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Objective Strong earthquake-induced landslides are characterized by large number, wide distribution and large scale, and seriously threaten people's lives and property. Landslide susceptibility mapping (LSM) can quickly predict the spatial distribution of prone areas, which is highly important for reducing the risk of post-earthquake disasters. However, in the studies of coseismic landslide LSMs, how to select negative landslide samples and integrate machine learning models to improve the evaluation accuracy still needs further investigation. Methods In this study, the landslides induced by the Wenchuan earthquake in mountainous areas are selected as a case study. First, 10 landslide influencing factors, such as topography, geological environment, and seismic parameters, are selected to analyse the spatial distribution of landslides. Then, collinearity analysis is used to test data redundancy, nonnegative sample points from the sampling strategies are randomly selected in the extremely low susceptibility regions by the frequency ratio (FR) method. Finally, gradient boosting decision tree (GBDT), random forest (RF), and their optimal models are used to predict coseismic landslide susceptibility, conduct a comparative study of the models and carry out an accuracy assessment. Results The results show that ① the spatial distribution of landslides is controlled by multiple factors, and ② the accuracy of the models is FR-RF(AUC=0.943)>FR-GBDT(AUC=0.926)>RF(AUC=0.901)>GBDT(AUC=0.856). ③ Selecting negative landslide samples in low susceptibility areas could significantly improve the accuracy of LSMs. Conclusion The research results can provide a reference for selecting negative landslide samples and constructing evaluation models, as well as for providing theoretical support for post-earthquake disaster prevention and mitigation.

This study explored 1-year follow-up of Parmaco-invasive strategy with half-dose recombinant human prourokinase (PHDP) in patients with acute ST-segment elevation myocardial infarction (STEMI). The follow-up endpoints were major adverse cardiovascular events (MACEs) occurring within 30 days and 1 year, as well as postoperative bleeding events. The study ultimately included 150 subjects, with 75 in the primary percutaneous coronary intervention (PPCI) group and 75 in the PHDP group. This study found that the PHDP group had a shorter FMC-reperfusion time (42.00 min vs 96.00 min, P 0.05). The PHDP facilitates early treatment of infarct-related vessels, shortens FMC-reperfusion time, and does not increase the risk of MACEs.

Landslide susceptibility mapping (LSM) is a crucial step in quantitatively assessing landslide risk, essential for geologic hazards prevention. With the rapid development of deep learning models, convolutional neural networks (CNNs), and transformer architectures have been applied to LSM. However, these models still face the challenges of suboptimal mapping accuracy and limited capacity for multilevel landslide features extraction. In this study, we present a CNN-transformer local-global features extraction network (CTLGNet) that combines the strengths of both CNN and transformer models to effectively extract both landslide local and global features. We apply this model to LSM in two regions: the Three Gorges Reservoir area and Jiuzhaigou. To begin, nine landslide conditioning factors are selected and analyzed to construct the landslide dataset for LSM. Subsequently, the dataset is randomly split into training, validation, and test datasets in a 6:2:2 ratio to attain LSM results. Then, CTLGNet is compared to CNN, residual neural network, densely connected convolutional network, vision transformer, and fractional Fourier image transformer using various evaluation metrics. The results demonstrate that CTLGNet exhibits exceptional landslide prediction and generalization capabilities, outperforming the other five models across all evaluation metrics except Recall, with AUC values of 0.9817 and 0.9693 for the two regions, respectively. The LSM results indicate that CTLGNet can effectively extract both landslide local and global features to achieve landslide localization and detail capture. Overall, our proposed framework excels in extracting multilevel landslide features and holds great potential for widespread application.

Landslide susceptibility assessment (LSA) holds crucial importance in guiding regional disaster prevention and reduction efforts. However, current deep learning (DL) models for LSA encounter challenges like insufficient landslide data samples and uneven distribution. In this article, we develop a new hybrid framework named landslide conditioning factors and swin transformer ensemble (LCFSTE), which integrates landslide conditioning factors (LCFs) and swin transformer (swin-T) for LSA. With this framework, we fully leverage the powerful nonlinear feature extraction capability of swin-T to extract abstract features from both landslides and LCFs. This approach ultimately enhances the precision and reliability of LSA. To assess the performance of our newly proposed framework, we selected Jiuzhaigou County, China, as our study area. First, a dataset for LSA was constructed using historical landslide data and 11 multisource LCFs. Then, these factors were screened through a multicollinearity test and factor importance analysis using variance inflation factor, tolerance, and information gain rate. Subsequently, the dataset was divided into three subsets: 60% for training, 20% for validation, and 20% for testing. Then, the LSA results were compared with four DL models. Seven evaluation metrics (EMs) are chosen to quantitatively evaluate the performance of these five LSA models. The results demonstrated that, among these seven EMs, LCFSTE outperformed the others, achieving the highest score in six out of the seven considered EMs. This outcome highlights the promising applicability of LCFSTE in enhancing LSA accuracy.

Landslides are catastrophic geological events that can cause significant damage to properties and result in the loss of human lives. Deep-learning technology applied to optical remote sensing images can enable effective landslide-prone area detection. However, conventional landslide detection (LD) models often employ complex structural designs to ensure detection accuracy. The complexity often hampers the detection speed, rendering these models inadequate for the swift emergency monitoring of landslides. To address these problems, we propose a new lightweight deep-learning-based framework, BisDeNet, for efficient LD. To improve the efficiency of the proposed BisDeNet, we replaced the context path in the original BiSeNet with DenseNet due to its strong feature extraction ability, few required parameters, and low model complexity. Two sites with different and representative landslide developments were selected as the study areas to verify the performance of our proposed BisDeNet. Additionally, we introduced landslide causative factors to enhance the sampling dataset. To evaluate the effectiveness of our approach, we compared the performance of our BisDeNet with the performances of three other BiSeNet-based methods and an advanced transformer-based model data-efficient image transformer (DeiT). Our experimental results indicate that the F1-scores of BisDeNet in the two study areas are 0.9006 and 0.8850, which are 26.22% and 1.86% higher than the scores of BiSeNet, respectively, but slightly lower than that of the DeiT model. Furthermore, our proposed BisDeNet requires the fewest number of parameters and the least memory out of the five models.

Landslides are one of the geological disasters with wide distribution, high impact and serious damage around the world. Landslide risk assessment can help us know the risk of landslides occurring, which is an effective way to prevent landslide disasters in advance. In recent decades, artificial intelligence (AI) has developed rapidly and has been used in a wide range of applications, especially for natural hazards. Based on the published literatures, this paper presents a detailed review of AI applications in landslide risk assessment. Three key areas where the application of AI is prominent are identified, including landslide detection, landslide susceptibility assessment, and prediction of landslide displacement. Machine learning (ML) containing deep learning (DL) has emerged as the primary technology which has been considered successfully due to its ability to quantify complex nonlinear relationships of soil structures and landslide predisposing factors. Among the algorithms, convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are two models that are most widely used with satisfactory results in landslide risk assessment. The generalization ability, sampling training strategies, and hyper-parameters optimization of these models are crucial and should be carefully considered. The challenges and opportunities of AI applications are also fully discussed to provide suggestions for future research in landslide risk assessment.

