Landslide susceptibility assessment based on frequency ratio and semi-supervised heterogeneous ensemble learning model.

Epistemic uncertainty in data-driven landslide susceptibility assessment often tends to be increased by the limited accuracy of an individual model, as well as uncertainties associated with the selection of non-landslide samples. To address these issues, this paper centers on the landslide disaster in Ji'an City, China, and proposes a heterogeneous ensemble learning method incorporating frequency ratio (FR) and semi-supervised sample expansion. Based on the superimposed results of 12 environmental factor frequency ratios (FFR), non-landslide samples were selected and input into light gradient boosting machine (LightGBM), random forest (RF), and convolutional neural network (CNN) models for prediction along with historical landslide samples. The predicted probability values are integrated by four heterogeneous ensemble strategies to expand samples from high-confidence results. The model's performance is evaluated using the area under the receiver operating characteristic curve (AUC), partition frequency ratio (PFR), and other verification methods. The results demonstrate that the negative sample based on FFR sampling is more accurate than the random sampling method, and the FR-SSELR model based on frequency ratio sampling and semi-supervised ensemble strategy exhibits the highest performance (AUC = 0.971, ACC = 0.941). A more reasonable landslide susceptibility map was drawn based on this model, with the lowest percentage of landslides in the low and very low susceptibility zones (sum of PFR = 0.194), as well as the highest percentage of landslides in the high and very high susceptibility zones (sum of PFR = 6.800). Furthermore, the FR-SSELR model improved economic benefits by 3.82-14.2%, offering valuable guidance for decision-making regarding landslide management and the sustainability of Ji'an City.

Debris flow susceptibility assessment based on information value and machine learning coupling method: from the perspective of sustainable development.

Accurately assessing the susceptibility of debris flow disasters is of great significance for reducing the cost of disaster prevention and mitigation, as well as disaster losses. Machine learning (ML) models have been widely used in the susceptibility assessment of debris flow disasters. However, these models often have randomness in the selection of non-disaster data, which can lead to redundant information and poor applicability and accuracy of susceptibility evaluation results. To address this issue, this paper focuses on debris flow disasters in Yongji County, Jilin Province, China; optimizes the sampling method of non-disaster datasets in machine learning susceptibility assessment; and proposes a susceptibility prediction model that couples information value (IV) with artificial neural network (ANN) and logistic regression (LR) models. A debris flow disaster susceptibility distribution map with higher accuracy was drawn based on this model. The model's performance is evaluated using the area under the receiver operating characteristic curve (AUC), information gain ratio (IGR), and typical disaster point verification methods. The results show that the rainfall and topography were found to be decisive factors in the occurrence of debris flow disasters, and the IV-ANN model established in this study had the highest accuracy (AUC = 0.968). Compared to traditional machine learning models, the coupling model produced an increase in economic benefit of about 25% while reducing the average disaster prevention and control investment cost by about 8%. Based on model's susceptibility map, this paper proposes practical disaster prevention and control suggestions that promote sustainable development in the region, such as establishing monitoring systems and information platforms to aid disaster management.

The Influence of Different Knowledge-Driven Methods on Landslide Susceptibility Mapping: A Case Study in the Changbai Mountain Area, Northeast China.

Landslides are one of the most frequent geomorphic hazards, and they often result in the loss of property and human life in the Changbai Mountain area (CMA), Northeast China. The objective of this study was to produce and compare landslide susceptibility maps for the CMA using an information content model (ICM) with three knowledge-driven methods (the artificial hierarchy process with the ICM (AHP-ICM), the entropy weight method with the ICM (EWM-ICM), and the rough set with the ICM (RS-ICM)) and to explore the influence of different knowledge-driven methods for a series of parameters on the accuracy of landslide susceptibility mapping (LSM). In this research, the landslide inventory data (145 landslides) were randomly divided into a training dataset: 70% (81 landslides) were used for training the models and 30% (35 landslides) were used for validation. In addition, 13 layers of landslide conditioning factors, namely, altitude, slope gradient, slope aspect, lithology, distance to faults, distance to roads, distance to rivers, annual precipitation, land type, normalized difference vegetation index (NDVI), topographic wetness index (TWI), plan curvature, and profile curvature, were taken as independent, causal predictors. Landslide susceptibility maps were developed using the ICM, RS-ICM, AHP-ICM, and EWM-ICM, in which weights were assigned to every conditioning factor. The resultant susceptibility was validated using the area under the ROC curve (AUC) method. The success accuracies of the landslide susceptibility maps produced by the ICM, RS-ICM, AHP-ICM, and EWM-ICM methods were 0.931, 0.939, 0.912, and 0.883, respectively, with prediction accuracy rates of 0.926, 0.927, 0.917, and 0.878 for the ICM, RS-ICM, AHP-ICM, and EWM-ICM, respectively. Hence, it can be concluded that the four models used in this study gave close results, with the RS-ICM exhibiting the best performance in landslide susceptibility mapping.

