This study estimates chemical composition of microplastic in sediments and benthic detritivores (sea cucumbers) collected from different marine rocky bottom habitat types (bank, landslide, cliff) of Salina Island (Aeolian Archipelago, Italy). Also, species richness and bottom coverings by benthic species were recorded at each sampling station. Correlations among chemical composition of microplastic in sediments and in detritivores were explored linking recorded variability to the factor “habitat type”. Results evidence that the habitat types considered in this study are characterized by wide species richness and by high percentages of bottom coverage by protected species by international conventions. In spite of the high ecological value of habitats considered in this study, microplastics were recorded both in sediments (PVC, PET, PE, PS, PA, PP) and in stomach contents of sea cucumbers (PET, PA) collected in all sampling sites, confirming the exposure of benthic species to microplastic pollution.

Typhoon Morakot pounded Taiwan in 2009 with record-breaking rainfall, washing an unprecedented amount of driftwood into the sea that was partially deposited at the coastal areas. According to the satellite imagery analysis, more than three million trees fell and were washed away to occupy 83.2% of the Taiwanese coastline, including 52 fishing harbors. The amount cleaned-up was only 1/7 of the total coastal driftwood. It was found that the amount of coastal driftwood is not only related to the amount of precipitation but is also related to the distance from the location of the landslide to the river mouth and to the landslide area. The amount of accumulated coastal driftwood demonstrated log-profile declines with increasing distance to the river mouth. Nearshore current and wave motion are the critical factors for driftwood deposition. Much of the driftwood washed into the sea harmed the tourism and fishing industries, endangered navigation and oceanic activities, and impacted the marine environment and ecosystem.

In addition to global population growth due to migration from rural areas to urban areas, population density is constantly increasing in certain regions, thereby necessitating the introduction of new settlements in these regions. However, in the selection of settlement areas, no sufficient preliminary examinations have been conducted; consequently, various natural disasters may cause significant life and property losses. Herein, the most suitable settlement areas were determined using GIS (geographic information systems) in Canik District, where the population is continuously increasing. Therefore, this study aimed to incorporate a new perspective into studies on this subject. Within the scope of the study, landslide and flood risks, which are among the most important natural disasters in the region, were primarily evaluated, and high-risk areas were determined. Elevation, slope, aspect, curvature, lithology, topographic humidity index (TWI), and proximity to river parameters were used to produce flood susceptibility maps. A digital elevation model (DEM) of the study area was produced using contours on the 1/25,000 scaled topographic map. The elevation, slope, aspect, curvature, and TWI parameters were produced from the DEM using the relevant analysis routines of ArcGIS software. The raster map of each parameter was divided into 5 subclasses using the natural breaks classification method. In the reclassified raster maps, the most flood-sensitive or flood-prone subclasses were assigned a value of 5, and the least sensitive subclasses were assigned a value of 1. Then, the reclassified maps of the 7 parameters were collected using the “map algebra” function of ArcGIS 10.5 software, and the flood susceptibility index (FSI) map of the study area was obtained. The flood susceptibility map of the study area was obtained by dividing the FSI into 5 subclasses (very low, low, moderate, high, and very high) according to the natural breaks classification method. Thereafter, suitable and unsuitable areas in terms of biocomfort, which affects people’s health, peace, comfort, and psychology and is significant in terms of energy efficiency, were determined. At the last stage of the study, the most suitable settlement areas that were suitable in terms of both biocomfort and low levels of landslide and flood risks were determined. The calculated proportion of such areas to the total study area was only 2.1%. Therefore, because these areas were insufficient for the establishment of new settlements, areas that had low landslide and flood risks but were unsuitable for biocomfort were secondarily determined; the ratio of these areas was calculated as 56.8%. The remaining areas were inconvenient for the establishment of settlements due to the risk of landslides and floods; the ratio of these areas was calculated as 41.1%. This study is exemplary in that the priority for the selection of settlement areas was specified, and this method can be applied for selecting new settlements for each region considering different criteria. Due to the risk of landslides or flooding in the study area, the areas unsuitable for establishing a settlement covered approximately 41.1% of the total study area. The areas that had low flood and landslide risks but were suitable for biocomfort constituted only 2.1% of the study area. In approximately 56.8% of the study area, the risk of landslides or floods was low, and these areas were unsuitable in terms of biocomfort. Therefore, these areas were secondarily preferred as settlement areas. The most suitable areas for settlements constituted only 0.19% of the total study area, and these areas will not be able to meet the increasing demand for settlement area. Therefore, it is recommended to select areas that do not have the risk of landslides and floods but are unsuitable for biocomfort. This study reveals that grading should be performed in the selection of settlement areas. When choosing a settlement area in any region, possible natural disasters in the region should be identified first, and these disasters should be ordered in terms of their threat potential. Moreover, biocomfort areas suitable for settlements should be considered. In the next stages of settlement area selection, the criteria that affect the peace and comfort of people, such as distance to pollution sources, distance to noise sources, and proximity to natural areas, should also be evaluated. Thus, a priority order should be created for the selection of settlement areas using various other criteria.

The study endeavored to analyze the risk perception, sense of place, and disaster preparedness in response to landslide disaster–prone mountain areas of Gilgit-Baltistan, Pakistan. To this end, we surveyed 315 rural residents of two vulnerable landslide districts (Hunza and Nagar) of Gilgit-Baltistan. To explore the relationships between the dimensions of risk perception, sense of place, and disaster preparedness, we used partial least squares (PLS) structural equation modeling (SEM) to test the hypotheses. The results derived from PLS-SEM have implied that there is a significant negative relationship between risk perception (apprehension and unidentified) with a sense of place (bond with society and place dependence). It was observed that the residents usually overestimate the risks of disasters due to their limited scientific knowledge regarding disaster occurrence, which reduces their dependencies on the place. We revealed that disaster preparedness enhances the place attachment and reduces the apprehension of landslides in the study area. This study devotes to government and relevant agencies to devise policies that can help relocate the vulnerable rural settlements, develop, and educate the masses on disaster mitigation and prevention strategies, and help prepare a suitable landslide management plan.

This paper addresses the geochemical evolution, volumetric water content, and temperature of porewater when constructing different soil layers to improve the surface acidic conditions on a slope at a closed mine. Three cases were set under different layer systems. Case 1 was solely composed of surface-weathered rocks. A vegetation layer was constructed on the surface rocks in case 2, whereas a top vegetation and bottom low-permeable layers were constructed on the rocks in case 3. In both cases, a soil–cement layer was constructed to prevent landslides. Porewater sampling systems and soil sensors were set at different depths to collect porewater and measure the volumetric water content and temperature. The results showed that, when no layers were applied (case 1), high concentrations of heavy metals and low pH values were observed regardless of the depth and season. When a vegetation layer (case 2) was applied, a dramatic decrease in heavy metal concentrations was observed, similar to the results in case 3. In both cases, pH values were circumneutral. Moreover, the addition of the low-permeable layer reduced the infiltration of rainfall through the layers by considering the changes in volumetric water content. Also, the results of case 1 were compared with those obtained at a flat surface under similar conditions. On the slope, the pH was more acidic, and heavy metal concentrations were higher. These suggest that the dissolution of heavy metals from the weathered rocks into the porewater is enhanced on the sloping surface due to a longer solid–liquid interaction time.

Spatio-temporal evolution of post-seismic landslides and debris flows provides a new perspective to understand post-earthquake evolution of geological environments and landscapes, and to instruct cascaded catastrophic hazard mitigation and post-disaster reconstruction. However, limited earthquake events have been investigated for post-earthquake geohazard evolution. This work reports the geohazard evolution after the 2017 Mₛ 7.0 Jiuzhaigou earthquake considering the effects of the earthquake, geology, terrain, meteorology, hydrology, and human engineering activity. Some new viewpoints are suggested. (1) Landslide and debris flow activity intensified in the first year following the earthquake under the effects of the antecedent earthquake, precipitation, fault tectonics, human engineering activity, and fluvial networks. (2) Landslide and debris flow activity declined rapidly in the second year as a result of dramatically reduced sediments, declined rainfall, and self-healed slopes. (3) The significant decay of landslide and debris flow activity and the prominent reduction of loose deposits indicate that the geological environment was gradually restoring. (4) Although the hazard effect mitigation and geological environment restoration were ongoing (in the absence of rainstorm events) to attain a new balance, the geoenvironment has not returned to the pre-earthquake level because of widespread unrecovered geohazards and the remaining loose deposits on hillslopes or in channels. (5) The geological environment after the Jiuzhaigou earthquake may re-equilibrate and return to the pre-earthquake level more quickly than after the Kashmir, Chi-Chi, Gorkha, Wenchuan, and Murchison earthquakes. This work provides new knowledge pertaining to geohazard evolution after a strong earthquake and to profound impacts of a catastrophic earthquake on geological environment and landscape.

