Susceptibility assessment of landslides triggered by earthquakes in the Western Sichuan Plateau

Cao, Juan; Zhang, Zhao; Wang, Chenzhi; Liu, Jifu; Zhang, Liangliang

doi:10.1016/j.catena.2018.12.013

Cited by 85 publications

(45 citation statements)

References 40 publications

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“…The non-linear ML methods (RF and LightGBM) perform overall better than the traditional linear method (RR) in yield prediction (Figures 4-8), and such results have been reported in previous studies [19,63]. This could be explained by the non-linear relationships between variables and crop yields [1,34].…”

Section: Methods Comparisonsupporting

confidence: 80%

Identifying the Contributions of Multi-Source Data for Winter Wheat Yield Prediction in China

et al. 2020

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Wheat is a leading cereal grain throughout the world. Timely and reliable wheat yield prediction at a large scale is essential for the agricultural supply chain and global food security, especially in China as an important wheat producing and consuming country. The conventional approach using either climate or satellite data or both to build empirical and crop models has prevailed for decades. However, to what extent climate and satellite data can improve yield prediction is still unknown. In addition, socio-economic (SC) factors may also improve crop yield prediction, but their contributions need in-depth investigation, especially in regions with good irrigation conditions, sufficient fertilization, and pesticide application. Here, we performed the first attempt to predict wheat yield across China from 2001 to 2015 at the county-level by integrating multi-source data, including monthly climate data, satellite data (i.e., Vegetation indices (VIs)), and SC factors. The results show that incorporating all the datasets by using three machine learning methods (Ridge Regression (RR), Random Forest (RF), and Light Gradient Boosting (LightGBM)) can achieve the best performance in yield prediction (R2: 0.68~0.75), with the most individual contributions from climate (~0.53), followed by VIs (~0.45), and SC factors (~0.30). In addition, the combinations of VIs and climate data can capture inter-annual yield variability more effectively than other combinations (e.g., combinations of climate and SC, and combinations of VIs and SC), while combining SC with climate data can better capture spatial yield variability than others. Climate data can provide extra and unique information across the entire growing season, while the peak stage of VIs (Mar.~Apr.) do so. Furthermore, incorporating spatial information and soil proprieties into the benchmark models can improve wheat yield prediction by 0.06 and 0.12, respectively. The optimal wheat prediction can be achieved with approximately a two-month leading time before maturity. Our study develops timely and robust methods for winter wheat yield prediction at a large scale in China, which can be applied to other crops and regions.

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Section: Methods Comparisonsupporting

confidence: 80%

Identifying the Contributions of Multi-Source Data for Winter Wheat Yield Prediction in China

et al. 2020

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“…The inappropriate ratio might cause potential problems in the procedure of data mining, such as overfitting or deficient model training, which significantly affects the predictive performance of the model. A split of 70%-30% is a common choice adopted by many investigators for coping with this challenge [17,22,30,31]. Therefore, 1082 watersheds consisted of 541 positive cases and 541 negative cases were used to train the models, while 462 watersheds contained 231 positive cases and 231 negative cases served the output validation.…”

Section: Partition Of Data Setsmentioning

confidence: 99%

“…Logistic Regression (LR) is a multivariate regression algorithm that has been extensively used for the susceptibility assessment [22,33,34]. LR is suitable to understand the relationship between a binary variable (whether the debris flow will occur or not) and several causal factors, and estimate the probability of an event [35].…”

Section: Logistic Regression (Lr)mentioning

confidence: 99%

“…Three active faults run through the study area, namely the Longmenshan fault, the Xianshuihe fault, and the Anninghe fault. Complex geological and geomorphological characteristics play an important role to trigger heavy geological hazards in this region, because of complex interactions between the Qinghai-Tibet Plateau and the Sichuan Basin [22]. The study area belongs to the subtropical monsoon zone with an annual average rainfall of 1000 mm; over 80% of precipitation usually takes place in the monsoon season (between April and October) [5].…”