The accurate prediction of landslide susceptibility relies on effectively handling landslide absence samples in machine learning (ML) models. However, existing research tends to generate these samples in feature space, posing challenges in field validation, or using physics-informed models, thereby limiting their applicability. The rapid progress of interferometric synthetic aperture radar (InSAR) technology may bridge this gap by offering satellite images with extensive area coverage and precise surface deformation measurements at millimeter scales. Here, we propose an InSAR-based sampling strategy to generate absence samples for landslide susceptibility mapping in the Badong–Zigui area near the Three Gorges Reservoir, China. We achieve this by employing a Small Baseline Subset (SBAS) InSAR to generate the annual average ground deformation. Subsequently, we select absence samples from slopes with very slow deformation. Logistic regression, support vector machine, and random forest models demonstrate improvement when using InSAR-based absence samples, indicating enhanced accuracy in reflecting non-landslide conditions. Furthermore, we compare different integration methods to integrate InSAR into ML models, including absence sampling, joint training, overlay weights, and their combination, finding that utilizing all three methods simultaneously optimally improves landslide susceptibility models.

This study investigated the efficacy and safety of pharmaco-invasive strategy with half-dose recombinant human prourokinase (PHDP) during hospitalization for patients with ST-segment elevation myocardial infarction (STEMI) to provide references for the treatment of STEMI. Patients with STEMI who fulfilled the inclusion and exclusion criteria and attended Chengde Central Hospital, Hebei Province, China, between September 3, 2019, and December 28, 2021, were included in this study. The experimental group received PHDP and the control group underwent primary percutaneous coronary intervention (PPCI). This study enrolled 150 patients with STEMI, 75 in the experimental group and 75 in the control group. Coronary angiography revealed successful thrombolysis in 64 (85.33%) patients. Compared with the control group, the experimental group had shorter first medical contact-reperfusion time ( P

Landslide susceptibility mapping (LSM) research is vital for averting and mitigating regional landslide disasters. Nevertheless, there has been a lack of systematic analysis regarding LSM's developmental drivers. Utilizing SciMAT, a scientometric tool, we analyzed 1661 papers on LSM from the Web of Science core collection database, spanning 1993 to 2022. We employed cluster and thematic evolution analysis in SciMAT to unveil trends. The results indicate a consistent upward trend over the past three decades. LSM modeling methods, geological data, and contributing factors are major focal points. Notably, the evolution of LSM models, with a rising adoption of machine learning and deep learning for risk assessment, emerges as a central knowledge pathway. This study offers valuable insights to scholars by identifying literature gaps and highlighting crucial research directions, facilitating informed decision-making in the realm of LSM.

Landslide disasters pose a significant threat, with their highly destructive nature underscoring the critical importance of timely and accurate recognition for effective early warning systems and emergency response efforts. In recent years, substantial advancements have been made in the realm of landslide recognition (LR) based on remote sensing data, leveraging deep learning techniques. However, the intricate and varied environments in which landslides occur often present challenges in detecting subtle changes, especially when relying solely on optical remote sensing images. InSAR (Interferometric Synthetic Aperture Radar) technology emerges as a valuable tool for LR, providing more detailed ground deformation data and enhancing the theoretical foundation. To harness the slow deformation characteristics of landslides, we developed the FCADenseNet model. This model is designed to learn features and patterns within ground deformation data, with a specific focus on improving LR. A noteworthy aspect of our model is the integration of an attention mechanism, which considers various monitoring factors. This holistic approach enables the comprehensive detection of landslide disasters across entire watersheds, providing valuable information on landslide hazards. Our experimental results demonstrate the effectiveness of the FCADenseNet model, with an F1-score of 0.7611, which is 9.53% higher than that of FC_DenseNet. This study substantiates the feasibility and efficacy of combining InSAR with deep learning methods for LR. The insights gained from this research contribute to the advancement of regional landslide geological hazard monitoring, identification, and prevention strategies.

Geohazards, such as landslides, rock avalanches, debris flow, ground fissures, and ground subsidence, pose significant threats to people’s lives and property [...]

Water percolation usually occurs in soil, making it highly challenging to simultaneously monitor and verify the water percolation process in real-time. We propose employing electrical resistivity tomography and the photography technique, respectively, to visualize and validate water percolation. An experiment was conducted to monitor water percolation in sand within a transparent glass trough using electrical resistivity tomography (ERT) and photography techniques. The experimental results show that the relatively low-resistivity anomalies in the electrical resistivity profiles and correction electrical resistivity profiles, derived from the experimental data, have a half-ellipse shape, while the wetting areas captured in the photographs exhibit a bulb-shaped pattern. The relatively low-electrical-resistivity anomaly areas delineated by the 2000 Ω·m contour line in the electrical resistivity profiles and the 120 Ω·m contour line in the correction electrical resistivity profiles exhibit a remarkable correspondence with the wetting areas captured in the simultaneous photographs. Our findings show that the ERT and photography techniques are suitable for the real-time monitoring and simultaneous verification of water percolation in sand within a narrow glass trough.

In a typical GNSS receiver, pseudorange and pseudorange rate measurements are generated through the code and carrier tracking loops, respectively. These measurements are then employed to calculate the user’s position and velocity (PV) solutions, which is typically achieved using a Kalman filter (KF) or the least squares (LS) algorithm. However, the LS method only uses the current observation without error analysis. The positioning result is greatly affected by the errors in the observed data. In KF, by using an iterative approach that combines predictions and measurements of PV information, more accurate estimates can be obtained because the PV information is time-correlated. Meanwhile, its optimal estimate requires that both the model and noise statistics are exactly known. Otherwise, achieving optimality cannot be guaranteed. To address this issue, this paper proposes and implements a novel GNSS solution method based on an unbiased finite impulse response (UFIR) filter. Two different field tests were conducted. The position results of UFIR are compared with those from the LS and KF methods, and the horizon positioning mean error is improved by 44% and 29%, respectively, which highlights its efficacy. The method offers two primary benefits: it is robust to noise uncertainty, and it leverages historical data within the UFIR framework to provide a more accurate estimate of the current state.

Abstract Background Asf1 is a well-conserved histone chaperone that regulates multiple cellular processes in different species. Two paralogous genes, Asf1a and Asf1b exist in mammals, but their role during fertilization and early embryogenesis remains to be investigated further. Methods We analyzed the dynamics of histone chaperone Asf1a and Asf1b in oocytes and pre-implantation embryos in mice by immunofluorescence and real-time quantitative PCR, and further investigated the role of Asf1a and Asf1b during fertilization and pre-implantation development by specific Morpholino oligos-mediated knock down approach. Results Immunofluorescence with specific antibodies revealed that both Asf1a and Asf1b were deposited in the nuclei of fully grown oocytes, accumulated abundantly in zygote and 2-cell embryonic nuclei, but turned low at 4-cell stage embryos. In contrast to the weak but definite nuclear deposition of Asf1a, Asf1b disappeared from embryonic nuclei at morula and blastocyst stages. The knockdown of Asf1a and Asf1b by specific Morpholino oligos revealed that Asf1a but not Asf1b was required for the histone H3.3 assembly in paternal pronucleus. However, knockdown of either Asf1a or Asf1b expression decreased developmental potential of pre-implantation embryos. Furthermore, while Asf1a KD severely reduced H3K56 acetylation level and the expression of Oct4 in blastocyst stage embryos, Asf1b KD almost eliminated nuclear accumulation of proliferating cell marker-PCNA in morula stage embryos. These results suggested that histone chaperone Asf1a and Asf1b play distinct roles during fertilization and pre-implantation development in mice. Conclusions Our data suggested that both Asf1a and Asf1b are required for pre-implantation embryonic development. Asf1a regulates H3K56ac levels and Oct4 expression, while Asf1b safeguards pre-implantation embryo development by regulating cell proliferation. We also showed that Asf1a, but not Asf1b, was necessary for the assembly of histone H3.3 in paternal pronuclei after fertilization.

Accurate prediction of landslide susceptibility relies on effectively handling absence samples in data-driven models. This study investigates the influence of different absence sampling methods, including buffer control sampling (BCS), controlled target space exteriorization sampling (CTSES), information value (IV), and mini-batch k-medoids (MBKM), on landslide susceptibility mapping in Songyang County, China, using support vector machines and random forest algorithms. Various evaluation metrics are employed to compare the efficacy of these sampling methods for susceptibility zoning. The results demonstrate that CTSES, IV, and MBKM methods exhibit an expansion of the high susceptibility region (maximum susceptibility mean value reaching 0.87) and divergence in the susceptibility index when extreme absence samples are present, with MBKM showing a comparative advantage (lower susceptibility mean value) compared to the IV model. Building on the strengths of different sampling methods, a novel integrative sampling approach that incorporates multiple existing methods is proposed. The integrative sampling can mitigate negative effects caused by extreme absence samples (susceptibility mean value is approximately 0.5 in the same extreme samples and presence-absence ratio) and obtain significantly better prediction results (AUC = 0.92, KC = 0.73, POA = 2.46 in the best model). Additionally, the mean level of susceptibility is heavily influenced by the proportion of absent samples.

Disasters caused by landslides pose a considerable threat to people’s lives and property, resulting in substantial losses each year. Landslide displacement rate prediction (LDRP) provides a useful fundamental tool for mitigating landslide disasters. However, more accurately predicting LDRP remains a challenge in the study of landslides. Lately, ensemble deep learning algorithms have shown promise in delivering a more precise and effective spatial modeling solution. The core aims of this research are to explore and evaluate the prediction capability of three progressive evolutionary deep learning (DL) techniques, i.e., a recurrent neural network (RNN), long short-term memory (LSTM), and a gated recurrent unit (GRU) ensemble AdaBoost algorithm for modeling rainfall-induced and reservoir-induced landslides in the Baihetan reservoir area in China. The outcomes show that the ensemble DL model could predict the Wangjiashan landslide in the Baihetan reservoir area with improved accuracy. The highest accuracy was achieved in the testing set when the window length equaled 30. However, assembling two predictors outperformed the accuracy of assembling three predictors, with the mean absolute error and root mean square error reaching 1.019 and 1.300, respectively. These findings suggest that the combination of strong learners and DL can yield satisfactory prediction results.

Karstic terrains are usually dominated by aquifer systems and/or underground cavities. Overexploitation of groundwater in such areas often induces land subsidence and sometimes causes sinkholes. The Cheria basin in Algeria suffers from severe land subsidence issues, and this phenomenon has been increasing in recent years due to population expansion and uncontrolled groundwater exploitation. This work uses GPS data and persistent scatterer interferometry synthetic aperture radar (PS-InSAR) techniques to monitor the land subsidence rate by employing Sentinel-1 satellite data for the period from 2016 to 2022. Our results demonstrate that the Cheria basin experiences both uplift and subsidence in places, with an overall substantial change in the land surface. The total cumulative subsidence over 6 years reached a maximum of 500 mm. Comparison of land deformation between PSI and GPS showed root mean square error (RMSE) values of about 2.83 mm/year, indicating that our analyzed results are satisfactorily reproducing the actual changes. Nonetheless, these results can be used to extract the susceptible zones for vertical ground displacement and evaluate the surface deformation inventory map of the region for reducing damages (e.g., human losses, economic impact, and environmental degradation) that may occur in the future (e.g., sinkholes) and can be further utilized in perspective for a sinkhole early warning system.

Hepatitis B virus (HBV) infection remains a major global threat to human health worldwide. Recently, the Chinese medicines with antiviral properties and low toxicity have been a concern. In our previous study, Eupolyphaga sinensis Walker polysaccharide (ESPS) has been isolated and characterized, while its antiviral effect on HBV remained unclear. The anti-HBV activity of ESPS and its regulatory pathway were investigated in vitro and in vivo. The results showed that ESPS significantly inhibited the production of HBsAg, HBeAg, and HBV DNA in the supernatants of HepG2.2.15 in a dose-dependent manner; HBV RNA and core protein expression were also decreased by ESPS. The in vivo studies using HBV transgenic mice further revealed that ESPS (20 and 40 mg/kg/2 days) significantly reduced the levels HBsAg, HBeAg, and HBV DNA in the serum, as well as HBV DNA and HBV RNA in mice liver. In addition, ESPS activated the Toll-like receptor 4 (TLR4) pathway; elevated levels of IFN-β, TNF-α, and IL-6 in the serum were observed, indicating that the anti-HBV effect of ESPS was achieved by potentiating innate immunity function. In conclusion, our study shows that ESPS is a potential anti-HBV ingredient and is of great value in the development of new anti-HBV drugs.

Landslide disasters cause serious property losses and casualties every year. Landslide displacement prediction is fundamental for mitigating landslide disasters. Several approaches have been used to predict landslide displacement, yet a more accurate and reliable displacement prediction still has a poor understanding of landslide early warning systems for landslide mitigation, due to limited data and mutational displacements. To boost the robustness and accuracy of landslide displacement prediction, this paper assembled a new hybrid model containing the local mean decomposition (LMD), innovations state space models for exponential smoothing (ETS), and the temporal convolutional network (TCN). The proposed model, which is based on over 10 years of long-term time series monitoring GPS data, was tested on the selected case—stepwise Baijiabao landslide in the Three Gorges Reservoir area of China (TGRA) was tested by the proposed model. The results presented that the LMD–ETS–TCN model has the best performance in comparison with other benchmark models. Compared with autoregressive integrated moving average (ARIMA), support vector regression (SVR), and long short-term memory neural network (LSTM), the accuracy was noticeably improved by an average of 40.9%, 46.2%, and 22.1%, respectively. The robustness and effectiveness of the presented approach are attested, and it has discernible improvements for landslide displacement prediction.

