Gully erosion susceptibility mapping using four machine learning methods in Luzinzi watershed, eastern Democratic Republic of Congo

Chuma, Géant Basimine; Mugumaarhahama, Yannick; Jean, Mondo Mubalama; Espoir, Bagula Mukengere; Adrien, Ndeko Byamungu; Lucungu, Prince Baraka; Karume, Katcho; Gustave, Mushagalusa Nachigera; Schmitz, Serge

doi:10.1016/j.pce.2022.103295

Cited by 19 publications

(6 citation statements)

References 104 publications

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“…008/PCMT/2022 of August 31, 2022, regarding cybersecurity. [79] considers four machine learning methods to examine gully erosion in Democratic Republic of the Congo. Soil erosion by gullying causes severe soil degradation, resulting in profound socio-economic and environmental damages in tropical and subtropical regions.…”

Section: Chadmentioning

confidence: 99%

Machine Intelligence in Africa: a survey

Tembine,

Tapo,

Danioko

et al. 2024

Preprint

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Section: Chadmentioning

confidence: 99%

Machine Intelligence in Africa: a survey

Tembine,

Tapo,

Danioko

et al. 2024

Preprint

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“…Earlier studies on the delineation and condition of marine clay gullies in Norway have relied on manual mapping of aerial images (Hamre et al, 2021) or high-resolution terrain models and surficial geological maps (Christoffersen et al, 2021;van Boeckel et al, 2022;van Boeckel et al, 2023). Approaches to automated delineations of gullies outside of Norway have also developed rapidly but have mostly focused on gully erosion susceptibility and comparison of different machine-learning algorithms (Arabameri et al, 2020;Arabameri et al, 2022;Band et al, 2020;Chen et al, 2021;Chuma et al, 2023;Gayen et al, 2019;Mohebzadeh et al, 2022;Setargie et al, 2023). Setargie et al (2023) used a Random Forest-based approach in Ethiopia, combining 164 manually mapped gullies with 20 predictors.…”

Section: Introductionmentioning

confidence: 99%

The applicability of automated marine clay gully delineation using deep learning in Norway

Ganerød,

van Boeckel,

Solberg

2024

Earth Surf Processes Landf

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Gullies and ravines are common landforms in raised marine fine‐grained deposits in Norway. Gullies in marine clay are significant landforms indicative of soil erosion and natural hazards and are of high conservation value. As a result of the substantial impact of human intervention over the past century, marine clay gullies are now red‐listed. To monitor the condition of these landforms, we need to improve our understanding of their spatial extent, complexity and morphology. We explore the applicability of automated approaches that use a methodology of combining deep learning (DL), fully convolutional neural networks (FCNNs) and an unmodified U‐Net model with ArcPy libraries and ground truth data to derive a high‐resolution map of gullies in raised marine fine‐grained deposits. Predictors used comprise solely terrain derivatives to broaden the usage of the pre‐trained model to other regions. Our best model achieved a precision score of 0.82 and a recall of 0.75. We find that our pre‐trained model can successfully predict gullies, also in blind‐test areas. The model performs better in regions with similar geological settings, scoring a length‐weighted overlap of >70% with reference datasets. The novelty of this study is that we demonstrate that the model's applicability for mapping routines increases when we post‐process the predictions by eliminating noise, especially by using the predictions derived from ensembled models. We, therefore, conclude that the pre‐trained models can effectively be used to supplement the geomorphological mapping of marine clay gullies in Norway. The outcome of this research contributes towards mapping the spatial extent and condition of red‐listed landforms in Norway, as well as the development of monitoring systems for future landscape change.

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“…However, there is a lack of standard guidelines that determine the role of geo‐environmental factors in gully occurrence. According to the literature, elevation, slope, aspect, plan curvature, profile curvature, relative slope position (RSP), topographic ruggedness index (TRI), stream power index (SPI), topographic wetness index (TWI), lithology, soil type, soil texture, land use/cover, mean annual precipitation, distance from roads, and distance from streams have been known as main drivers of gully occurrences (Bernini et al, 2021; Chuma et al, 2023; Conoscenti et al, 2013). Interestingly, artificial intelligence models support planners and decision‐makers in investigating the role of these variables in gully occurrences (Huang 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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Gully erosion susceptibility mapping using four machine learning methods in Luzinzi watershed, eastern Democratic Republic of Congo

Cited by 19 publications

References 104 publications

Machine Intelligence in Africa: a survey

Machine Intelligence in Africa: a survey

The applicability of automated marine clay gully delineation using deep learning in Norway

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

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