A Method for Improving Controlling Factors Based on Information Fusion for Debris Flow Susceptibility Mapping: A Case Study in Jilin Province, China.

Debris flow is one of the most frequently occurring geological disasters in Jilin province, China, and such disasters often result in the loss of human life and property. The objective of this study is to propose and verify an information fusion (IF) method in order to improve the factors controlling debris flow as well as the accuracy of the debris flow susceptibility map. Nine layers of factors controlling debris flow (i.e., topography, elevation, annual precipitation, distance to water system, slope angle, slope aspect, population density, lithology and vegetation coverage) were taken as the predictors. The controlling factors were improved by using the IF method. Based on the original controlling factors and the improved controlling factors, debris flow susceptibility maps were developed while using the statistical index (SI) model, the analytic hierarchy process (AHP) model, the random forest (RF) model, and their four integrated models. The results were compared using receiver operating characteristic (ROC) curve, and the spatial consistency of the debris flow susceptibility maps was analyzed while using Spearman's rank correlation coefficients. The results show that the IF method that was used to improve the controlling factors can effectively enhance the performance of the debris flow susceptibility maps, with the IF-SI-RF model exhibiting the best performance in terms of debris flow susceptibility mapping.

Combined System of Magnetic Resonance Sounding and Time-Domain Electromagnetic Method for Water-Induced Disaster Detection in Tunnels.

Underground construction projects such as tunnel construction are at high risk of water-induced disasters. Because this type of disaster poses a serious threat to worker safety and productivity, instruments and methods that can accurately detect the water source are critical. In this study, a water detection instrument that combines Magnetic Resonance Sounding (MRS) and Time-domain Electromagnetic Method (TEM) techniques to yield a joint MRS-TEM interpretation method was developed for narrow underground spaces such as tunnels. Joint modules including a transmitter and receiver were developed based on a dual-purpose and modular design concept to minimize the size and weight of the instrument and consequently facilitate transportation and measurement. Additionally, wireless control and communication technology was implemented to enable inter-module cooperation and simplify instrument wiring, and wireless synchronization was accomplished by implementing a Global Positioning System (GPS)-based timing scheme. The effectiveness and reliability of the instrument were verified via indoor laboratory tests and field measurement signal tests. Furthermore, the practicability of the combined instrument and its interpretation method was verified via a field case performed in a tunnel in Hubei, China.

Response Characteristics and Experimental Study of Underground Magnetic Resonance Sounding Using a Small-Coil Sensor.

Due to its unique sensitivity to hydrogen protons, magnetic resonance sounding (MRS) is the only geophysical method that directly detects water and can provide nondestructive information on subsurface aquifer properties. The relationship between the surface MRS signal and the location and characteristics of aquifers using large-coil (typically 50-150 m) sensors has been discussed based on forward modelling and experiments. However, few researchers have studied underground MRS using a small-coil sensor. In this paper, a parametric study and a large-scale physical model test were conducted to shed light on the critical response characteristics of underground MRS using a small-coil sensor. The effects of the size and number of turns of the transmitter coil and receiver coil, the geomagnetic declination, the geomagnetic inclination, and the position, thickness, and water content of a water-bearing structure on the performance of the underground MRS were studied based on numerical simulations. Furthermore, we derived the kernel function and underground MRS signal curves for a water-bearing structure model based on the simulations. Finally, a large-scale physical model test on underground MRS using a small-coil sensor was performed using a physical test system for geological prediction of tunnels at Shandong University. The results show that the inversion results of the physical model test were in good agreement with the physical prototype results. Using both numerical modeling and physical model tests, this study showed that underground MRS using a small-coil sensor can be used to predict water-bearing structures in underground engineering.

[Dynamics of fluvo-aquic soil organic matter fractions under long-term fertilization].

By the method of relative density fractionation, this paper studied the dynamics of organic matter and its light and heavy fractions in a fluvo-aquic soil under long-term fertilization. The results indicated under current fertilization system, the contents of soil organic matter and its light and heavy fractions were basically unchanged within 13 successive years of no fertilization, but had an increasing trend with the duration of chemical fertilizer NPK and organic manure applications, with a larger fluctuation among years and a less increment in treatment NPK. Regression analysis showed that soil organic matter and its light and heavy fractions had a linear correlation with the duration of fertilization in treatment NPK, and had a logarithm correlation in treatment organic manure.

[Chemical depletion of cumulative phosphorus in soils].

Cumulative phosphorus was defined as the phosphorus which was unavailable for plants and accumulated in soils fertilizer application. In this paper, chemical depletion of cumulative phosphorus in soils was studied by methods of batch equilibrium, kinetic, and anionic exchange resin membrane. The results showed that desorption amount of cumulative P increased with time increasing, and the desorption process was consonant with second-order kinetic equation. Release rate of P in different treatments was in order of PK > NPK > NK. The amount and rate of P released from red soil were higher than those of Fluvio-aquic soil. Desorbed P was significantly correlated with soil available P and P uptake by Plant. The maximum utilization of cumulative P in soils was about 45% of total phosphorus.