gradient | 96 |
| abstract_inverted_index.However, | 127 |
| abstract_inverted_index.M5Rules, | 91 |
| abstract_inverted_index.Machines | 106 |
| abstract_inverted_index.Networks | 94 |
| abstract_inverted_index.absolute | 193 |
| abstract_inverted_index.approach | 148 |
| abstract_inverted_index.channels | 137, 175 |
| abstract_inverted_index.datasets | 236 |
| abstract_inverted_index.multiple | 76 |
| abstract_inverted_index.resource | 62, 258 |
| abstract_inverted_index.sediment | 199 |
| abstract_inverted_index.testing. | 219 |
| abstract_inverted_index.trapping | 200 |
| abstract_inverted_index.According | 243 |
| abstract_inverted_index.Function) | 115 |
| abstract_inverted_index.Universal | 111 |
| abstract_inverted_index.capturing | 268 |
| abstract_inverted_index.challenge | 4 |
| abstract_inverted_index.channels, | 42 |
| abstract_inverted_index.datasets. | 126, 272 |
| abstract_inverted_index.discusses | 16 |
| abstract_inverted_index.diversity | 232, 270 |
| abstract_inverted_index.important | 19 |
| abstract_inverted_index.including | 131 |
| abstract_inverted_index.performed | 160 |
| abstract_inverted_index.provision | 38 |
| abstract_inverted_index.regulated | 40 |
| abstract_inverted_index.resources | 21 |
| abstract_inverted_index.stations. | 242 |
| abstract_inverted_index.technique | 226 |
| abstract_inverted_index.Artificial | 57 |
| abstract_inverted_index.adequately | 229 |
| abstract_inverted_index.deposition | 48, 140 |
| abstract_inverted_index.difficulty | 263 |
| abstract_inverted_index.efficiency | 201 |
| abstract_inverted_index.estimating | 26 |
| abstract_inverted_index.irrigation | 51 |
| abstract_inverted_index.predicting | 43, 54, 132 |
| abstract_inverted_index.techniques | 144 |
| abstract_inverted_index.understand | 230 |
| abstract_inverted_index.(AdaBoost), | 102 |
| abstract_inverted_index.coefficient | 178 |
| abstract_inverted_index.dissipation | 29, 134, 172 |
| abstract_inverted_index.engineering | 22, 63 |
| abstract_inverted_index.groundwater | 55, 234 |
| abstract_inverted_index.literature. | 151 |
| abstract_inverted_index.traditional | 147 |
| abstract_inverted_index.(SVM-Pearson | 109 |
| abstract_inverted_index.Feed-Forward | 92 |
| abstract_inverted_index.combinations | 124 |
| abstract_inverted_index.engineering, | 259 |
| abstract_inverted_index.intelligence | 58 |
| abstract_inverted_index.kernel-based | 107 |
| abstract_inverted_index.outperformed | 145 |
| abstract_inverted_index.determination | 180 |
| abstract_inverted_index.circumstances, | 130 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| corresponding_author_ids | https://openalex.org/A5113019213 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| corresponding_institution_ids | https://openalex.org/I154851008 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/6 |
| sustainable_development_goals[0].score | 0.6499999761581421 |
| sustainable_development_goals[0].display_name | Clean water and sanitation |
| citation_normalized_percentile.value | 0.71701082 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |