The present study aims to develop two hybrid models to optimize the factors and enhance the predictive ability of the landslide susceptibility models. For this, a landslide inventory map was created with 406 historical landslides and 2030 non-landslide points, which was randomly divided into two datasets for model training (70%) and model testing (30%). 22 factors were initially selected to establish a landslide factor database. We applied the GeoDetector and recursive feature elimination method (RFE) to address factor optimization to reduce information redundancy and collinearity in the data. Thereafter, the frequency ratio method, multicollinearity test, and interactive detector were used to analyze and evaluate the optimized factors. Subsequently, the random forest (RF) model was used to create a landslide susceptibility map with original and optimized factors. The resultant hybrid models GeoDetector-RF and RFE-RF were evaluated and compared by the area under the receiver operating characteristic curve (AUC) and accuracy. The accuracy of the two hybrid models (0.868 for GeoDetector-RF and 0.869 for RFE-RF) were higher than that of the RF model (0.860), indicating that the hybrid models with factor optimization have high reliability and predictability. Both RFE-RF GeoDetector-RF had higher AUC values, respectively 0.863 and 0.860, than RF (0.853). These results confirm the ability of factor optimization methods to improve the performance of landslide susceptibility models.

Landslide susceptibility mapping using hybrid random forest with GeoDetector and RFE for factor optimization

As threats of landslide hazards have become gradually more severe in recent decades, studies on landslide prevention and mitigation have attracted widespread attention in relevant domains. A hot research topic has been the ability to predict landslide susceptibility, which can be used to design schemes of land exploitation and urban development in mountainous areas. In this study, the teaching-learning-based optimization (TLBO) and satin bowerbird optimizer (SBO) algorithms were applied to optimize the adaptive neuro-fuzzy inference system (ANFIS) model for landslide susceptibility mapping. In the study area, 152 landslides were identified and randomly divided into two groups as training (70%) and validation (30%) dataset. Additionally, a total of fifteen landslide influencing factors were selected. The relative importance and weights of various influencing factors were determined using the step-wise weight assessment ratio analysis (SWARA) method. Finally, the comprehensive performance of the two models was validated and compared using various indexes, such as the root mean square error (RMSE), processing time, convergence, and area under receiver operating characteristic curves (AUROC). The results demonstrated that the AUROC values of the ANFIS, ANFIS-TLBO and ANFIS-SBO models with the training data were 0.808, 0.785 and 0.755, respectively. In terms of the validation dataset, the ANFIS-SBO model exhibited a higher AUROC value of 0.781, while the AUROC value of the ANFIS-TLBO and ANFIS models were 0.749 and 0.681, respectively. Moreover, the ANFIS-SBO model showed lower RMSE values for the validation dataset, indicating that the SBO algorithm had a better optimization capability. Meanwhile, the processing time and convergence of the ANFIS-SBO model were far superior to those of the ANFIS-TLBO model. Therefore, both the ensemble models proposed in this paper can generate adequate results, and the ANFIS-SBO model is recommended as the more suitable model for landslide susceptibility assessment in the study area considered due to its excellent accuracy and efficiency.

Landslide susceptibility modeling based on ANFIS with teaching-learning-based optimization and Satin bowerbird optimizer

Abstract The machine-learning “black box” models, which lack interpretability, have limited application in landslide susceptibility mapping. To interpret the black-box models, some interpretable machine learning algorithms have been proposed recently. Among them is SHaply Additive ExPlanation (SHAP), which has attracted much attention because of its ease of operation and comprehensiveness. In this study, a novel interpretable model based on SHAP and XGBoost is proposed to interpret landslides susceptibility evaluation at global and local levels. The established evaluation model provided 0.75 accuracy and 0.83 AUC value for the test sets. The global interpretation shows that the peak rainfall intensity and elevation are the dominant factors that influence the occurrence of landslides in the study area. The combination of local interpretation and field investigations can provide a comprehensive framework for evaluating designated landslides, and it can also be used as a reference for preventing and managing the hazards of landslides.

