Assessment of the interpretability of data mining for the spatial modelling of water erosion using game theory

Mohammadifar, Aliakbar; Gholami, Hamid; Comino, Jesús Rodrígo; Collins, Adrian L.

doi:10.1016/j.catena.2021.105178

Cited by 51 publications

(23 citation statements)

References 83 publications

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“…This is in line with many findings, e.g. (Chang Z et al 2020;Hong et al 2017;Mohammadifar et al 2021;Pradhan 2013) that reported SVM outperformed other traditional algorithms.…”

Section: Resultssupporting

confidence: 92%

“…Furthermore, the reliability of the models (Figure 6b-d), when compared, has been enhanced. The obtained differences are attributed to how well the training process was carried out, plus the sufficiency of the training datasets; these were also observed in many works of literature (Chen W and Zhang Y 2021;Mohammadifar et al 2021). Lack of a considerable factor difference from model comparisons entails the absence of significant data overfitting in the training process (Hong et al 2017).…”

Section: Resultsmentioning

confidence: 94%

“…Statistical significance test (Table 5) is conducted to check the level of significance among the models, which helps in reducing subjectivity (Ritter and Muñoz-Carpena 2013). The P-Value and the Z-values test were part of the statistical significance investigation on the models as reported similarly by (Chen W and Zhang S 2021;Mohammadifar et al 2021).…”

Section: Resultsmentioning

confidence: 99%

“…The landslides susceptibility models developed from the SVM, RT, and NBM algorithms (Figure 5c,d) were subjected to the performance evaluation (Hong et al 2020;Li L et al 2020;Shin 2020). Conducting a performance analysis on results obtained through data mining techniques is crucial and cannot be over-emphasized (Remondo et al 2003;Brock et al 2020;Mohammadifar et al 2021). In addition, the evaluations help verify and define the level of accuracy and performance of the landslides susceptibility models (Althuwaynee et al 2021).…”

Section: Resultsmentioning

confidence: 99%

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Advanced data mining techniques for landslide susceptibility mapping

Ibrahim

Mustaffa

Balogun

et al. 2021

Geomatics, Natural Hazards and Risk

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This paper describes the development and validation of landslides susceptibility models for mountainous regions using advanced data mining techniques. The investigation was carried out to ascertain the effectiveness of Naïve Bayes Multinomial (NBM) and Random Trees (RT) in landslide susceptibility mapping. The NBM is an advancement of the frequently used Naïve Bayes classifiers, while the RT was built to overcome the limitations of the traditional forest classifiers. A geospatial database for this investigation comprises 148 landslide locations influenced by ten ( 10) landslide conditioning factors. The factors (Slope Angle, Slopes Elevation, Slope Aspect, Plan curvature, Profile Curvature, Lithology, Soil type, Stream power index (SPI), Sediment transport index (STI), and Rainfall precipitation) were drawn using a Multi Collinearity Decision Making (MCDM) technique. A Frequency Ratio (FR) analysis was used to obtain the relative significance of the factors in the slides. Predictive models were also developed by quantifying these models using data mining techniques. A section of the entire geospatial data (70%) was used as training datasets, while the remaining part of the data (30%) was used to validate the trained datasets. SVM, RT, and NBM algorithms were used to produce predicted datasets from the training datasets. These predicted datasets were used to develop the Landslides Susceptibility Models. A comparative assessment between the two classifiers against the famous traditional learning algorithm, the Support vector machines (SVM), was conducted. Model performance evaluators such as the AUROC, RSME, F-measure, MAE, and ACC were employed to check the predictive capabilities and accuracies of the models. The indices indicated that the SVM model performed better than the other two algorithms in both training and validation datasets. Further analysis and comparison of the models reveal that the new data mining techniques are reliable for landslide susceptibility. Simultaneously, the traditional algorithm is also useful and remains relevant, especially with similar site conditions. This study has provided insights on better ARTICLE HISTORY

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“…This is in line with many findings, e.g. (Chang Z et al 2020;Hong et al 2017;Mohammadifar et al 2021;Pradhan 2013) that reported SVM outperformed other traditional algorithms.…”

Section: Resultssupporting

confidence: 92%

Section: Resultsmentioning

confidence: 94%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Advanced data mining techniques for landslide susceptibility mapping

Ibrahim

Mustaffa

Balogun

et al. 2021

Geomatics, Natural Hazards and Risk

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“…Accelerated soil erosion by water is an environmental threat on different continents including Asia (e.g. Iran, China, India), North America (e.g., Canada), Europe (especially the Mediterranean regions) and Africa (Koiter et al, 2013;Mararakanye and Sumner, 2017;Mohammadifar et al, 2021;Marzolff et al, 2011). Elevated suspended sediment loads in riverine systems resulting from the accelerated erosion due to human activities are a serious threat to the sustainable management of watersheds and ecosystem services therein worldwide (Collins et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

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

Fatahi

Gholami

Esmaeilpour

et al. 2021

Preprint

Self Cite

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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-plots were used as an additional step to assess the tracers selected in the different final composite signatures for source discrimination. The predictions of spatial source contributions generated by GLUE were assessed using statistical tests and virtual sample mixtures. On this basis, TRT+KW+DFA and RTM+KW+DFA yielded the best source discrimination and the tracers in these composite signatures were shown by the biplots to be broadly conservative during transportation from source to sink. Using these final two composite signatures, the estimated mean contributions for the western, central and eastern sub-basins, respectively, ranged between 10-60% (overall mean contribution 36%), 0.3-16% (overall mean contribution 6%) and 38-77% (overall mean contribution 58%). In comparison, the final tracers selected using TRT+KW+PCA generated respective corresponding contributions of 1-42% (overall mean 20%), 0.5-30% (overall mean 12%) and 55-84% (overall mean 68%) compared with 17-69% (overall mean 41%), 0.2-12% (overall mean 5%) and 29-76% (overall mean 54%) using the final tracers selected by RTM+KW+PCA. Based on the mean absolute fit (MAF; ≥ 95% for all target sediment samples) and goodness-of-fit (GOF; ≥ 99% for all samples), GLUE with the final tracers selected using TRT+KW+PCA performed slightly better than GLUE with the final signatures selected by the three other combinations of statistical tests. Based on the virtual mixture tests, however, predictions provided by GLUE with the final tracers selected using TRT+KW+DFA and RTM+KW+DFA (mean MAE = 11% and mean RMSE = 13%) performed marginally better than GLUE with RTM+KW+PCA (mean MAE = 14% and mean RMSE =16%) and GLUE with TRT+KW+PCA (mean MAE = 17% and mean RMSE = 19%). The estimated source proportions can help watershed engineers plan the targeting of conservation programmes for soil and water resources.

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Snow avalanche susceptibility mapping using novel tree-based machine learning algorithms (XGBoost, NGBoost, and LightGBM) with eXplainable Artificial Intelligence (XAI) approach

Iban

Bilgilioğlu

2023

Stoch Environ Res Risk Assess

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Assessment of the interpretability of data mining for the spatial modelling of water erosion using game theory

Cited by 51 publications

References 83 publications

Advanced data mining techniques for landslide susceptibility mapping

Advanced data mining techniques for landslide susceptibility mapping

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

Snow avalanche susceptibility mapping using novel tree-based machine learning algorithms (XGBoost, NGBoost, and LightGBM) with eXplainable Artificial Intelligence (XAI) approach

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