Developing an Optimal Spatial Predictive Model for Seabed Sand Content Using Machine Learning, Geostatistics, and Their Hybrid Methods

Li, Jin; Siwabessy, Justy; Huang, Zhi; Nichol, Scott L.

doi:10.3390/geosciences9040180

Cited by 11 publications

(8 citation statements)

References 38 publications

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“…For RF, variable selection methods may include (1) variable importance (VI) [78], (2) averaged variable importance (AVI) [79], (3) Boruta [105], (4) knowledge informed AVI (KIAVI) [6,79], (5) recursive feature selection (RFE) [106], and (6) variable selection using RF (VSURF) [107]. Of these methods, KIAVI is recommended because it outperforms all other variable selection methods [6,79,104,108].…”

Section: Variable Selectionmentioning

confidence: 99%

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A Critical Review of Spatial Predictive Modeling Process in Environmental Sciences with Reproducible Examples in R

2019

Applied Sciences

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Spatial predictive methods are increasingly being used to generate predictions across various disciplines in environmental sciences. Accuracy of the predictions is critical as they form the basis for environmental management and conservation. Therefore, improving the accuracy by selecting an appropriate method and then developing the most accurate predictive model(s) is essential. However, it is challenging to select an appropriate method and find the most accurate predictive model for a given dataset due to many aspects and multiple factors involved in the modeling process. Many previous studies considered only a portion of these aspects and factors, often leading to sub-optimal or even misleading predictive models. This study evaluates a spatial predictive modeling process, and identifies nine major components for spatial predictive modeling. Each of these nine components is then reviewed, and guidelines for selecting and applying relevant components and developing accurate predictive models are provided. Finally, reproducible examples using spm, an R package, are provided to demonstrate how to select and develop predictive models using machine learning, geostatistics, and their hybrid methods according to predictive accuracy for spatial predictive modeling; reproducible examples are also provided to generate and visualize spatial predictions in environmental sciences.

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Section: Variable Selectionmentioning

confidence: 99%

“…For GBM, variables can be selected in terms of the relative influence [95,108]. The recursive feature selection [106] can also be used for variable selection.…”

Section: Variable Selectionmentioning

confidence: 99%

A Critical Review of Spatial Predictive Modeling Process in Environmental Sciences with Reproducible Examples in R

2019

Applied Sciences

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“…[16] uses neural networks to predict surface daily minimum temperature. Decision tree methods as random forest are applied by [17,18] to predict seabed sand content. This work applies and compares the accuracy of the following four methods to GFS weather data: linear interpolation, the RBF method, Kriging, feedforward neural networks, and decision tree methods.…”

Section: Introductionmentioning

confidence: 99%

Importance of Weather Conditions in a Flight Corridor

Chen

Fricke

Okhrin

et al. 2022

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Current research initiatives, such as the Single European Sky Air Traffic Management Research Program, call for an air traffic system with improved safety and efficiency records and environmental compatibility. The resulting multi-criteria system optimization and individual flight trajectories require, in particular, reliable three-dimensional meteorological information. The Global (Weather) Forecast System only provides data at a resolution of around 100 km. We postulate a reliable interpolation at high resolution to compute these trajectories accurately and in due time to comply with operational requirements. We investigate different interpolation methods for aerodynamic crucial weather variables such as temperature, wind speed, and wind direction. These methods, including Ordinary Kriging, the radial basis function method, neural networks, and decision trees, are compared concerning cross-validation interpolation errors. We show that using the interpolated data in a flight performance model emphasizes the effect of weather data accuracy on trajectory optimization. Considering a trajectory from Prague to Tunis, a Monte Carlo simulation is applied to examine the effect of errors on input (GFS data) and output (i.e., Ordinary Kriging) on the optimized trajectory.

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“…In various scientific fields, the successful designing of hybrid techniques for forest fires (e.g., Abedi Gheshlaghi & Feizizadeh, 2017; Chen, Xie, et al, 2018; Li, Siwabessy, Huang, & Nichol, 2019; Liu & Sasaki, 2019; Sakizadeh, 2020; Takahashi & Yao, 2020; Talal, Attiya, Metwalli, El‐Samie, & Dessouky, 2019) is the next logical step in spatially evident forest fire modeling. This helps hybrid techniques to evaluate the probability of forest fires with minimum error, both during training and particularly during the validation phase (Abedi Gheshlaghi et al, 2020; Bui et al, 2017; Busico et al, 2019; Jaafari et al, 2019; Syifa et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Forest fire susceptibility modeling using hybrid approaches

Gheshlaghi

Feizizadeh

Blaschke

et al. 2020

Transactions in GIS

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Forest fires are considered one of the most highly damaging and devastating of natural disasters, causing considerable casualties and financial losses every year. Hence, it is important to produce susceptibility maps for the management of forest fires so as to reduce their harmful effects. The purpose of this study is to map the susceptibility to forest fires over Nowshahr County in Iran, using an integrated approach of index of entropy (IOE) with fuzzy membership value (FMV), frequency ratio (FR), and information value (IV) with a comparison of their precision. The spatial database incorporated the inventory of forest fire and conditioning factors. As a whole, 41 forest fire locations were identified. Out of these, 29 locations (≈70%) were randomly chosen for the forest fire susceptibility modeling (FFSM), and the remaining 12 locations (≈30%) were utilized for the validation of the models. Subsequently, utilizing FMV‐IOE, FR‐IOE, and IV‐IOE models, forest fire susceptibility maps were acquired. Finally, the modeling ability of the models for FFSM was assessed using an area under the receiver operating characteristic (AUROC) curve. The results manifested that the prediction accuracy of the FMV‐IOE model is slightly higher than that of the FR‐IOE and IV‐IOE models. The incorporation of IOE with FMV, FR, and IV models had AUROC values of 0.890, 0.887, and 0.878, respectively. The resulting FFSM can be effective in fire repression resource planning, sustainable development, and primary warning in regions with similar conditions.

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Developing an Optimal Spatial Predictive Model for Seabed Sand Content Using Machine Learning, Geostatistics, and Their Hybrid Methods

Cited by 11 publications

References 38 publications

A Critical Review of Spatial Predictive Modeling Process in Environmental Sciences with Reproducible Examples in R

A Critical Review of Spatial Predictive Modeling Process in Environmental Sciences with Reproducible Examples in R

Importance of Weather Conditions in a Flight Corridor

Forest fire susceptibility modeling using hybrid approaches

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