Abstract Landslides can substantially impact the fluvial systems, which is why the continuous mapping of their extent, evolution and stability assessment is crucial. However, in such environments, material identification (e.g. colluvium) and subsurface characterization by the methods used for geologic mapping and geotechnical investigation is often a challenging task. Thus, these classical invasive methods may benefit from geophysical techniques to enable and enhance our understanding of the subsurface in these areas. To examine such integrated approach, Multi-Channel Analysis of Surface Waves (MASW) combined with Electrical Resistivity Tomography (ERT) were applied on a geomorphologically active fluvial valley in Sobradinho (the Federal District of Brazil). The subsurface materials showed a specific range of resistivity values as dry soil, saprolite, and landslide slip surface. The 1D shear wave velocity (Vs) model showed an increasing trend of Vs with depth at a location away from the landslide mass, while the longitudinal profile (over the landslide) showed an anomalous change in Vs (~ 250 to 400 m/sec). Based on the existing information about the landslide, the ERT appeared to be an effective method over MASW. This study shows how the integration of geophysical data with the geological and geotechnical investigation helps to obtain a more realistic or unambiguous model of the subsurface.

Because of unique optoelectronic properties, such as extended carrier lifetime, large absorption coefficient, high defect tolerance factor, low exciton binding energy, and ambipolar diffusion, metal halide perovskites display enormous potential for the next generation of photovoltaics. Flexible perovskite solar cells with low weight, high flexibility, and conformability have attracted attention for portable electronic products. The interface is crucial in perovskite solar cells for both photovoltaic efficiency and operational stability. A high-quality interface could be obtained through interface engineering. Here, we summarize the degradation mechanisms and review interfacial engineering with the emphasis on their effects in flexible perovskite solar cells. On the basis of recent research progress in flexible devices, current challenges, possible directions, and perspectives are discussed. This would be helpful to promote the commercialization of flexible perovskite solar cells.

This paper presents the objectives of reclaimed water use in China and the current quantities used at the national and provincial levels. Using 2015 statistical data as an example, this paper studies the influences of water resources quantity, gross domestic product and policy promotion hierarchy on reclaimed water use in all provincial administrative regions in China. Reclaimed water use in Beijing and Jiangsu are presented as two representative examples. China's reclaimed water experience can provide some guidance for other countries facing similar water resource situations.

Malignant melanoma is one of the most common and dangerous skin cancers with a high rate of death every year. Furthermore, N-RAS and B-RAF mutations in melanoma cells increase the difficulties for clinical treatment in patients. Therefore, development of effective and universal drugs against melanoma is urgently needed. Here we demonstrate that baicalein and baicalin, the active components of the Chinese traditional medicinal plant Scutellaria baicalensis Georgi, can significantly inhibit melanoma cell growth and proliferation, suppress tumor cell colony formation and migration, as well as induce apoptosis and senescence in melanoma cells. The anti-tumor effects mediated by baicalein and baicalin are independent of N-RAS and B-RAF mutation statuses in melanoma cells. Mechanistically, we identify that the suppression of baicalein and baicalin on melanoma cells is due to inhibition of tumor cell glucose uptake and metabolism by affecting the mTOR-HIF-1α signaling pathway. In addition, we demonstrated that baicalein and baicalin can suppress tumorigenesis and tumor growth in vivo in the melanoma model. These studies clearly indicate that baicalein and baicalin can control tumor growth and development metabolically and have great potential as novel and universal drugs for melanoma therapy.

Landslides are usually triggered by strong earthquakes, heavy rainfalls, or intensive human activities in common wisdom. However, an unexpected landslide occurred in the Yabakei area, Nakatsu, Oita, Japan, at the pre-dawn hour 3:50 a.m. on 11 April 2018, without any accompanying rainfall and earthquake records during the event. This catastrophic landslide was 200 m in width, 110 m in height, and 60,000 m3 in mass volume, damaging four residential buildings with fatalities of six residents at the landslide toe. Field investigation was conducted immediately to identify geological setting, hydrological condition, and landslide geomorphological characteristics. Key findings speculate that infiltration of groundwater stored in the internal fractures led to the swelling and breaking of illite and askanite in the weathered sediment rocks, resulting in the failure of the Yabakei landslide. To reproduce and explore the dynamic process of this landslide event, based on spatial GIS data, we applied the proposed three-dimensional, Herschel-Bulkley-Papanastasiou rheology model-based smooth particle hydrodynamics (HBP-SPH) method to simulate the landslide dynamic process. Buildings in the landslide area are covered by a set of surfaced cells (SC) to analyze the mass impact on the residential buildings. Results showed good accordance between observation and simulation by the proposed SC-HBP-SPH method. The landslide impact force to the residential buildings could be up to 4224.89 kN, as indicated by the simulation.

In this study, an extended model for describing the temporal evolution of a characteristic floc size of cohesive sediment particles when the flocculation system is subject to a piecewise varied turbulent shear rate was derived by the probability methods based on the Shannon entropy theory following Zhu (2018). This model only contained three important parameters: initial and steady-state values of floc size, and a parameter characterizing the maximum capacity for floc size increase (or decay), and it can be adopted to capture well a monotonic pattern in which floc size increases (or decays) with flocculation time. Comparison with 13 literature experimental data sets regarding floc size variation to a varied shear rate showed the validity of the entropic model with a high correlation coefficient and few errors. Furthermore, for the case of tapered shear flocculation, it was found that there was a power decay of the capacity parameter with the shear rate, which is similar to the dependence of the steady-state floc size on the shear rate. The entropic model was further parameterized by introducing these two empirical relations into it, and the finally obtained model was found to be more sensitive to two empirical coefficients that have been incorporated into the capacity parameter than those in the steady-state floc size. The proposed entropic model could have the potential, as an addition to existing flocculation models, to be coupled into present mature hydrodynamic models to model the cohesive sediment transport in estuarine and coastal regions.

With the development of remote sensing algorithms and increased access to satellite data, generating up-to-date, accurate land use/land cover (LULC) maps has become increasingly feasible for evaluating and managing changes in land cover as created by changes to ecosystem and land use. The main objective of our study is to evaluate the performance of Support Vector Machine (SVM), Artificial Neural Network (ANN), Maximum Likelihood Classification (MLC), Minimum Distance (MD), and Mahalanobis (MH) algorithms and compare them in order to generate a LULC map using data from Sentinel 2 and Landsat 8 satellites. Further, we also investigate the effect of a penalty parameter on SVM results. Our study uses different kernel functions and hidden layers for SVM and ANN algorithms, respectively. We generated the training and validation datasets from Google Earth images and GPS data prior to pre-processing satellite data. In the next phase, we classified the images using training data and algorithms. Ultimately, to evaluate outcomes, we used the validation data to generate a confusion matrix of the classified images. Our results showed that with optimal tuning parameters, the SVM classifier yielded the highest overall accuracy (OA) of 94%, performing better for both satellite data compared to other methods. In addition, for our scenes, Sentinel 2 date was slightly more accurate compared to Landsat 8. The parametric algorithms MD and MLC provided the lowest accuracy of 80.85% and 74.68% for the data from Sentinel 2 and Landsat 8. In contrast, our evaluation using the SVM tuning parameters showed that the linear kernel with the penalty parameter 150 for Sentinel 2 and the penalty parameter 200 for Landsat 8 yielded the highest accuracies. Further, ANN classification showed that increasing the hidden layers drastically reduces classification accuracy for both datasets, reducing zero for three hidden layers.

