Head-cut gully erosion susceptibility modelling based on ensemble Random Forest with oblique decision trees in Fareghan watershed, Iran

Pham, Quoc Bao; Mukherjee, Kaustuv; Norouzi, Akbar; Linh, Nguyễn Thị Thùy; Janizadeh, Saeid; Ahmadi, Kourosh; Cerdà, Artemi; Doan, Thi Ngoc Canh; Anh, Duong Tran

doi:10.1080/19475705.2020.1837968

Cited by 17 publications

(3 citation statements)

References 112 publications

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“…Random Forest (RF) RF [42], having high prediction accuracy and avoiding over fitting, is both an efficient and intuitive machine learning algorithm based on classification and regression tree [43][44][45][46]. Through the self-help sampling method, RF can obtain the sampling set containing m training samples in a given m sample data set after M random sampling, and then train based on each sampling set to construct a decision tree.…”

Section: Multi-layer Perceptron (Mlp)mentioning

confidence: 99%

Forecast of Hourly Airport Visibility Based on Artificial Intelligence Methods

et al. 2022

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Based on the hourly visibility data, visibility and its changes during 2010–2020 at monthly and annual time scales over 47 international airports in China are investigated, and nine artificial-intelligence-based hourly visibility prediction models are trained (hourly data in 2018–2019) and tested (hourly data in 2020) at these airports. The analyses show that the visibility of airports in eastern and central China is at a poor level all year round, and LXA (in Lhasa) has good visibility all year round. Airports in south and the northwest China have better visibility from May to October and poorer visibility from November to April. In all months, the increasing visibility mainly occurs in the central, northeast and coastal areas of China, while decreasing visibility mainly appears in the western and northern parts of China. In spring, summer and autumn, the changes difference between east and west is particularly obvious. This East–West distribution of trends is obviously different from the North–South distribution shown by the mean. For all airports, good visibility mainly occurs from 14:00–18:00 p.m. Beijing Time, while poor visibility mainly concentrates from 22:00 p.m. to 12:00 p.m. the next day, especially between 3:00–9:00 a.m. Our proposed artificial intelligence algorithm models can be reasonably used in airport visibility prediction. In particular, most algorithm models have the best results in the visibility prediction over HFE (in Hefei) and SJW (in Shijiazhuang). On the contrary, the worst forecast results appear at LXA and LHW (in Lanzhou) airports. The prediction results of airport visibility in the cold season (October–December) are better than those in the warm season (May–September). Among the algorithm models, the prediction performance of the RF-based model is the best.

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Section: Multi-layer Perceptron (Mlp)mentioning

confidence: 99%

Forecast of Hourly Airport Visibility Based on Artificial Intelligence Methods

et al. 2022

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“…Machine learning models have become widespread in a variety of applications related to geoscience and natural disaster assessments. The most widely used machine learning models in the gully susceptibility domain (called shallow machine learning models) are support vector machines (e.g., Pourghasemi et al, 2017; Rahmati et al, 2017), artificial neural networks (ANNs) (e.g., Roy et al, 2020; Shahabi et al, 2019), random forest (e.g., Pal et al, 2020; Pham et al, 2020; Saha et al, 2020), maximum entropy (e.g., Azareh et al, 2019), boosted regression trees (e.g., Amiri et al, 2019; Hembram et al, 2021; Wang et al, 2021), flexible discriminant analysis (e.g., Gayen et al, 2019), quick, unbiased, efficient statistical tree (e.g., Soleimanpour et al, 2021), randomized tree (e.g., Wang et al, 2022), naïve Bayes (e.g., Garosi et al, 2019; Hitouri et al, 2022; Lana et al, 2022), best‐first decision tree (e.g., Lei et al, 2020), least absolute shrinkage and selection operator (e.g., Pourghasemi et al, 2020), extreme gradient boosting (e.g., Chen et al, 2021), and extreme gradient boosting (e.g., Chen et al, 2021). Furthermore, artificial intelligence/machine learning models have been widely used for different geomorphic processes, including landslide, soil erosion, and land subsidence (e.g., Marjanović et al, 2011; Merghadi et al, 2020; Mohammady et al, 2019; Sahour et al, 2021; Sekkeravani et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Scrutinizing gully erosion hotspots using hybridized deep‐learning analysis to avoid land degradation

