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

Abstract

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.

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

“…According to the literature, common classification ranges were considered to classify each gully susceptibility map based on the equal interval procedure: very low susceptibility (0–0.2). low susceptibility (0.2–0.4), medium susceptibility (0.4–0.6), high susceptibility (0.6–0.8), and very high susceptibility (0.8–1) (Choubin et al, 2019; Hitouri et al, 2022; Raj et al, 2022). The equal interval procedure makes it possible to compare the output of different models (Rahmati et al, 2022).…”

“…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).…”

Flood Susceptibility Mapping Using SAR Data and Machine Learning Algorithms in a Small Watershed in Northwestern Morocco

“…The stream power index (SPI) measures the capacity for erosion due to surface water flow [47]. It was calculated using the following equation [48], which depends on sediment transport and river channel erosion [49].…”

“…SVM is another powerful machine learning algorithm that has been widely used for flood susceptibility modeling [21]. SVM is a binary classification algorithm that aims to find an optimal hyperplane that separates data points belonging to different classes with the largest margin [47,55]. It can also be extended for multi-class classification tasks [51].…”

Stability prediction for soil-rock mixture slopes based on a novel ensemble learning model

“…The rock contact, the rock content greatly contribute to the stability coefficient of soil-rock nixture slopes (Wang et al, 2022a;Wang et al, 2022b;Wang et al, 2022c). If there are multicollinearities among the input parameters in machine learning, the accuracy of the prediction model can be affected (Hitouri et al, 2022;Selamat et al, 2022;Xia et al, 2022). Therefore, this study uses rock content as an input parameter instead of weight, cohesion, and internal friction angle.…”