Reliable characterization of the karst system is essential for risk assessment where many associated hazards (e.g., cover-collapse dolines and groundwater pollution) can affect natural and built environments, threatening public safety. The use of multiple geophysical approaches may offer an improved way to investigate such cover-collapse sinkholes and aid in geohazard risk assessments. In this paper, covered karst, which has two types of shallow caves (vadose and fluvial) located in Tarimba (Goias, Brazil), was investigated using various geophysical methods to evaluate their efficiency in the delineation of the geometry of sediments filled sinkhole. The methods used for the investigation were Electrical Resistivity Tomography (ERT), Seismic Refraction Survey (SRS), Seismic Refraction Tomography (SRT) and the Very Low Frequency Electromagnetic (VLF-EM) method. The study developed several (2D) sections of the measured physical properties, including P-wave velocity and electrical resistivity, as well as the induced current (because of local bodies). For the analysis and processing of the data obtained from these methods, the following approaches were adopted: ERT inversion using a least-square scheme, Karous-Hjelt filter for VLF-EM data and time-distance curves and Vp cross-sections for the SRS. The refraction data analysis showed three-layered stratigraphy topsoil, claystone and carbonate bedrock, respectively. The findings obtained from ERT (three-layered stratigraphy and sediment-filled doline), as well as VLF-EM (fractured or filled caves as a positive anomaly), were found to be consistent with the actual field conditions. However, the SRS and SRT methods did not show the collapsed material and reached the limited the depth because of shorter profile lengths. The study provides a reasonable basis for the development of an integrated geophysical approach for site characterization of karst systems, particularly the perched tank and collapse doline.

Flash flood is one of the most dangerous natural phenomena because of its high magnitudes and sudden occurrence, resulting in huge damages for people and properties. Our work aims to propose a state-of-the-art model for susceptibility mapping of the flash flood using the decision tree random subspace ensemble optimized by hybrid firefly–particle swarm optimization (HFPS), namely the HFPS-RSTree model. In this work, we used data from a flood inventory map consisting of 1866 polygons derived from Sentinel-1 C-band synthetic aperture radar (SAR) data and a field survey conducted in the northwest mountainous area of the Van Ban district, Lao Cai Province in Vietnam. A total of eleven flooding conditioning factors (soil type, geology, rainfall, river density, elevation, slope, aspect, topographic wetness index (TWI), normalized difference vegetation index (NDVI), plant curvature, and profile curvature) were used as explanatory variables. These indicators were compiled from a geological and mineral resources map, soil type map, and topographic map, ALOS PALSAR DEM 30 m, and Landsat-8 imagery. The HFPS-RSTree model was trained and verified using the inventory map and the eleven conditioning variables and then compared with four machine learning algorithms, i.e., the support vector machine (SVM), the random forests (RF), the C4.5 decision trees (C4.5 DT), and the logistic model trees (LMT) models. We employed a range of statistical standard metrics to assess the predictive performance of the proposed model. The results show that the HFPS-RSTree model had the best predictive performance and achieved better results than those of other benchmarks with the ability to predict flash flood, reaching an overall accuracy of over 90%. It can be concluded that the proposed approach provides new insights into flash flood prediction in mountainous regions.

The use of geophysical characterization of karst systems can provide an economical and non-invasive alternative for extracting information about cavities, sinkholes, pathways for water infiltration as well as the degree of karstification of underlying carbonate rocks. In the present study, three geophysical techniques, namely, Ground Penetrating Radar (GPR), Electrical Resistivity Tomography (ERT) and Very Low Frequency Electromagnetic (VLFEM) methods were applied at three different locations in relation to fluvial karst, which is listed as an environmentally sensitive area in Rio Vermelho, Mambaí, Goiás, Brazil. In the data acquisition phase, the GPR, direct-current (DC) resistivity and VLFEM profiles were obtained at the three locations in the area. Data were analyzed using commonly adopted processing workflows. The GPR results showed a well-defined lithology of the site based on the amplitude of the signal and radar typologies. On the other hand, the inverted resistivity cross-sections showed a three-layered stratigraphy, pathways of water infiltration and the weathered structures in carbonate (Bambui group). The interpretation of VLFEM as contours of current density resulted from Fraser and Karous–Hjelt filters, indicated the presence of conductive structures (high apparent current density) that might be linked to the weathered carbonate and other conductive and resistive anomalies associated with the water-filled and dry cavities (cave), respectively. The results encourage the integrated application of geophysical techniques such as the reconnaissance for further detailed characterization of the karst areas.

This paper aims to apply and compare the performance of the three machine learning algorithms–support vector machine (SVM), bayesian logistic regression (BLR), and alternating decision tree (ADTree)–to map landslide susceptibility along the mountainous road of the Salavat Abad saddle, Kurdistan province, Iran. We identified 66 shallow landslide locations, based on field surveys, by recording the locations of the landslides by a global position System (GPS), Google Earth imagery and black-and-white aerial photographs (scale 1: 20,000) and 19 landslide conditioning factors, then tested these factors using the information gain ratio (IGR) technique. We checked the validity of the models using statistical metrics, including sensitivity, specificity, accuracy, kappa, root mean square error (RMSE), and area under the receiver operating characteristic curve (AUC). We found that, although all three machine learning algorithms yielded excellent performance, the SVM algorithm (AUC = 0.984) slightly outperformed the BLR (AUC = 0.980), and ADTree (AUC = 0.977) algorithms. We observed that not only all three algorithms are useful and effective tools for identifying shallow landslide-prone areas but also the BLR algorithm can be used such as the SVM algorithm as a soft computing benchmark algorithm to check the performance of the models in future.

The soiling of solar panels from dry deposition affects the overall efficiency of power output from solar power plants. This study focuses on the detection and monitoring of sand deposition (wind-blown dust) on photovoltaic (PV) solar panels in arid regions using multitemporal remote sensing data. The study area is located in Bhadla solar park of Rajasthan, India which receives numerous sandstorms every year, carried by westerly and north-westerly winds. This study aims to use Google Earth Engine (GEE) in monitoring the soiling phenomenon on PV panels. Optical imageries archived in the GEE platform were processed for the generation of various sand indices such as the normalized differential sand index (NDSI), the ratio normalized differential soil index (RNDSI), and the dry bare soil index (DBSI). Land surface temperature (LST) derived from Landsat 8 thermal bands were also used to correlate with sand indices and to observe the pattern of sand accumulation in the target region. Additionally, high-resolution PlanetScope images were used to quantitatively validate the sand indices. Our study suggests that the use of freely available satellite data with semiautomated processing on GEE can be a useful alternative to manual methods. The developed method can provide near real-time monitoring of soiling on PV panels cost-effectively. This study concludes that the DBSI method has a comparatively higher potential (89.6% Accuracy, 0.77 Kappa) in the detection of sand deposition on PV panels as compared to other indices. The findings of this study can be useful to solar energy companies in the development of an operational plan for the cleaning of PV panels regularly.

