Factors Affecting Landslide Susceptibility Mapping: Assessing the Influence of Different Machine Learning Approaches, Sampling Strategies and Data Splitting

Abraham, Minu Treesa; Satyam, Neelima; Lokesh, Revuri; Pradhan, Biswajeet; Alamri, Abdullah

doi:10.3390/land10090989

Cited by 40 publications

(15 citation statements)

References 36 publications

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“…However, our study did not compare different suitability evaluation methods. Future studies should add other evaluation methods, such as machine learning and sensitivity analysis, to increase the objectivity of suitability evaluation [ 83 , 84 ].…”

Section: Discussionmentioning

confidence: 99%

Spatio-Temporal Evolution, Prediction and Optimization of LUCC Based on CA-Markov and InVEST Models: A Case Study of Mentougou District, Beijing

Yang

Zhang²,

Zhu

et al. 2022

IJERPH

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With the rapid advancement of urbanization and industrialization, the contradiction between the social economy and resources and the environment has become increasingly prominent. On the basis of limited land resources, the way to promote multi-objective comprehensive development such as economic, social development and ecological and environmental protection through structure and layout regulation, so as to maximize regional comprehensive benefits, is an important task of current land spatial planning. Our aim is to obtain land-use-change data in the study area using remote-sensing data inversion and multiple-model simulation. Based on land suitability evaluation, we predict and optimize the land use structure of the study area in 2030 and evaluate and compare ecosystem services. Based on remote-sensing images and eco-environmental data from 1985 to 2014 in the study area, land use/land cover change (LUCC) and future simulation data were obtained by using supervised classification, landscape metrics and the CA-Markov model. The ecosystem services were evaluated by the InVEST model. The analytic hierarchy process (AHP) method was used to evaluate the land suitability for LUCC. Finally, the LUCC in 2030 under two different scenarios, Scenario_1 (prediction) and Scenario_2 (optimization), were evaluated, and the ecosystem service functions were compared. In the last 30 years, the landscape in the study area has gradually fragmented, and the built-up land has expanded rapidly, increased by one-third, mainly at the cost of cropland, orchards and wasteland. According to the suitability evaluation, giving priority to the land use types with higher environmental requirements will ensure the study area has a higher ecosystem service value. The rapid development of urbanization has a far-reaching impact on regional LUCC. Intensive land resources need reasonable and scientific land use planning, and land use planning should be based on the suitability evaluation of land resources, which can improve the regional ecosystem service function.

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Section: Discussionmentioning

confidence: 99%

Spatio-Temporal Evolution, Prediction and Optimization of LUCC Based on CA-Markov and InVEST Models: A Case Study of Mentougou District, Beijing

Yang

Zhang²,

Zhu

et al. 2022

IJERPH

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show abstract

“…There are no standard guidelines for selecting LCFs; data availability for the case study location is often the main factor in selecting LCFs. Many authors have considered 20 or more causative factors [38][39][40][41][42][43]. The importance of causative factors differs from one study location to another.…”

Section: Legendmentioning

confidence: 99%

“…However, the data point may not be able to represent the whole area of landslide. Abrahan et al [43] explored the positional accuracy and sampling strategy of landslide inventory. The study compared landslide polygon data and point data for generating LSM.…”

Section: Datasets and Landslide Inventorymentioning

confidence: 99%

Landslide Susceptibility Mapping Using Machine Learning: A Literature Survey

et al. 2022

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Landslide is a devastating natural disaster, causing loss of life and property. It is likely to occur more frequently due to increasing urbanization, deforestation, and climate change. Landslide susceptibility mapping is vital to safeguard life and property. This article surveys machine learning (ML) models used for landslide susceptibility mapping to understand the current trend by analyzing published articles based on the ML models, landslide causative factors (LCFs), study location, datasets, evaluation methods, and model performance. Existing literature considered in this comprehensive survey is systematically selected using the ROSES protocol. The trend indicates a growing interest in the field. The choice of LCFs depends on data availability and case study location; China is the most studied location, and area under the receiver operating characteristic curve (AUC) is considered the best evaluation metric. Many ML models have achieved an AUC value > 0.90, indicating high reliability of the susceptibility map generated. This paper also discusses the recently developed hybrid, ensemble, and deep learning (DL) models in landslide susceptibility mapping. Generally, hybrid, ensemble, and DL models outperform conventional ML models. Based on the survey, a few recommendations and future works which may help the new researchers in the field are also presented.

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“…The NDVI can quantitatively reflect the relationships between vegetation coverage and slope stabilities (Abraham et al, 2021). The NDVI values were derived from Landsat4-5TM images with a 30-m spatial resolution acquired on 11 August 2011 (http://www.gscloud.cn).…”

Section: Environmental Factorsmentioning

confidence: 99%

Landslide susceptibility assessment using statistical and machine learning techniques: A case study in the upper reaches of the Minjiang River, southwestern China

et al. 2022

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Landslides have frequently occurred in deeply incised valleys in the upper reaches of the Minjiang River. Long-term interactions between rock uplift and river undercutting developed widely distributed landslides in this catchment, which recorded the typical tectonic geomorphology in the eastern margin of the Tibetan Plateau. In this study, we examined the landslides in the Minjiang catchment and aimed to compare the prediction ability of the statistical and machine learning (ML) models in landslide susceptibility assessment. We adopted the statistical models of the frequency ratio (FR) and information value (IV) models, and the ML models represented by a logistic model tree (LMT) and radial basis function classifier (RBFC) for landslide prediction. An inventory map of 668 landslides was compiled, and the landslides were randomly divided into training (80%) and validation (20%) datasets. Furthermore, 11 control factors of landslides based on topography, geology, hydrology, and other environments were applied for the analysis. The comprehensive performance of the four models was validated and compared using accuracy and area under the receiver operating characteristic curve (AUC). The results indicated that both sides of the valley along the Mingjiang and Heishuihe Rivers are in the high and very high susceptibility zones; in particular, the river segment from Wenchuan to Maoxian County has the highest susceptibility. The AUC values of the FR, IV, LMT, and RBFC models with the training data were 0.842, 0.862, 0.898, and 0.894, respectively, while the validation dataset illustrated the highest AUC value of 0.879 in the LMT model, followed by the RBFC (0.871), IV (0.869), and FR (0.839) models. Moreover, the LMT and RBFC models had higher accuracy values than the FR and IV models. This suggests that the ML models are superior to the statistical models in generating adequate landslide susceptibility maps, and the LMT model is the most efficient one for landslide prediction in the study region. This study provides a typical case in a landslide-prone region in the plateau margin to advance the understanding of landslide susceptibility assessment.

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Factors Affecting Landslide Susceptibility Mapping: Assessing the Influence of Different Machine Learning Approaches, Sampling Strategies and Data Splitting

Cited by 40 publications

References 36 publications

Spatio-Temporal Evolution, Prediction and Optimization of LUCC Based on CA-Markov and InVEST Models: A Case Study of Mentougou District, Beijing

Spatio-Temporal Evolution, Prediction and Optimization of LUCC Based on CA-Markov and InVEST Models: A Case Study of Mentougou District, Beijing

Landslide Susceptibility Mapping Using Machine Learning: A Literature Survey

Landslide susceptibility assessment using statistical and machine learning techniques: A case study in the upper reaches of the Minjiang River, southwestern China

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