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Tsunamis, generated by submarine earthquakes, landslides, or volcanic eruptions, are a significant hazard to coastal areas owing to their sudden onset and rapid propagation speed. Thus, tsunami disaster risk assessment is crucial to determine potential losses and severity of future tsunami impacts, providing essential support for disaster prevention and mitigation efforts. This study presents a case study of Xiamen City in Fujian province for developing a high-precision tsunami model. Utilizing nearshore water depth and digital elevation model data, we established a high-resolution tsunami numerical simulation model for Xiamen City that accounts for the impact of water overrunning levees and overshoals. Historical tsunami disaster records were used to validate the numerical model. By determining multiple tsunami source scenarios that could potentially affect the counties of Xiamen City, we simulated the inundation range and water depth distribution required for the potential maximum tsunami event. The simulation results facilitated a tsunami hazard assessment. Considering land use and important tsunami-affected exposures including industrial and chemical enterprises and ports, we evaluated the vulnerability of Xiamen City to tsunami disasters. Based on the determination of hazard and vulnerability levels, we investigated the risk distribution of tsunami disasters in Xiamen City. The results of this study lay the groundwork for developing methodologies to improve tsunami disaster risk assessment in coastal areas.

Landslide disasters, due to their widespread distribution and clustered occurrences, pose a significant threat to human society. Rainfall is considered a primary triggering factor, and the frequent clustering of landslides underscores the importance of early warning systems for regional landslide disasters in preventing and mitigating rainfall-induced landslides. Research on early warning models is crucial for accurately predicting rainfall-induced landslides. However, traditional models face challenges such as the complexity of landslide causes, insufficient data, and limited analysis methods, resulting in low accuracy and inadequate precision. This study focuses on Fujian Province, China, proposing a four-step process for building a regional landslide early warning model based on machine learning. The process includes data integration and cleaning, sample set construction, model training and validation, and practical application. By integrating and cleaning the latest and most detailed data, a training sample set (15,589 samples) for the regional landslide disaster early warning model is established. Three machine learning algorithms—Random Forest, Multilayer Perceptron, and Convolutional Neural Network—are employed and compared, the evaluation results indicated that the RF-based warning model achieved an accuracy of 0.930–0.957 and an AUC value of 0.955. The CNN-based warning model demonstrated an accuracy of 0.945–0.948 with an AUC value of 0.940. The MLP-based warning model achieved an accuracy of 0.930–0.953 and an AUC value of 0.930. The results showed comparable accuracy metrics among the three models, with RF exhibiting a significant advantage in AUC values. Finally, the models are applied to the regional landslide disasters induced by heavy rainfall in Fujian Province on 5 August 2021. The results showed that in the binary classification warning strategy, the accuracy of the Random Forest and Convolutional Neural Network was 92.9%, while that of the Multilayer Perceptron was 85.8%, all performing well. In the multi-classification hierarchical warning strategy, the Random Forest excelled, while the performance of the Convolutional Neural Network and Multilayer Perceptron was relatively limited. The findings of this study contribute to valuable attempts in landslide disaster warning model research, with anticipated further improvements through the gradual accumulation of samples and practical application verification.

Landslide disasters occur frequently in Fujian Province, and early warning of landslide disasters on a regional scale is an important means of effective disaster prevention and mitigation. Due to the complex mechanism of landslide disasters, the traditional regional landslide early warning methods have such problems as insufficient accuracy. Deep learning mainly refers to the technology of feature extraction, abstraction, representation and learning by constructing the neural network model, which is a kind of machine learning. As a classical deep learning algorithm, convolutional neural network has more powerful classification and representation ability than traditional machine learning. Taking Fujian Province as the research area, this paper introduces the convolution neural network into the field of landslide disaster early warning and constructs a regional landslide early warning model of Fujian Province. The process is as follows: (1) The SMOTE optimization algorithm is used to optimize the sample database of landslide disasters in Fujian Province from 2010 to 2018, enlarging the number of positive samples and expanding the proportion of positive and negative samples from 1∶3.4 to 1∶2, and the total number of samples reaches 18040. (2) Construct a convolution neural network model structure, which includes an input layer, two convolution layers, two maximum pooling layers, a full connection layer and an output layer. (3) Use the convolution neural network to train the optimized samples (80% of the samples from 2010 to 2018 as the training set), and use the Bayesian optimization algorithm to optimize the model parameters to obtain the regional landslide early warning model of Fujian Province. (4) The model is tested with 20% of the samples from 2010 to 2018 as the test set, and the confusion matrix and ROC curve are used to test the model. The results show that the accuracy of the model ranges from 0.96 to 0.97, the AUC value is 0.977, indicating that the model accuracy and generalization ability are good. (5) The actual situation of the landslide disaster in the flood season of 2019 is taken as a positive sample, negative samples are collected through the method of time-space sampling, and the 2019 regional landslide sample verification set (603 samples) is constructed. The model is further verified by using the confusion matrix and ROC curve. The results show that the accuracy of the model ranges from 0.75 to 0.85, and the AUC value is 0.852. Although only the actual landslide samples in the flood season of 2019 is used for verification, good results is also achieved. In this paper, the convolution neural network algorithm is applied to the regional landslide early warning, which provides a new way to establish the regional landslide early warning model. The preliminary verification shows that the model is effective and will be further applied and verified in Fujian Province in the future.

