| Página principal > Artículos > Detection of land subsidence using hybrid and ensemble deep learning models > MARC |
000136340 001__ 136340 000136340 005__ 20250908131433.0 000136340 0247_ $$2doi$$a10.1007/s12518-024-00572-9 000136340 0248_ $$2sideral$$a139250 000136340 037__ $$aART-2024-139250 000136340 041__ $$aeng 000136340 100__ $$aKariminejad, Narges 000136340 245__ $$aDetection of land subsidence using hybrid and ensemble deep learning models 000136340 260__ $$c2024 000136340 5203_ $$aLand subsidence (LS) is among the most prominent forms of subsurface erosion and geomorphological hazards. This study used two deep learning (DL) models consisting of the hybrid CNN-RNN and ensemble DL (EDL) merged with two dense models. The main variables controlling LS (consisting of environmental, hydrological, hydrogeological, digital elevation model, and soil characteristics), were used as the input for the predictive DL models. Likewise, to establish the degree of performance of each parameter, different control points have been established. We then trained and tested our DL models using the receiver-operating characteristic-area under curve (ROC-AUC) and precision-recall plots. The measures based on the game theory consisting of permutation feature importance measure (PFIM) and SHapley Additive exPlanations (SHAP) were employed to assess the features relative importance and interpretability of the predictive model output. Our findings show that the ensemble CNN-RNN model performed well with the ROC-AUC curve (0.95) of class 1 (land subsidence) for training data for detecting and mapping land subsidence compared to EDL with the ROC curve (0.93) of class 1 (land subsidence) for training datasets. The CNN-RNN also performed well with the precision-recall curve (0.954) of class 1 for testing data for detecting and mapping land subsidence compared to the EDL model with the precision-recall curve (0.95) of class 1. The results of this research revealed that much of the study area is susceptible to land subsidence. The results of the model sensitivity analysis suggested that the groundwater drop rate is the most sensitive for the model. Based on the SHAP values, the groundwater drop rate was identified as the most contributed feature to the model output. The importance of this study is at a broader level, especially in arid and semiarid environments with similar geomorphological, and climatic conditions. 000136340 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/ 000136340 592__ $$a0.523$$b2024 000136340 593__ $$aEarth and Planetary Sciences (miscellaneous)$$c2024$$dQ2 000136340 593__ $$aGeography, Planning and Development$$c2024$$dQ2 000136340 593__ $$aEnvironmental Science (miscellaneous)$$c2024$$dQ2 000136340 593__ $$aEngineering (miscellaneous)$$c2024$$dQ2 000136340 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion 000136340 700__ $$aMohammadifar, Aliakbar 000136340 700__ $$aSepehr, Adel 000136340 700__ $$aGarajeh, Mohammad Kazemi 000136340 700__ $$aRezaei, Mahrooz 000136340 700__ $$0(orcid)0000-0001-8949-5676$$aDesir, Gloria$$uUniversidad de Zaragoza 000136340 700__ $$aQuesada-Román, Adolfo 000136340 700__ $$aGholami, Hamid 000136340 7102_ $$12000$$2427$$aUniversidad de Zaragoza$$bDpto. Ciencias de la Tierra$$cÁrea Geodinámica Externa 000136340 773__ $$g16 (2024), 593–610$$pAppl. geomat.$$tApplied geomatics$$x1866-9298 000136340 8564_ $$s5051321$$uhttps://zaguan.unizar.es/record/136340/files/texto_completo.pdf$$yVersión publicada 000136340 8564_ $$s2307385$$uhttps://zaguan.unizar.es/record/136340/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada 000136340 909CO $$ooai:zaguan.unizar.es:136340$$particulos$$pdriver 000136340 951__ $$a2025-09-08-12:56:45 000136340 980__ $$aARTICLE
El sistema ha encontrado un error mientras gestionaba su petición.
Los administradores del sistema han sido avisados.
En caso de duda, póngase en contacto con deposita@unizar.es