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Research paper thumbnail of A wrapper feature selection approach for efficient modelling of gully erosion susceptibility mapping

Progress in Physical Geography: Earth and Environment, 2021

Identifying the vulnerability level of an area to soil erosion, particularly gully erosion, is ke... more Identifying the vulnerability level of an area to soil erosion, particularly gully erosion, is key to the development of an efficient management strategy for policymakers. While efforts into susceptibility mapping of natural disasters have grown in recent years, understanding the most relevant predictive causal factors is still a challenge. As the selection of these factors, among many potentially relevant factors, is an important part of the model selection process, we propose a hybrid intelligent approach for the optimal selection of a set of relevant factors based on logistic regression (LR) and genetic algorithms. In order to verify the effectiveness of the proposed approach, this study also identified areas that were highly susceptible to gully erosion using three different classifiers – namely, the LR, support vector machine (SVM) and k-nearest neighbours (k-NN) techniques. We tested the approach in the Yeli Bedrag watershed in north-eastern Golestan province, Iran. The result...

Research paper thumbnail of Correction to: Monte Carlo fingerprinting of the terrestrial sources of different particle size fractions of coastal sediment deposits using geochemical tracers: some lessons for the user community

Environmental Science and Pollution Research

Research paper thumbnail of Comparison of Bayesian, k-Nearest Neighbor and Gaussian process regression methods for quantifying uncertainty of suspended sediment concentration prediction

Science of The Total Environment

Research paper thumbnail of Fingerprinting the Spatial Sources of Fine-grained Sediment Deposited in the Bed of the Mehran River Draining into the Northern Coast of the Persian Gulf using Elemental Geochemistry

Accurate information on the sources of suspended sediment in riverine systems is essential to tar... more Accurate information on the sources of suspended sediment in riverine systems is essential to target mitigation. Accordingly, we applied a generalized likelihood uncertainty estimation (GLUE) framework for quantifying contributions from three sub-basin spatial sediment sources in the Mehran River catchment draining into the Persian Gulf, Hormozgan province, southern Iran. A total of 28 sediment samples were collected from the three sub-basin sources and six from the overall outlet. 43 geochemical elements (e.g., major, trace and rare earth elements) were measured in the samples. Four different combinations of statistical tests comprising: 1) traditional range test (TRT), Kruskal-Wallis (KW) H-test and stepwise discriminant function analysis (DFA) (TRT+KW+DFA); 2) traditional range test using mean values (RTM) and two additional tests (RTM+KW+DFA); 3) TRT+KW+PCA (principle component analysis), and; 4) RTM+KW+PCA, were used to the spatial sediment source discrimination. Tracer bi-plot...

Research paper thumbnail of Source fingerprinting loess deposits in Central Asia using elemental geochemistry with Bayesian and GLUE models

Research paper thumbnail of Improving the spatial prediction of soil salinity in arid regions using wavelet transformation and support vector regression models

Research paper thumbnail of Using GLUE to pull apart the provenance of atmospheric dust

Research paper thumbnail of Correction to: Monte Carlo fingerprinting of the terrestrial sources of different particle size fractions of coastal sediment deposits using geochemical tracers: some lessons for the user community

Environmental Science and Pollution Research

The original publication of this paper contains a mistake.

Research paper thumbnail of A wrapper feature selection approach for efficient modelling of gully erosion susceptibility mapping

Progress in Physical Geography: Earth and Environment, 2021

Identifying the vulnerability level of an area to soil erosion, particularly gully erosion, is ke... more Identifying the vulnerability level of an area to soil erosion, particularly gully erosion, is key to the development of an efficient management strategy for policymakers. While efforts into susceptibility mapping of natural disasters have grown in recent years, understanding the most relevant predictive causal factors is still a challenge. As the selection of these factors, among many potentially relevant factors, is an important part of the model selection process, we propose a hybrid intelligent approach for the optimal selection of a set of relevant factors based on logistic regression (LR) and genetic algorithms. In order to verify the effectiveness of the proposed approach, this study also identified areas that were highly susceptible to gully erosion using three different classifiers – namely, the LR, support vector machine (SVM) and k-nearest neighbours (k-NN) techniques. We tested the approach in the Yeli Bedrag watershed in north-eastern Golestan province, Iran. The result...

Research paper thumbnail of Correction to: Monte Carlo fingerprinting of the terrestrial sources of different particle size fractions of coastal sediment deposits using geochemical tracers: some lessons for the user community

Environmental Science and Pollution Research

Research paper thumbnail of Comparison of Bayesian, k-Nearest Neighbor and Gaussian process regression methods for quantifying uncertainty of suspended sediment concentration prediction

Science of The Total Environment

Research paper thumbnail of Fingerprinting the Spatial Sources of Fine-grained Sediment Deposited in the Bed of the Mehran River Draining into the Northern Coast of the Persian Gulf using Elemental Geochemistry

Accurate information on the sources of suspended sediment in riverine systems is essential to tar... more Accurate information on the sources of suspended sediment in riverine systems is essential to target mitigation. Accordingly, we applied a generalized likelihood uncertainty estimation (GLUE) framework for quantifying contributions from three sub-basin spatial sediment sources in the Mehran River catchment draining into the Persian Gulf, Hormozgan province, southern Iran. A total of 28 sediment samples were collected from the three sub-basin sources and six from the overall outlet. 43 geochemical elements (e.g., major, trace and rare earth elements) were measured in the samples. Four different combinations of statistical tests comprising: 1) traditional range test (TRT), Kruskal-Wallis (KW) H-test and stepwise discriminant function analysis (DFA) (TRT+KW+DFA); 2) traditional range test using mean values (RTM) and two additional tests (RTM+KW+DFA); 3) TRT+KW+PCA (principle component analysis), and; 4) RTM+KW+PCA, were used to the spatial sediment source discrimination. Tracer bi-plot...

Research paper thumbnail of Source fingerprinting loess deposits in Central Asia using elemental geochemistry with Bayesian and GLUE models

Research paper thumbnail of Improving the spatial prediction of soil salinity in arid regions using wavelet transformation and support vector regression models

Research paper thumbnail of Using GLUE to pull apart the provenance of atmospheric dust

Research paper thumbnail of Correction to: Monte Carlo fingerprinting of the terrestrial sources of different particle size fractions of coastal sediment deposits using geochemical tracers: some lessons for the user community

Environmental Science and Pollution Research

The original publication of this paper contains a mistake.

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