Matches in SemOpenAlex for { <https://semopenalex.org/work/W4200539666> ?p ?o ?g. }
- W4200539666 endingPage "108254" @default.
- W4200539666 startingPage "108254" @default.
- W4200539666 abstract "The robustness of landslide prediction models has become a major focus of researchers worldwide. We developed two novel hybrid predictive models that combine the self-organizing, deep-learning group method of data handling (GMDH) with two swarm intelligence optimization algorithms, i.e., cuckoo search algorithm (CSA) and whale optimization algorithm (WOA) for spatially explicit prediction of landslide susceptibility. Eleven landslide-causing factors and 334 historic landslides in a 31,340 km2 landslide-prone area in Iran were used to produce geospatial training and validation datasets. The GMDH model was employed to develop a basic predictive model that was then restructured and its parameters were optimized using the CSA and WOA algorithms, yielding the novel hybrid GMDH-CSA and GMDH-WOA models. The hybrid models were validated and compared to the standalone GMDH model by calculating the area under the receiver operating characteristic (AUC) curve and root mean square error (RMSE). The results demonstrated that the hybrid models overcame the computational shortcomings of the basic GMDH model and significantly improved landslide susceptibility prediction (GMDH-CSA, AUC = 0.909 and RMSE = 0.089; GMDH-WOA, AUC = 0.902 and RMSE = 0.129; standalone GMDH, AUC = 0.791 and RMSE = 0.226). Further, the hybrid models were more robust than the standalone GMDH model, showing consistently excellent performance when the training and validation datasets were changed. Overall, the swarm intelligence-optimized models, but not the standalone model, identified the best trade-offs among objectives, accuracy, and robustness." @default.
- W4200539666 created "2021-12-31" @default.
- W4200539666 creator A5000652481 @default.
- W4200539666 creator A5039567207 @default.
- W4200539666 creator A5042392421 @default.
- W4200539666 creator A5056706783 @default.
- W4200539666 creator A5059040421 @default.
- W4200539666 creator A5063537118 @default.
- W4200539666 creator A5076352077 @default.
- W4200539666 creator A5077439959 @default.
- W4200539666 creator A5091211368 @default.
- W4200539666 date "2022-02-01" @default.
- W4200539666 modified "2023-10-15" @default.
- W4200539666 title "Swarm intelligence optimization of the group method of data handling using the cuckoo search and whale optimization algorithms to model and predict landslides" @default.
- W4200539666 cites W1964002270 @default.
- W4200539666 cites W1974895230 @default.
- W4200539666 cites W1997564410 @default.
- W4200539666 cites W2022932590 @default.
- W4200539666 cites W2056258586 @default.
- W4200539666 cites W2063958435 @default.
- W4200539666 cites W2080134555 @default.
- W4200539666 cites W2108566668 @default.
- W4200539666 cites W2130089609 @default.
- W4200539666 cites W2147555471 @default.
- W4200539666 cites W2150913357 @default.
- W4200539666 cites W2157825442 @default.
- W4200539666 cites W2290883490 @default.
- W4200539666 cites W2343905117 @default.
- W4200539666 cites W2344071072 @default.
- W4200539666 cites W2604315272 @default.
- W4200539666 cites W2621028994 @default.
- W4200539666 cites W2785033958 @default.
- W4200539666 cites W2805076621 @default.
- W4200539666 cites W2888955873 @default.
- W4200539666 cites W2890029039 @default.
- W4200539666 cites W2890264245 @default.
- W4200539666 cites W2891027816 @default.
- W4200539666 cites W2900344777 @default.
- W4200539666 cites W2909188960 @default.
- W4200539666 cites W2916750663 @default.
- W4200539666 cites W2934708281 @default.
- W4200539666 cites W2945306155 @default.
- W4200539666 cites W2953075763 @default.
- W4200539666 cites W2955022329 @default.
- W4200539666 cites W2957420625 @default.
- W4200539666 cites W2968335386 @default.
- W4200539666 cites W2969141309 @default.
- W4200539666 cites W2971741962 @default.
- W4200539666 cites W2972082796 @default.
- W4200539666 cites W2972223586 @default.
- W4200539666 cites W2980595968 @default.
- W4200539666 cites W2980831575 @default.
- W4200539666 cites W2982365055 @default.
- W4200539666 cites W2991196721 @default.
- W4200539666 cites W2991273079 @default.
- W4200539666 cites W2993177448 @default.
- W4200539666 cites W2997037219 @default.
- W4200539666 cites W2997067188 @default.
- W4200539666 cites W2997942134 @default.
- W4200539666 cites W3000503970 @default.
- W4200539666 cites W3004173660 @default.
- W4200539666 cites W3004517429 @default.
- W4200539666 cites W3010735201 @default.
- W4200539666 cites W3016427037 @default.
- W4200539666 cites W3021008856 @default.
- W4200539666 cites W3033772434 @default.
- W4200539666 cites W3049651666 @default.
- W4200539666 cites W3107446066 @default.
- W4200539666 cites W3112585178 @default.
- W4200539666 cites W3119886363 @default.
- W4200539666 cites W3130537610 @default.
- W4200539666 cites W3170349234 @default.
- W4200539666 cites W3197949786 @default.
- W4200539666 cites W3198062544 @default.
- W4200539666 cites W3206918010 @default.
- W4200539666 cites W3216294106 @default.
- W4200539666 doi "https://doi.org/10.1016/j.asoc.2021.108254" @default.
- W4200539666 hasPublicationYear "2022" @default.
- W4200539666 type Work @default.
- W4200539666 citedByCount "23" @default.
- W4200539666 countsByYear W42005396662022 @default.
- W4200539666 countsByYear W42005396662023 @default.
- W4200539666 crossrefType "journal-article" @default.
- W4200539666 hasAuthorship W4200539666A5000652481 @default.
- W4200539666 hasAuthorship W4200539666A5039567207 @default.
- W4200539666 hasAuthorship W4200539666A5042392421 @default.
- W4200539666 hasAuthorship W4200539666A5056706783 @default.
- W4200539666 hasAuthorship W4200539666A5059040421 @default.
- W4200539666 hasAuthorship W4200539666A5063537118 @default.
- W4200539666 hasAuthorship W4200539666A5076352077 @default.
- W4200539666 hasAuthorship W4200539666A5077439959 @default.
- W4200539666 hasAuthorship W4200539666A5091211368 @default.
- W4200539666 hasConcept C104317684 @default.
- W4200539666 hasConcept C105795698 @default.
- W4200539666 hasConcept C11413529 @default.
- W4200539666 hasConcept C117241572 @default.
- W4200539666 hasConcept C119487961 @default.
- W4200539666 hasConcept C119857082 @default.