Soil erosion susceptibility mapping using ensemble machine learning models: A case study of upper Congo river sub-basin

Kulimushi, Luc Cimusa; Bashagaluke, Janvier Bigabwa; Prasad, Pankaj; Heri‐Kazi, Aimé B.; Kushwaha, Nand Lal; Masroor, Md; Choudhari, Pandurang; Elbeltagi, Ahmed; Sahana, Mehebub; Mohammed, Safwan

doi:10.1016/j.catena.2022.106858

Cited by 18 publications

(4 citation statements)

References 91 publications

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“…The results are consistent with other studies, e.g. Kulimushi et al (2023) mapping Soil erosion susceptibility using ensemble machine learning models and considering the main effective natural variables.…”

Section: Variables Affecting the Modelsupporting

confidence: 92%

Potential Erosion Mapping Using Machine Learning Methods (Case Study: Rud-e-Faryab Basin, Bushehr Province, Iran)

Damaneh,

Safdari,

Azarnejad

et al. 2023

Preprint

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Purpose. The requirement of soil erosion management is to provide appropriate solutions which can be obtained by recognizing the state of soil erosion. The purpose of the study is to model the potential erosion using 10 environmental variables and 10 models in BIOMOD-2 package in R software and to evaluate the stability of the model in order to be aware of the sensitivity of erosion in Rud-e-Faryab basin in Bushehr province. Methods. In addition, the location of five dominant erosions of the basin was recorded using GPS. These events were classified into two groups of training and validation with a ratio of 70 to 30. In order to evaluate the stability, the BIOMOD-2 package model was repeated 10 times. The efficiency of the model was evaluated using ROC, KAPPA and TSS. Results. According to stability results, the best models are determined as GLM model with the accuracy of above 70% in low-rated waterway erosion, RF model with the accuracy of above 80% in medium-rated waterway erosion, ANN and SRE models with 100% accuracy in medium-rated rill erosion, MARS model with accuracy above 60% in high-rated rill erosion and GLM model with accuracy above 80% in medium-rated stream bank erosion according to all three validation parameters. Conclusion. These results indicate high agreement with studies which have used the vector machine models as an effective tool in preparing the potential erosion map of watersheds. In general, it can be said that vector machine methods are a helpful new approach for land use planning and erosion risk management.

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Section: Variables Affecting the Modelsupporting

confidence: 92%

Potential Erosion Mapping Using Machine Learning Methods (Case Study: Rud-e-Faryab Basin, Bushehr Province, Iran)

Damaneh,

Safdari,

Azarnejad

et al. 2023

Preprint

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“…Ensemble learning (EL) is a machine learning framework based on the idea of "group intelligence", which is capable of combining (averaging) the predictions of multiple participating models [25][26][27][28]. Compared to single-model predictions, EL can consistently generate more accurate and reliable predictions [29][30][31]. In practical applications, EL can be homogenous and heterogeneous based on whether the participating models are of the same type [32].…”

Section: Introductionmentioning

confidence: 99%

Monitoring Soil Salinity Classes through Remote Sensing-Based Ensemble Learning Concept: Considering Scale Effects

Chen,

Wu,

2024

Remote Sensing

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Remote sensing (RS) technology can rapidly obtain spatial distribution information on soil salinization. However, (1) the scale effects resulting from the mismatch between ground-based “point” salinity data and remote sensing pixel-based “spatial” data often limit the accuracy of remote sensing monitoring of soil salinity, and (2) the same salinity RS monitoring model usually provides inconsistent or sometimes conflicting explanations for different data. Therefore, based on Landsat 8 imagery and synchronously collected ground-sampling data of two typical study regions (denoted as N and S, respectively) of the Yichang Irrigation Area in the Hetao Irrigation District for May 2013, this study used geostatistical methods to obtain “relative truth values” of salinity corresponding to the Landsat 8 pixel scale. Additionally, based on Landsat 8 multispectral data, 14 salinity indices were constructed. Subsequently, the Correlation-based Feature Selection (CFS) method was used to select sensitive features, and a strategy similar to the concept of ensemble learning (EL) was adopted to integrate the single-feature-sensitive Bayesian classification (BC) model in order to construct an RS monitoring model for soil salinization (Nonsaline, Slightly saline, Moderately saline, Strongly saline, and Solonchak). The research results indicated that (1) soil salinity exhibits moderate to strong variability within a 30 m scale, and the spatial heterogeneity of soil salinity needs to be considered when developing remote sensing models; (2) the theoretical models of salinity variance functions in the N and S regions conform to the exponential model and the spherical model, with R2 values of 0.817 and 0.967, respectively, indicating a good fit for the variance characteristics of salinity and suitability for Kriging interpolation; and (3) compared to a single-feature BC model, the soil salinization identification model constructed using the concept of EL demonstrated better potential for robustness and effectiveness.

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“…SE has been happening for hundreds of years, allowing the soil regeneration and regaining of its nutritional value. Additionally, it increases sediment transport (estimated to be 2.3 ± 0.6 BMT of sediment every year) beyond agricultural fields [3]. Due to climate change and land use changes, many areas are at risk of SE worldwide, including arid and semi-arid regions as well as humid ones [4,5].…”

Section: Introductionmentioning

confidence: 99%

Harnessing the Power of Remote Sensing and Unmanned Aerial Vehicles: A Comparative Analysis for Soil Loss Estimation on the Loess Plateau

Kariminejad,

Kazemi Garajeh,

Hosseinalizadeh

et al. 2023

Drones

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This study explored the innovative use of multiple remote sensing satellites and unmanned aerial vehicles to calculate soil losses in the Loess Plateau of Iran. This finding emphasized the importance of using advanced technologies to develop accurate and efficient soil erosion assessment techniques. Accordingly, this study developed an approach to compare sinkholes and gully heads in hilly regions on the Loess Plateau of northeast Iran using convolutional neural network (CNN or ConvNet). This method involved coupling data from UAV, Sentinel-2, and SPOT-6 satellite data. The soil erosion computed using UAV data showed AUC values of 0.9247 and 0.9189 for the gully head and the sinkhole, respectively. The use of SPOT-6 data in gully head and sinkhole computations showed AUC values of 0.9105 and 0.9123, respectively. The AUC values were 0.8978 and 0.9001 for the gully head and the sinkhole using Sentinel-2, respectively. Comparison of the results from the calculated UAV, SPOT-6, and Sentinel-2 data showed that the UAV had the highest accuracy for calculating sinkhole and gully head soil features, although Sentinel-2 and SPOT-6 showed good results. Overall, the combination of multiple remote sensing satellites and UAVs offers improved accuracy, timeliness, cost effectiveness, accessibility, and long-term monitoring capabilities, making it a powerful approach for calculating soil loss in the Loess Plateau of Iran.

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Soil erosion susceptibility mapping using ensemble machine learning models: A case study of upper Congo river sub-basin

Cited by 18 publications

References 91 publications

Potential Erosion Mapping Using Machine Learning Methods (Case Study: Rud-e-Faryab Basin, Bushehr Province, Iran)

Potential Erosion Mapping Using Machine Learning Methods (Case Study: Rud-e-Faryab Basin, Bushehr Province, Iran)

Monitoring Soil Salinity Classes through Remote Sensing-Based Ensemble Learning Concept: Considering Scale Effects

Harnessing the Power of Remote Sensing and Unmanned Aerial Vehicles: A Comparative Analysis for Soil Loss Estimation on the Loess Plateau

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