The Büyük Menderes watershed is the largest drainage watershed in Western Anatolia with an area of approximately 26,000 km². In the study area, almost 863 landslides occurred, extending over 222 km² with a mean landslide area of 0.21 km². In this study, landslide susceptibility assessments were carried out using artificial neural network method, which is one of the data-driven methods. In this study, that will contribute to the mitigation or control of the landslides caused by the reasons controlling the spatial and temporal distribution of landslides created in the GIS and MATLAB environment by using scientific and technological approaches within the framework. Since derivative activation function is also used in back-propagation artificial neural networks, its derivative is easily calculated in order not to slow down the calculation. Levenberg–Marquardt back-propagation (LM), resilient back propagation back-propagation (trainrp), scaled conjugate gradient back-propagation (trainscg), conjugate gradient with Powell/Beale restarts back-propagation (traincgb), and Fletcher-Powell conjugate gradient back-propagation (traincgf) algorithms are used, which constantly interrogate the link between the input parameter and the result output, and at least one cell’s output is given as an input to any other cell. Geology, digital elevation model, slope, topographic wetness index, roughness index, plan, profile curvatures, and proximity to active faults and rivers were used as landslide conditioning factors. In susceptibility assessments, landslides were separated by 70% analysis, 15% test, and 15% validation datasets by random selection method. The performances of the landslide susceptibility maps were assessed by the area under the ROC curve (AUC), accuracy (ACC), precision, recall, F1 score, Kappa test error histogram, and confusion matrix, respectively. The area under the receiver operating characteristic curves, analysis, testing, validation, landslides, and study areas were found between 0.873 and 0.911. The susceptibility map had a high prediction rate in which high and very high susceptible zones corresponded to 26% of the study area including 82% of the recorded landslides.

Landslides and other disastrous natural catastrophes jeopardise natural resources, assets, and people’s lives. As a result, future resource management will necessitate landslide susceptibility mapping (LSM) using the best conditioning factors. In Aqabat Al-Sulbat, Asir province, Saudi Arabia, the goal of this study was to find optimal conditioning parameters dependent hybrid LSM. LSM was created using machine learning methods such as random forest (RF), logistic regression (LR), and artificial neural network (ANN). To build ensemble models, the LR was combined with RF and ANN models. The receiver operating characteristic (ROC) curve was used to validate the LSMs and determine which models were the best. Then, utilising random forest (RF), classification and regression tree (CART), and correlation feature selection, sensitivity analysis was carried out. Through sensitivity analysis, the most relevant conditioning factors were determined, and the best model was applied to the important parameters to build a highly robust LSM with fewer variables. The ROC curve was also used to evaluate the final model. The results show that two hybrid models (LR-ANN and LR-RF) were predicted the very high as 29.67–32.73 km² and high LS regions as 21.84–33.38 km², with LR predicting 22.34km² as very high and 45.15km² as high LS zones. The LR-RF appeared as best model (AUC: 0.941), followed by LR-ANN (AUC: 0.915) and LR (AUC: 0.872). Sensitivity analysis, on the other hand, allows for the exclusion of aspects, hillshade, drainage density, curvature, and TWI from LSM. The LSM was then predicted using the LR-RF model based on the remaining nine conditioning factors. With fewer variables, this model has achieved greater accuracy (AUC: 0.927). This comes very close to being the best hybrid model. As a result, it is strongly advised to choose conditioning parameters with caution, as redundant parameters would result in less resilient LSM. As a consequence, both time and resources would be saved, and precise LSM would indeed be possible.

As a clean energy, natural gas hydrate should be developed and utilised. The greatest concerns for natural gas hydrate development being still at a preliminary stage are that natural gas hydrate development could cause explosions or environmental disasters. This manuscript addresses these concerns, analyses the principles of natural gas hydrate development, conducts a survey on natural gas hydrate development practice and points out that the most important physical process of natural gas hydrate development is gasification. Restricted by the heat supply speed of the stratum, the development of natural gas hydrate is relatively slow, and large-scale and uncontrollable gasification will not happen. Its development will also not seriously influence marine water, ecological environment or the atmosphere. Conventional problems as seabed collapse and seafloor landslide may occur, but, overall, natural gas hydrate development is safe. These views are consistent with two experiments conducted by the Chinese government in the South China Sea.

Understanding the effect of acid rain to landslides is crucial for a better landslide risk assessment. This work aims to reveal the unsuspected but key role of acid rain in Panzhihua airport landslide, China. Firstly, we propose a hypothesis that acid rain may aggravate the slaking behavior of mudstone at weak interlayer and make it more fragmented, eventually further reducing its shear strength and predisposing the Panzhihua airport landslide. Subsequently, mudstone samples are subjected to slaking durability test, respectively, using water with a pH of 7 and two dilute hydrochloric acid solution with pH of 5 and 3. Slaking durability index (Idₙ) is adopted aiming to quantitatively evaluate the impact of acid rain on the slaking. Moreover, the mechanisms of acid rain affecting the slaking behavior of mudstone are revealed by (1) analyzing cation compositions changes in different pH slaking fluid and (2) observing micro-structure change of mudstone-chip before and after acid rain treatment. Finally, three works are conducted as evidences to prove that acid rain indeed plays a key role in the occurrence of Panzhihua airport landslide, including (1) analysis of the link between the slaking behavior of mudstone and its shear strength, (2) comparison of cations between spring water at the edge of the toe of landslide and acid rain, and (3) comparison of mineral contents of mudstone samples collected from different locations. These findings have implications for comprehensively analyzing the formation mechanism of landslide in acid rain area (such as Europe, North America, and China).