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Regional flood frequency analysis using data-driven models (M5, random forest, and ANFIS) and a multivariate regression method in ungauged catchments
Flooding is recognized worldwide joined of the most expensive natural hazards. To adopt proper structural and nonstructural measurements for...
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Modelling soil stability in wide tunnels using FELA and multivariate adaptive regression splines analysis
Stability evaluations of soil or rock excavation are significantly affected by the shape of the underground cavity. Whilst most of the previous...
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Prediction of blast-induced dust emissions in surface mines using integration of dimensional analysis and multivariate regression analysis
Dust is one of the most significant challenges in the mining industry, which adversely impacts the environment and human health. This research...
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Salinity analysis based on multivariate nonlinear regression for web‐based visualization of oceanic data
Traditionally, temperature-salinity (T-S) relationship was analysed to indicate the characteristic of water mass, and prediction models based on...
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Forecasting Surface Facilities Investment Based on Factor Analysis and Multiple Regression Analysis
Surface facilities investment is a critical component of engineering investment estimation, which holds a relatively significant proportion of the...
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Modeling of kappa factor using multivariate adaptive regression splines: application to the western Türkiye ground motion dataset
The recent seismic activity on Türkiye’s west coast, especially in the Aegean Sea region, shows that this region requires further attention. The...
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Bayesian Robust Multivariate Time Series Analysis in Nonlinear Regression Models with Vector Autoregressive and t-Distributed Errors
Geodetic measurements rely on high-resolution sensors, but produce data sets with many observations which may contain outliers and correlated...
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Understanding groundwater mineralization controls and the implications on its quality (Southwestern Ghana): insights from hydrochemistry, multivariate statistics, and multi-linear regression models
In Southwestern Ghana, groundwater is highly needed for several uses, including drinking, domestic, agricultural, and socioeconomic activities....
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A hybrid intelligent prediction model of autoencoder neural network and multivariate adaptive regression spline for uniaxial compressive strength of rocks
In geomechanics, the determination of uniaxial compressive strength (UCS) from typical laboratory procedures is a challenging and time-consuming...
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A Rapid Design Procedure for Tied-Back Soil Walls Using Multivariate Adaptive Regression Splines (MARS) Method
Deep excavations are widespread geotechnical structures in urban areas. Vertical deep excavations may cause irreparable harms to the adjacent...
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Slope reliability analysis in spatially variable soils using sliced inverse regression-based multivariate adaptive regression spline
Reliability analysis of slope considering the spatial variability of soil properties may be subjected to the curse of high dimensionality, which...
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Rainfall-Runoff Simulation in Ungauged Tributary Streams Using Drainage Area Ratio-Based Multivariate Adaptive Regression Spline and Random Forest Hybrid Models
For various reasons, it is not always possible to obtain adequate and reliable long-term streamflow records in a river basin. It is known that...
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Multivariate Statistics
Multivariate analysis is used to understand and describe the relationships between an arbitrary number of variables. Earth scientists often deal with...
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Comparison of artificial intelligence and multivariate regression methods in predicting the uniaxial compressive strength of rock during the specific resistivity monitoring
Uniaxial compressive strength is one of the most important mechanical characteristics of rocks, and its prediction is essential in most rock...
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Multivariate Statistik
Die multivariate Analyse wird verwendet, um die Beziehungen zwischen einer beliebigen Anzahl von Variablen zu verstehen und zu beschreiben....
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A data-driven method for predicting debris-flow runout zones by integrating multivariate adaptive regression splines and Akaike information criterion
Debris-flow runout zones are important parameters for delineation of endangered areas and design of mitigation works. It is necessary to properly...
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Probabilistic Analysis of Passive Trapdoor in c-ϕ Soil Considering Multivariate Cross-Correlated Random Fields
This study investigates the effect of multivariate cross-correlated random fields on the failure behaviour of passive trapdoors in c - ϕ soil utilising...
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Multivariate fire risk models using copula regression in Kalimantan, Indonesia
Forest fires have become a national issue yearly and elicited serious attention from the government and researchers in Indonesia. Copula-based joint...
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A comparative study of artificial neural networks and multivariate regression for predicting groundwater depths in the Arak aquifer
In recent years, the groundwater resources of Arak plain have been under severe stress, so in some areas, due to the drying up of wells, the depth of...