The velocity profile of an open channel is an important research topic in the context of open channel hydraulics; in particular, the velocity-dip position has drawn the attention of hydraulic scientists. In this study, analytical expressions for the velocity-dip position over the entire cross section and at the centerline of a rectangular open channel are derived by adopting probability methods based on the Tsallis and general index entropy theories. Two kinds of derived entropy-based expressions have the same mathematical form as a function of the lateral distance from the sidewall of the channel or of the aspect ratio of the channel. Furthermore, for the velocity-dip position over the entire cross section of the rectangular open channel, the derived expressions are compared with each other, as well as with two existing deterministic models and the existing Shannon entropy-based expression, using fifteen experimental datasets from the literature. An error analysis shows that the model of Yang et al. and the Tsallis entropy-based expression predict the lateral distribution of the velocity-dip position better than the other proposed models. For the velocity-dip position at the centerline of the rectangular open channel, six existing conventional models, the derived Tsallis and general index entropy-based expressions, and the existing Shannon entropy-based models are tested against twenty-one experimental datasets from the literature. The results show that the model of Kundu and the Shannon entropy-based expression have superior prediction accuracy with respect to experimental data compared with other models. With the exception of these models, the Tsallis entropy-based expression has the highest correlation coefficient value and the lowest root mean square error value for experimental data among the other models. This study indicates that the Tsallis entropy could be a good addition to existing deterministic models for predicting the lateral distribution of the velocity-dip position of rectangular open channel flow. This work also shows the potential of entropy-based expressions, the Shannon entropy and the Tsallis entropy in particular, to predict the velocity-dip position at the centerline of both narrow and wide rectangular open channels.

Spatial urban growth and its impact on land surface temperature (LST) is a high priority environmental issue for urban policy. Although the impact of horizontal spatial growth of cities on LST is well studied, the impact of the vertical spatial distribution of buildings on LST is under-investigated. This is particularly true for cities in sub-tropical developing countries. In this study, TerraSAR-X add-on for Digital Elevation Measurement (TanDEM-XDEM), Advanced Spaceborne Thermal Emission and Reflection (ASTER)-Global Digital Elevation Model (GDEM), and ALOS World 3D-30m (AW3D30) based Digital Surface Model (DSM) data were used to investigate the vertical growth of the Dhaka Metropolitan Area (DMA) in Bangladesh. Thermal Infrared (TIR) data (10.6-11.2µm) of Landsat-8 were used to investigate the seasonal variations in LST. Thereafter, the impact of horizontal and vertical spatial growth on LST was studied. The result showed that: (a) TanDEM-X DSM derived building height had a higher accuracy as compared to other existing DSM that reveals mean building height of the Dhaka city is approximately 10 m, (b) built-up areas were estimated to cover approximately 94%, 88%, and 44% in Dhaka South City Corporation (DSCC), Dhaka North City Corporation (DNCC), and Fringe areas, respectively, of DMA using a Support Vector Machine (SVM) classification method, (c) the built-up showed a strong relationship with LST (Kendall tau coefficient of 0.625 in summer and 0.483 in winter) in comparison to vertical growth (Kendall tau coefficient of 0.156 in the summer and 0.059 in the winter), and (d) the ‘low height-high density’ areas showed high LST in both seasons. This study suggests that vertical development is better than horizontal development for providing enough open spaces, green spaces, and preserving natural features. This study provides city planners with a better understating of sustainable urban planning and can promote the formulation of action plans for appropriate urban development policies.

Landslides pose a serious threat to the safety of human life and property in mountainous regions. Susceptibility assessment for landslides is critical in landslide management strategy. Recent studies indicate that the traditional assessment models in many previous studies commonly assume a fixed relationship between influencing factors and landslide occurrence within an area, resulting in an inadequate evaluation for the local landslides susceptibility. To address this issue, in this paper we propose a spatial proximity-based geographically weighted regression (S-GWR) model considering spatial non-stationarity of landslide data for assessing the landslide susceptibility. Spatial proximity is the basic input condition for the proposed S-GWR model. The challenge lies in defining the spatial proximity expression that shows the geographical features of landslides and therefore affects the model ability of S-GWR. Our solution chooses the slope unit as spatial adjacency, rather than the grid unit in DTM. The multicollinearity between landslide influencing factors is then eliminated through variance inflation factor (VIF) method and principal component analysis (PCA). The proposed model is subsequently validated by using data in Qingchuan County, southwestern China. Spatial non-stationary is identified for landslide data. A comparison with grid unit and four traditional evaluation models is conducted. Validation results using the area under the ROC (receiver operating characteristic) curve and success rate curve indicate that the spatial proximity-based GWR model with slope unit has the highest predictive accuracy (0.859 and 0.850 respectively).

In this study, the ilmenite content in beach placer sand was estimated using seven soft computing techniques, namely random forest (RF), artificial neural network (ANN), k-nearest neighbors (kNN), cubist, support vector machine (SVM), stochastic gradient boosting (SGB), and classification and regression tree (CART). The 405 beach placer borehole samples were collected from Southern Suoi Nhum deposit, Binh Thuan province, Vietnam, to test the feasibility of these soft computing techniques in estimating ilmenite content. Heavy mineral analysis indicated that valuable minerals in the placer sand are zircon, ilmenite, leucoxene, rutile, anatase, and monazite. In this study, five materials, namely rutile, anatase, leucoxene, zircon, and monazite, were used as the input variables to estimate ilmenite content based on the above mentioned soft computing models. Of the whole dataset, 325 samples were used to build the regarded soft computing models; 80 remaining samples were used for the models’ verification. Root-mean-squared error (RMSE), determination coefficient (R2), a simple ranking method, and residuals analysis technique were used as the statistical criteria for assessing the model performances. The numerical experiments revealed that soft computing techniques are capable of estimating the content of ilmenite with high accuracy. The residuals analysis also indicated that the SGB model was the most suitable for determining the ilmenite content in the context of this research.

