127 citations as recorded by crossref.

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48. Drought Severity and Frequency Analysis Aided by Spectral and Meteorological Indices in the Kurdistan Region of Iraq H. Gaznayee et al. https://doi.org/10.3390/w14193024
49. Enhancing seismic activity classification in augmented soil gas radon time series data through computational intelligence techniques N. Bashir et al. https://doi.org/10.1016/j.jastp.2025.106560
50. A computationally efficient system for assessing near-real-time instability of regional unsaturated soil slopes under rainfall L. Li et al. https://doi.org/10.1007/s10346-019-01307-3
51. Employing a genetic algorithm and grey wolf optimizer for optimizing RF models to evaluate soil liquefaction potential J. Zhou et al. https://doi.org/10.1007/s10462-022-10140-5
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54. Multi-objective feature selection (MOFS) algorithms for prediction of liquefaction susceptibility of soil based on in situ test methods S. Das et al. https://doi.org/10.1007/s11069-020-04089-3
55. A new approach for modeling of flow number of asphalt mixtures A. Alavi et al. https://doi.org/10.1016/j.acme.2016.06.004
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58. Large, deepwater slope failures: Implications for landslide-generated tsunamis C. Lo Iacono et al. https://doi.org/10.1130/G33446.1
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61. A novel approach for assessment of seismic induced liquefaction susceptibility of soil D. Kumar et al. https://doi.org/10.1007/s12040-024-02341-z
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69. Combining spatial autocorrelation with artificial intelligence models to estimate spatial distribution and risks of heavy metal pollution in agricultural soils E. Günal et al. https://doi.org/10.1007/s10661-022-10813-2
70. ESTIMATION OF SOIL LIQUEFACTION POTENTIAL IN THE DALAMAN RESIDENTIAL AREA USING A SUPERVISED MACHINE LEARNING MODEL O. Türe & E. Karacan https://doi.org/10.22531/muglajsci.1602580
71. Liq-EBM: data-driven assessment of liquefaction triggering and associated ground damage Y. Hwang et al. https://doi.org/10.1007/s11069-026-08100-1
72. Engineering-Geological Features Supporting a Seismic-Driven Multi-Hazard Scenario in the Lake Campotosto Area (L’Aquila, Italy) B. Antonielli et al. https://doi.org/10.3390/geosciences11030107
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74. Kernel-based machine learning model for liquefaction probability prediction and its application to the seismically active indo-gangetic plain S. Ghani https://doi.org/10.1007/s12145-025-01790-4
75. Coastal vulnerability assessment based on video wave run-up observations at a mesotidal, steep-sloped beach M. Vousdoukas et al. https://doi.org/10.1007/s10236-011-0480-x
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