et al. 2023

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Despite the importance of the prediction of land susceptibility to gully erosion, there is a lack of research studies adopting the deep‐learning approach. This study aimed to predict gully susceptibility hotspots using hybridized deep‐learning models and evaluate their efficiency. Field records of gully occurrences in a gully‐prone region, the Talwar watershed (6468 km2), eastern Kurdistan province, Iran, were used to generate a gully inventory dataset. A total of 14 geomorphometric, environmental, and topo‐hydrological gully drivers were selected as predictor variables. The hybridized models were developed using convolutional neural network (NNC) and metaheuristic procedures, including the gray wolf optimizer (GWO) and the imperialist competitive algorithm (ICA). The validity of the resulting outputs was investigated based on the area under the receiver operating characteristic (ROC) curve. Results revealed that the NNC‐GWO had the highest efficiency in the validation step (AUC = 97.2%), whereas the NNC‐ICA was the second‐best model (AUC = 95.1%). The standalone NNC model showed the lowest accuracy (AUC = 91.2%) in predicting gully susceptibility hotspots compared to NNC‐GWO and NNC‐ICA. Thus, both hybridized models had better predictive performance for identifying gully susceptibility in comparison with the standalone NNC model. Furthermore, according to the NNC‐GWO model, about 0.2% (1294.8 ha) and 0.05% (235.2 ha) of the study area were identified as high and very high gully susceptibility classes. In addition, the application of the standalone NNC led to an overestimation of the susceptibility degree for gully initiation. This study supports researchers efforts to increase the model's performance when working in the land degradation domain.

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“…The RF model and information value approaches are the most often utilized methods in the bivariate model category [36,40]. The RF model has produced positive outcomes in various studies [36,41,42]. Bivariate models can be simply applied within a geographic information system (GIS) due to their straightforward interpretation [43].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Machine Learning Approach for Gully Erosion Mapping Susceptibility at a Watershed Scale

Hitouri

Varasano

Mohajane

et al. 2022

IJGI

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Gully erosion is a serious threat to the state of ecosystems all around the world. As a result, safeguarding the soil for our own benefit and from our own actions is a must for guaranteeing the long-term viability of a variety of ecosystem services. As a result, developing gully erosion susceptibility maps (GESM) is both suggested and necessary. In this study, we compared the effectiveness of three hybrid machine learning (ML) algorithms with the bivariate statistical index frequency ratio (FR), named random forest-frequency ratio (RF-FR), support vector machine-frequency ratio (SVM-FR), and naïve Bayes-frequency ratio (NB-FR), in mapping gully erosion in the GHISS watershed in the northern part of Morocco. The models were implemented based on the inventory mapping of a total number of 178 gully erosion points randomly divided into 2 groups (70% of points were used for training the models and 30% of points were used for the validation process), and 12 conditioning variables (i.e., elevation, slope, aspect, plane curvature, topographic moisture index (TWI), stream power index (SPI), precipitation, distance to road, distance to stream, drainage density, land use, and lithology). Using the equal interval reclassification method, the spatial distribution of gully erosion was categorized into five different classes, including very high, high, moderate, low, and very low. Our results showed that the very high susceptibility classes derived using RF-FR, SVM-FR, and NB-FR models covered 25.98%, 22.62%, and 27.10% of the total area, respectively. The area under the receiver (AUC) operating characteristic curve, precision, and accuracy were employed to evaluate the performance of these models. Based on the receiver operating characteristic (ROC), the results showed that the RF-FR achieved the best performance (AUC = 0.91), followed by SVM-FR (AUC = 0.87), and then NB-FR (AUC = 0.82), respectively. Our contribution, in line with the Sustainable Development Goals (SDGs), plays a crucial role for understanding and identifying the issue of “where and why” gully erosion occurs, and hence it can serve as a first pathway to reducing gully erosion in this particular area.

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Head-cut gully erosion susceptibility modelling based on ensemble Random Forest with oblique decision trees in Fareghan watershed, Iran

Cited by 17 publications

References 112 publications

Forecast of Hourly Airport Visibility Based on Artificial Intelligence Methods

Forecast of Hourly Airport Visibility Based on Artificial Intelligence Methods

Scrutinizing gully erosion hotspots using hybridized deep‐learning analysis to avoid land degradation

Hybrid Machine Learning Approach for Gully Erosion Mapping Susceptibility at a Watershed Scale

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