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Predicting rice yield based on weather variables using multiple linear, neural networks, and penalized regression models
Rice is one of the most important cereal foods not only for India but also for the world. The production of crop depends upon the favorable climatic...
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Multiple linear regression and gene expression programming to predict fracture density from conventional well logs of basement metamorphic rocks
Fracture identification and evaluation requires data from various resources, such as image logs, core samples, seismic data, and conventional well...
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A multiple linear regression model for the prediction of summer rainfall in the northwestern Peruvian Amazon using large-scale indices
The northwestern Peruvian Amazon (NWPA) basin (78.4–75.8° W, 7.9–5.4° S) is an important region for coffee and rice production in Peru. Currently, no...
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A Comparative Study of the Influence of Volumetric Joint Counts (Jv) and Resistivity on Rock Quality Designation (RQD) Using Multiple Linear Regression
Rock quality designation (RQD) index is useful for assessing rock mass quality and slope instabilities. The traditional method of calculating RQD,...
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Modified extension evaluation of foundation pit engineering combined with orthogonal experiments and multiple linear regression
In recent years, excavation work of underground railways has been becoming increasingly complex in the congested urban areas and frequently exposed...
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Efficacy of linear multiple regression and artificial neural network for long-term rainfall forecasting in Western Australia
Precipitation is one of the most intrinsic resources for manifold industrial activities all over Western Australia; consequently, immaculate rainfall...
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Estimating Soil Moisture by Radar Data Based on Multiple Regression
AbstractThe problem of estimating soil moisture by remote (satellite) methods remains topical. To do this, regression models based on the correlation...
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Forecasting yield of rapeseed and mustard using multiple linear regression and ANN techniques in the Brahmaputra valley of Assam, North East India
Crop yield forecasting is the art of predicting yield before harvest and is crucial for sound planning and policy making at various levels. Rapeseed...
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Prediction of flyrock distance induced by blasting using particle swarm optimization and multiple regression analysis: an engineering perspective
Flyrock is one of the major safety hazards induced by blasting operations. However, few studies were for predicting blasting-induced flyrock distance...
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Statistical analysis of the landslides triggered by the 2021 SW Chelgard earthquake (ML = 6) using an automatic linear regression (LINEAR) and artificial neural network (ANN) model based on controlling parameters
This study uses automatic linear regression (LINEAR) and artificial neural network (ANN) models to statistically analyze the area of landslides...
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Assessing the Importance of Climate Variables on RDI and SPEI Using Backward Multiple Linear Regression in Arid to Humid Regions Over Iran
Drought is a natural disaster that has adverse effects on various regions, especially in areas with a shortage of available water resources and areas...
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Exploring the accuracy of Random Forest and Multiple Regression models to predict rill detachment in soils under different plant species and soil treatments in deforested lands
Rill detachment capacity (D c ) is a key factor of the overall erosion process on steep and long hillslopes of deforested areas. Accurate predictions...
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Estimation of loss on ignition values of the magnesite minerals using robust multiple regression
Magnesite is an ore used in the production of a wide variety of industrial minerals and compounds and magnesium metal, as well as its alloys. The...
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Digital soil map**: a predictive performance assessment of spatial linear regression, Bayesian and ML-based models
Nowadays, information on the spatial distribution of soil properties is considered a key element for environmental research and for agricultural...
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Slope-scale landslide susceptibility assessment based on coupled models of frequency ratio and multiple regression analysis with limited historical hazards data
Conducting a precise landslide susceptibility assessment at the slope scale is challenging due to complex parameters and limited historical hazards...
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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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Using random forest and multiple-regression models to predict changes in surface runoff and soil erosion after prescribed fire
Prescribed fire is a viable practice to reduce the wildfire risk in forests, but its application may lead to increased surface runoff and soil...
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Linear regression model for noise pollution over central Delhi to highlight the alarming threat for the environment
Noise pollution is the most ignored and underappreciated problem in the world. Even though scientists all over the world have done a lot of research...
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Inverse distance weighted (IDW) and kriging approaches integrated with linear single and multi-regression models to assess particular physico-consolidation soil properties for Kirkuk city
Due to significant budgetary constraints, it is impractical to experimentally study a wide territory to identify soil characteristics over the entire...
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An Efficient Experimental Model to Estimate the Performance of the Raise Borer Drilling Machine Using Linear and Nonlinear Regression Approaches in the Azad Dam in Iran
This research evaluates the chief shaft in the Azad Dam hydroelectric power plant in Iran using a raise borer machine (RBM). Core samples were taken...