The morphological properties of kaolin flocs were investigated in a Couette-flow experiment at the steady state under seven shear flow conditions (shear rates of 5.36, 9.17, 14, 24, 31, 41 and 53 s−1). These properties include a one-dimensional (1-D) fractal dimension (D1), a two-dimensional (2-D) fractal dimension (D2), a perimeter-based fractal dimension (Dpf) and an aspect ratio (AR). They were calculated based on the projected area (A), equivalent size, perimeter (P) and length (L) of the major axis of the floc determined through sample observation and an image analysis system. The parameter D2, which characterizes the relationship between the projected area and the length of the major axis using a power function, , increased from 1.73 ± 0.03, 1.72 ± 0.03, and 1.75 ± 0.04 in the low shear rate group (G = 5.36, 9.17, and 14 s−1) to 1.92 ± 0.03, 1.82 ± 0.02, 1.85 ± 0.02, and 1.81 ± 0.02 in the high shear rate group (24, 31, 41 and 53 s−1), respectively. The parameter D1 characterizes the relationship between the perimeter and length of the major axis by the function and decreased from 1.52 ± 0.02, 1.48 ± 0.02, 1.55 ± 0.02, and 1.63 ± 0.02 in the low shear group (5.36, 9.17, 14 and 24 s−1) to 1.45 ± 0.02, 1.39 ± 0.02, and 1.39 ± 0.02 in the high shear group (31, 41 and 53 s−1), respectively. The results indicate that with increasing shear rates, the flocs become less elongated and that their boundary lines become tighter and more regular, caused by more breakages and possible restructurings of the flocs. The parameter Dpf, which is related to the perimeter and the projected area through the function , decreased as the shear rate increased almost linearly. The parameter AR, which is the ratio of the length of the major axis and equivalent diameter, decreased from 1.56, 1.59, 1.53 and 1.51 in the low shear rate group to 1.43, 1.47 and 1.48 in the high shear rate group. These changes in Dpf and AR show that the flocs become less convoluted and more symmetrical and that their boundaries become smoother and more regular in the high shear rate group than in the low shear rate group due to breakage and possible restructuring processes. To assess the effects of electrolyte and sediment concentration, 0.1 mol/L calcium chloride (CaCl2) and initial sediment concentration from 7.87 × 10−5 to 1.57 × 10−5 were used in this preliminary study. The addition of electrolyte and increasing sediment concentration could produce more symmetrical flocs with less convoluted and simpler boundaries. In addition, some new information on the temporal variation of the median size of the flocs during the flocculation process is presented.

This paper proposes an automatic method for detecting landslides by using an integrated approach comprising object-oriented image analysis (OOIA), a genetic algorithm (GA), and a case-based reasoning (CBR) technique. It consists of three main phases: (1) image processing and multi-image segmentation; (2) feature optimization; and (3) detecting landslides. The proposed approach was employed in a fast-growing urban region, the Pearl River Delta in South China. The results of detection were validated with the help of field surveys. The experimental results indicated that the proposed OOIA-GA-CBR (0.87) demonstrates higher classification performance than the stand-alone OOIA (0.75) method for detecting landslides. The area under curve (AUC) value was also higher than that of the simple OOIA, indicating the high efficiency of the proposed landslide detection approach. The case library created using the integrated model can be reused for time-independent analysis, thus rendering our approach superior in comparison to other traditional methods, such as the maximum likelihood classifier. The results of this study thus facilitate fast generation of accurate landslide inventory maps, which will eventually extend our understanding of the evolution of landscapes shaped by landslide processes.

Abstract Accumulating evidence suggests the immunosuppressive microenvironments created by malignant tumors represent a major obstacle for effective anti‐tumor immunity. A better understanding of the suppressive mechanisms mediated by tumor microenvironments and the development of strategies to reverse the immune suppression are major challenges for the success of tumor immunotherapy. Here, we report that human tumor cells can induce senescence in naïve/effector T cells, exhibiting potent suppressive function in vitro and in vivo. We further show that tumor‐derived endogenous cyclic adenosine monophosphate (cAMP) is responsible for the induction of T‐cell senescence. Importantly, activation of TLR8 signaling in tumor cells can block the induction and reverse the suppression of senescent naïve and tumor‐specific T cells in vitro and in vivo, resulting in enhanced anti‐tumor immunity. These studies identify a novel mechanism of human tumor‐mediated immune suppression and provide a new strategy to reverse tumor immunosuppressive effects for tumor immunotherapy.

We prepared a landslide susceptibility map for the Sarkhoon watershed, Chaharmahal-w-bakhtiari, Iran, using novel ensemble artificial intelligence approaches. A classifier of support vector machine (SVM) was employed as a base classifier, and four Meta/ensemble classifiers, including Adaboost (AB), bagging (BA), rotation forest (RF), and random subspace (RS), were used to construct new ensemble models. SVM has been used previously to spatially predict landslides, but not together with its ensembles. We selected 20 conditioning factors and randomly portioned 98 landslide locations into training (70%) and validating (30%) groups. Several statistical metrics, including sensitivity, specificity, accuracy, kappa, root mean square error (RMSE), and area under the receiver operatic characteristic curve (AUC), were used for model comparison and validation. Using the One-R Attribute Evaluation (ORAE) technique, we found that all 20 conditioning factors were significant in identifying landslide locations, but “distance to road” was found to be the most important. The RS (AUC = 0.837) and RF (AUC = 0.834) significantly improved the goodness-of-fit and prediction accuracy of the SVM (AUC = 0.810), whereas the BA (AUC = 0.807) and AB (AUC = 0.779) did not. The random subspace based support vector machine (RSSVM) model is a promising technique for helping to better manage land in landslide-prone areas.

Renewable energy has received noteworthy attention during the last few decades. This is partly due to the fact that fossil fuels are depleting and the need for energy is soaring because of the growing population of the world. This paper attempts to provide an idea of what is being done by researchers in remote sensing and geographical information system (GIS) field for exploring the renewable energy resources in order to get to a more sustainable future. Several studies related to renewable energy resources viz. geothermal energy, wind energy, hydropower, biomass, and solar energy, have been considered in this paper. The focus of this review paper is on exploring how remote sensing and GIS-based techniques have been beneficial in exploring optimal locations for renewable energy resources. Several case studies from different parts of the world which use such techniques in exploring renewable energy resource sites of different kinds have also been included in this paper. Though each of the remote sensing and GIS techniques used for exploration of renewable energy resources seems to efficiently sell itself in being the most effective among others, it is important to keep in mind that in actuality, a combination of different techniques is more efficient for the task. Throughout the paper, many issues relating to the use of remote sensing and GIS for renewable energy are examined from both current and future perspectives and potential solutions are suggested. The authors believe that the conclusions and recommendations drawn from the case studies and the literature reviewed in the present study will be valuable to renewable energy scientists and policymakers.

In this study, a novel technique to support smart city planning in estimating and controlling the heating load (HL) of buildings, was proposed, namely PSO-XGBoost. Accordingly, the extreme gradient boosting machine (XGBoost) was developed to estimate HL first; then, the particle swarm optimization (PSO) algorithm was applied to optimize the performance of the XGBoost model. The classical XGBoost model, support vector machine (SVM), random forest (RF), Gaussian process (GP), and classification and regression trees (CART) models were also investigated and developed to predict the HL of building systems, and compared with the proposed PSO-XGBoost model; 837 investigations of buildings were considered and analyzed with many influential factors, such as glazing area distribution (GAD), glazing area (GA), orientation (O), overall height (OH), roof area (RA), wall area (WA), surface area (SA), and relative compactness (RC). Mean absolute percentage error (MAPE), root-mean-squared error (RMSE), variance account for (VAF), mean absolute error (MAE), and determination coefficient (R2), were used as the statistical criteria for evaluating the performance of the above models. The color intensity, as well as the ranking method, were also used to compare and evaluate the models. The results showed that the proposed PSO-XGBoost model was the most robust technique for estimating the HL of building systems. The remaining models (i.e., XGBoost, SVM, RF, GP, and CART) yielded more mediocre performance through RMSE, MAE, R2, VAF, and MAPE metrics. Another finding of this study also indicated that OH, RA, WA, and SA were the most critical parameters for the accuracy of the proposed PSO-XGBoost model. They should be particularly interested in smart city planning as well as the optimization of smart cities.