An interpretable model for the susceptibility of rainfall-induced shallow landslides based on SHAP and XGBoost

Simulating land use/land cover change in an arid region with the coupling models

Evaluating the contributions of climate change and human activities to runoff in typical semi-arid area, China

This study investigates the relations between climate change and both runoff and sediment yield in watersheds and provides a scientific basis for water resources planning and design as well as watershed-scale soil and water conservation. The impact of climate change on runoff and sediment yield in a watershed does not occur in isolation, but is a synergistic process in which climate and vegetation jointly influence runoff and sediment yield. Previous studies have seldom addressed this synergistic effect. For this study, a regression model between climate factors (temperature and precipitation) and the Normalized Difference Vegetation Index (NDVI) was established using data from the Zhenjiangguan Watershed in China. By combining data on climate-driven changes in vegetation cover, the Soil and Water Assessment Tool (SWAT) model was built to simulate runoff and sediment yield in the watershed under two scenarios: changes in climate, and synergistic changes in climate and vegetation cover. The simulation results show that precipitation is the most sensitive factor affecting runoff and sediment yield; 10% change in annual precipitation can cause 10%–14% change in annual runoff and 17%–24% change in annual sediment yield. Temperature is also an important factor affecting runoff and sediment yield in the watershed. Each temperature increase of 0.7 °C can result in a decrease of 1.4%–2% in annual runoff and 2%–3.7% in annual sediment yield. This research reveals that accounting for synergetic change in vegetation has an impact on runoff and sediment yield results, and that the magnitude and nature of this influence vary among different combinations of temperature and precipitation changes. When temperature was kept constant, the effect of vegetation cover change caused by precipitation change on runoff and sediment yield was relatively small (only 0.9%–1.5% of the total change), and vegetation cover change inhibited the effects of precipitation change on runoff and sediment yield. When precipitation was kept constant, the effects of vegetation cover change caused by temperature change on runoff and sediment yield were relatively large (20%–30% of the total change), and vegetation cover change enhanced the effects of temperature change on runoff and sediment yield. This investigation considers the synergistic effects of climate and vegetation cover change, and thus further clarifies the extent to which climate change impacts water and ecological resources.

Impacts on watershed-scale runoff and sediment yield resulting from synergetic changes in climate and vegetation

Exploration of the temporal and spatial evolutionary trends of future extreme precipitation under different emission scenarios is of considerable importance for promoting active response to climate change. Based on the output of CMIP6 global climate models (GCMs) with four Shared Socioeconomic Pathways (SSPs), precipitation data for the Jialing River Basin (China) were obtained through CMhyd downscaling and correction. A relative threshold of 95 percentile of each year was used to extract extreme precipitation in the basin, and historical observational data were used to verify the capability of the GCMs to simulate extreme precipitation. Based on Extreme-point Symmetric Mode Decomposition, the trends and periodic characteristics of future extreme precipitation in the Jialing River Basin under four different emission scenarios (SSP2.6, SSP4.5, SSP7.0, and SSP8.5) were simulated and analyzed. The future temporal and spatial distributions of extreme precipitation frequency and intensity in different periods were described. The results revealed the following. (1) Future extreme precipitation in the Jialing River Basin is likely to increase with rates of 21.8 mm/10a, 11.4 mm/10a, 8.7 mm/10a, and 8.3 mm/10a for SSP8.5, SSP 7.0, SSP4.5, and SSP2.6, respectively. (2) The frequency of extreme precipitation decreased in the whole basin with a 1-day decrease of 3.88 % (SSP8.5), 7.11 % (SSP7.0), 5.66 % (SSP4.5), and 2.40 % (SSP2.6) from historical levels. (3) The intensity of extreme precipitation increased with a 1-day increase of 11.18 % (SSP8.5), 6.80 % (SSP7.0), 5.04 % (SSP4.5), and 4.57 % (SSP2.6) from historical levels. Over time, the change of extreme precipitation showed strong periodic characteristics with three periodic components of 2–3, 4–6, and 9–12 years. In terms of spatial distribution, the frequency (intensity) center of extreme precipitation in the next 80 years will be distributed in the south (southeast) of the basin. Compared with historical observations, the area of extreme precipitation intensity in the future accounted for a larger proportion, while the area of extreme precipitation frequency accounted for a smaller proportion. In terms of increased emission levels, the extreme precipitation frequency would decrease and the intensity would increase. The conclusions of this study could provide a reference for disaster prevention and mitigation of the effects of extreme precipitation under future climate change. 

Temporal and spatial evolutionary trends of regional extreme precipitation under different emission scenarios: Case study of the Jialing River Basin, China

Effect of the quality of streamflow forecasts on the operation of cascade hydropower stations using stochastic optimization models

Synergetic impact of climate and vegetation cover on runoff, sediment, and nitrogen and phosphorus losses in the Jialing River Basin, China

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Impacts on watershed-scale runoff and sediment yield resulting from synergetic changes in climate and vegetation

Temporal and spatial evolutionary trends of regional extreme precipitation under different emission scenarios: Case study of the Jialing River Basin, China

Effect of the quality of streamflow forecasts on the operation of cascade hydropower stations using stochastic optimization models

Synergetic impact of climate and vegetation cover on runoff, sediment, and nitrogen and phosphorus losses in the Jialing River Basin, China