Island beaches, once mistakenly considered as 'ecological deserts', are actually unique ecosystems that support abundant macrofaunal communities. However, limited studies have examined the variations and determinants of macrofaunal communities across the intertidal zones on island beaches, which is crucial for shedding light on the underlying ecological processes and enhancing biodiversity conservation efforts. Here, we explored the variations in macrofaunal communities along sand physico-chemical gradient across four intertidal zones on nine representative beaches, within the largest land-bridge archipelago in eastern China. Our findings revealed that species richness, density, and Shannon-Wiener diversity of macrofaunal communities were notably higher in the lower intertidal zones, exhibiting a positive correlation with elevated levels of sand moisture (M), electrical conductivity (EC), available potassium (AK), and grain size (GS). Conversely, macrofaunal Simpson diversity displayed an inverse pattern, with heightened values observed in the vegetation zone. The Saprophagous, Predaceous, and Omnivorous groups of macrofaunal communities demonstrated an increase with higher levels of EC, AK, and M in the lower intertidal zones. Furthermore, Phytophagous group of macrofaunal communities tended to inhabit the vegetation zones characterized by elevated levels of sand total phosphorus (TP), total nitrogen (TN), and organic carbon (OC). Primarily, our findings indicate that sand nutrition, moisture, and salinity play pivotal roles in shaping the variations of macrofaunal communities on island beaches. These insights offer valuable perspectives into the holistic responses of macrofauna on island beaches to changes in sand physico-chemical properties, serving as a crucial theoretical foundation for the assessment, conservation, and restoration of island beaches.

Variations in illumination and image background present challenges for using UAV RGB imagery. Existing studies often overlook these issues, especially in rice. To separately evaluate the impacts of illumination variation and image background on rice LAI assessment, this study utilized Retinex correction and image segmentation to eliminate illumination variations and background effects, and then analyzed the changes in color indices and their relationship with LAI before and after implementing these methods separately. The results indicated that both Retinex correction and image segmentation significantly enhanced the correlation between color indices and LAI at different growth stages as well as the accuracy of constructing a multivariate linear regression model separately. Our analysis confirmed the significance of accounting for variation in illumination and rice field backgrounds in LAI analysis when using UAV RGB images. Illumination variation and image background elements significantly degrade the accuracy of LAI estimation.

China’s landslide disasters are serious, and regional landslide disaster early-warning is one of the important means of disaster prevention and mitigation. The traditional regional landslide disaster early-warning model, however, is limited by the complex landslide induction mechanism, limited data accumulation, and insufficient big data analysis methods, and has problems such as limited early-warning accuracy and insufficient refinement. In this paper, a machine learning method was introduced into the field of regional landslide disaster warning. From the model construction process of training sample-set construction, sample learning and training, model parameter optimization, model preservation, warning output, and so on, a method for constructing a regional landslide early-warning model based on machine learning was systematically proposed. In the sample learning and training, 80% of the training sample-set was used as the training set, and 20% was used as the test set for five-fold cross validation. The Bayesian Optimization algorithm was used to optimize the model parameters, and the accuracy, ROC curve, and AUC value were used to verify the model accuracy and model generalization ability. With China’s Fujian province as an example, based on nine years of geological and meteorological data (2010–2018), geological environment factors, factors of hazard-affected bodies and historical disaster situations, and rainfall-induced factors in four categories, a total of 26 indicators were used as input characteristic parameters. Six machine learning algorithms were adopted to improve model training; the results showed that the Random Forest algorithm performed the best, giving an accuracy of 92.3%, and was the model with the best generalization ability (AUC was 0.955). The second best was the Artificial Neural Network model, with an accuracy of 0.937 and an AUC of 0.935. Next were the Nearest Neighbor model, the Logistic Regression model, and the Support Vector Machine; the poorest results were from the Decision Tree model. Finally, the typical rainfall-type landslide disaster process in Fujian Province was selected as an example to verify the Random Forest algorithm model. The results showed that compared with the early-warning results of the original explicit statistical model, the hit rate of the new model was 6 times, or equal to that of the original model, and the landslide density in the early-warning area of the new model was 1.6–1.7 times that of the original model. Preliminary verification showed that the new model based on the Random Forest method has obvious advantages, a higher hit rate and a smaller warning area, and can achieve more accurate warnings. The follow-up will continue to track the new landslide disaster situation in the study area and carry out model verification and correction.

Based on the background of Puyang Jiang River, a 2D hydrodynamic model was built to explore the distribution characteristics of tidal range in the downstream section of Puyang River under different flood tide combinations between Puyang River and Qiantang River. The influence of different frequency floods and flood discharges of Puyang River on the flood level of Puyang River was analysed. The results show that the tidal range at each point of the exit section decreases with the increase of floods in Puyang River. Under the same flood conditions of Puyang River, the tidal range at each point of the river increases with the increase of the Qiantang River rushing tide; under the same characteristic tidal conditions, the tidal gradient along the water flow direction of the Sanjiangkou-Xizhuang section increases with the increase of floods in the Puyang River. When Puyang Jiang River suffered flooding from once ten years to twenty years and then to fifty years, the mean value of water level reduction of east and west river is from 0.66 cm to 0.72 cm and then to 0.63 cm, namely, the sensitivity of the discharge of Gaohu flood retarding basin to the water level of east and west river of Puyang Jiang River is first strengthened and then weakened. The research results provide a guidance for flood regulation of Puyang River and optimization of operation in Gaohu flood detention area. [ABSTRACT FROM AUTHOR]