Section: Introductionmentioning

confidence: 99%

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Comparison of Different Machine Learning Methods for Debris Flow Susceptibility Mapping: A Case Study in the Sichuan Province, China

Adhikari

Stamatopoulos

et al. 2020

Remote Sensing

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Debris flow susceptibility mapping is considered to be useful for hazard prevention and mitigation. As a frequent debris flow area, many hazardous events have occurred annually and caused a lot of damage in the Sichuan Province, China. Therefore, this study attempted to evaluate and compare the performance of four state-of-the-art machine-learning methods, namely Logistic Regression (LR), Support Vector Machines (SVM), Random Forest (RF), and Boosted Regression Trees (BRT), for debris flow susceptibility mapping in this region. Four models were constructed based on the debris flow inventory and a range of causal factors. A variety of datasets was obtained through the combined application of remote sensing (RS) and geographic information system (GIS). The mean altitude, altitude difference, aridity index, and groove gradient played the most important role in the assessment. The performance of these modes was evaluated using predictive accuracy (ACC) and the area under the receiver operating characteristic curve (AUC). The results of this study showed that all four models were capable of producing accurate and robust debris flow susceptibility maps (ACC and AUC values were well above 0.75 and 0.80 separately). With an excellent spatial prediction capability and strong robustness, the BRT model (ACC = 0.781, AUC = 0.852) outperformed other models and was the ideal choice. Our results also exhibited the importance of selecting suitable mapping units and optimal predictors. Furthermore, the debris flow susceptibility maps of the Sichuan Province were produced, which can provide helpful data for assessing and mitigating debris flow hazards.

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“…First, the SVM method is not oriented to linear features or even features that interact linearly. It can solve nonlinear and high-dimensional pattern recognition problems better than LR [47]. Secondly, SVM is less prone to over fitting in comparison with LR.…”

Section: Discussionmentioning

confidence: 99%

Planet Image-Based Inventorying and Machine Learning-Based Susceptibility Mapping for the Landslides Triggered by the 2018 Mw6.6 Tomakomai, Japan Earthquake

Shao

et al. 2019

Remote Sensing

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The 5 September 2018 (UTC time) Mw6.6 earthquake of Tomakomai, Japan has triggered about 10,000 landslides with high density, causing widespread concern. We attempted to establish a detailed inventory of this slope failure and use proper methods to assess landslide susceptibility in the entire affected area. To this end we applied the logistic regression (LR) and the support vector machine (SVM) for this study. Based on high-resolution (3 m) optical satellite images (planet image) before and after the earthquake, we delineated 9295 individual landslides triggered by the earthquake, occupying an area of 30.96 km2. Ten controlling factors were selected for susceptibility analysis, including elevation, slope angle, aspect, curvature, distances to faults, distances to the epicenter, Peak ground acceleration (PGA), distance to rivers, distances to roads and lithology. Using the LR and SVM, two landslide susceptibility maps were produced for the study area. The results show that in the LR model, the success rate is 84.7% between the landslide susceptibility map and the training dataset, and the prediction rate is 83.9% shown by comparing the test dataset and the landslide susceptibility map. In the SVM model, a success rate of 90.9% exists between the susceptibility map and the test samples, and a prediction rate of 87.1% from comparison of the test dataset and the landslides susceptibility map. In comparison, the performance of the SVM is slightly better than the LR model.

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Susceptibility assessment of landslides triggered by earthquakes in the Western Sichuan Plateau

Cited by 85 publications

References 40 publications

Identifying the Contributions of Multi-Source Data for Winter Wheat Yield Prediction in China

Identifying the Contributions of Multi-Source Data for Winter Wheat Yield Prediction in China

Comparison of Different Machine Learning Methods for Debris Flow Susceptibility Mapping: A Case Study in the Sichuan Province, China

Planet Image-Based Inventorying and Machine Learning-Based Susceptibility Mapping for the Landslides Triggered by the 2018 Mw6.6 Tomakomai, Japan Earthquake

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