Energy-efficiency is one of the critical issues in smart cities. It is an essential basis for optimizing smart cities planning. This study proposed four new artificial intelligence (AI) techniques for forecasting the heating load of buildings’ energy efficiency based on the potential of artificial neural network (ANN) and meta-heuristics algorithms, including artificial bee colony (ABC) optimization, particle swarm optimization (PSO), imperialist competitive algorithm (ICA), and genetic algorithm (GA). They were abbreviated as ABC-ANN, PSO-ANN, ICA-ANN, and GA-ANN models; 837 buildings were considered and analyzed based on the influential parameters, such as glazing area distribution (GLAD), glazing area (GLA), orientation (O), overall height (OH), roof area (RA), wall area (WA), surface area (SA), relative compactness (RC), for estimating heating load (HL). Three statistical criteria, such as root-mean-squared error (RMSE), coefficient determination (R2), and mean absolute error (MAE), were used to assess the potential of the aforementioned models. The results indicated that the GA-ANN model provided the highest performance in estimating the heating load of buildings’ energy efficiency, with an RMSE of 1.625, R2 of 0.980, and MAE of 0.798. The remaining models (i.e., PSO-ANN, ICA-ANN, ABC-ANN) yielded lower performance with RMSE of 1.932, 1.982, 1.878; R2 of 0.972, 0.970, 0.973; MAE of 1.027, 0.980, 0.957, respectively.

The bathymetry of nearshore coastal environments and lakes is constantly reworking because of the change in the patterns of energy dispersal and related sediment transport pathways. Therefore, updated and accurate bathymetric models are a crucial component in providing necessary information for scientific, managerial, and geographical studies. Recent advances in satellite technology revolutionized the acquisition of bathymetric profiles, offering new vistas in mapping. This contribution analyzed the suitability of Sentinel-2 and Landsat-8 images for bathymetric mapping of coastal and lake environments. The bathymetric algorithm was developed using an empirical approach and a random forest (RF) model based on the available high-resolution LiDAR bathymetric data for Mobile Bay, Tampa Bay, and Lake Huron regions obtained from the National Oceanic and Atmospheric Administration (NOAA) National Geophysical Data Center (NGDC). Our results demonstrate that the satellite-derived bathymetry is efficient for retrieving depths up to 10 m for coastal regions and up to 30 m for the lake environment. While using the empirical approach, the root-mean-square error (RMSE) varied between 1.99 m and 4.74 m for the three regions. The RF model, on the other hand, provided an improved bathymetric model with RMSE between 1.13 m and 1.95 m. The comparative assessment suggests that Sentinel-2 has a slight edge over Landsat-8 images while employing the empirical approach. On the other hand, the RF model shows that Landsat-8 retrieves a better bathymetric model than Sentinel-2. Our work demonstrated that the freely available Sentinel-2 and Landsat-8 imageries proved to be reliable data for acquiring updated bathymetric information for large areas in a short period.

In the context of river dynamics, some experimental results have shown that particle velocity is different from fluid velocity along the stream-wise direction for uniform sediment-laden open-channel flows; this velocity difference has been termed velocity lag in the literature. In this study, an analytical expression for estimating the velocity lag in open-channel flows was derived based on the Tsallis entropy theory together with the principle of maximum entropy. The derived expression represents the velocity lag as a function of a non-dimensional entropy parameter depending on the average and maximum values of velocity lag from experimental measurements. The derived expression was tested against twenty-two experimental datasets collected from the literature with three deterministic models and the developed Shannon entropy-based model. The Tsallis entropy-based model agreed better with the experimental datasets than the deterministic models for eighteen out of the twenty-two total real cases, and the prediction accuracy for the eighteen experimental datasets was comparable to that of the developed Shannon entropy-based model (the Tsallis entropy-based expression agreed slightly better than the Shannon entropy-based model for twelve out of eighteen test cases, whereas for the other six test cases, the Shannon entropy-based model had a slightly higher prediction accuracy). Finally, the effects of the friction velocity of the flow, the particle diameter, and the particles’ specific gravity on the velocity lag were analyzed based on the Tsallis entropy-based model. This study shows the potential of the Tsallis entropy theory together with the principle of maximum entropy to predict the stream-wise velocity lag between a particle and the surrounding fluid in sediment-laden open-channel flows.

Landslides are typically triggered by earthquakes or rainfall occasionally a rainfall event followed by an earthquake or vice versa. Yet, most of the works presented in the past decade have been largely focused at the single event-susceptibility model. Such type of modeling is found insufficient in places where the triggering mechanism involves both factors such as one found in the Chuetsu region, Japan. Generally, a single event model provides only limited enlightenment of landslide spatial distribution and thus understate the potential combination-effect interrelation of earthquakes- and rainfall-triggered landslides. This study explores the both-effect of landslides triggered by Chuetsu-Niigata earthquake followed by a heavy rainfall event through examining multiple traditional statistical models and data mining for understanding the coupling effects. This paper aims to compare the abilities of the statistical probabilistic likelihood-frequency ratio (PLFR) model, information value (InV) method, certainty factors (CF), artificial neural network (ANN) and ensemble support vector machine (SVM) for the landslide susceptibility mapping (LSM) using high-resolution-light detection and ranging digital elevation model (LiDAR DEM). Firstly, the landslide inventory map including 8459 landslide polygons was compiled from multiple aerial photographs and satellite imageries. These datasets were then randomly split into two parts: 70% landslide polygons (5921) for training model and the remaining polygons for validation (2538). Next, seven causative factors were classified into three categories namely topographic factors, hydrological factors and geological factors. We then identified the associations between landslide occurrence and causative factors to produce LSM. Finally, the accuracies of five models were validated by the area under curves (AUC) method. The AUC values of five models vary from 0.77 to 0.87. Regarding the capability of performance, the proposed SVM is promising for constructing the regional landslide-prone potential areas using both types of landslides. Additionally, the result of our LSM can be applied for similar areas which have been experiencing both rainfall-earthquake landslides.

In this study, the distribution of sediment concentration and the mean sediment concentration in debris flow were investigated using deterministic and probabilistic approaches. Tsallis entropy and Shannon entropy have recently been employed to estimate these parameters. However, other entropy theories, such as the general index entropy and Renyi entropy theories, which are generalizations of the Shannon entropy, have not been used to derive the sediment concentration in debris flow. Furthermore, no comprehensive and rigorous analysis has been conducted to compare the goodness of fit of existing conventional deterministic methods and different entropy-based methods using experimental data collected from the literature. Therefore, this study derived the analytical expressions for the distribution of sediment concentration and the mean sediment concentration in debris flow based on the general index entropy and Renyi entropy theories together with the principle of maximum entropy and tested the validity of existing conventional deterministic methods as well as four different entropy-based expressions for the limited collected observational data. This study shows the potential of using the Tsallis entropy theory together with the principle of maximum entropy to predict sediment concentration in debris flow over an erodible channel bed.