Selecting the optimal intensity measure (IM) is essential for accurately assessing the seismic performance of the submarine shield tunnels in the layered liquefiable seabed. However, current research relies on simplistic ranking or filtering methods that neglect the different contributions of each evaluation criterion on IM's overall performance. To address this, this study begins by developing a numerical simulation method for nonlinear dynamic analysis, considering joint deformation, ocean environmental loads, and soil liquefaction, which is validated by experimental and theoretical methods. Subsequently, a fuzzy multiple criteria decision-making (FMCDM) method based on fuzzy probabilistic seismic demand models (FPSDM) is proposed, which integrates the fuzzy analytical hierarchical process (FAHP) for calculating weights and the fuzzy technique for order preference by similarity to ideal solution (FTOPSIS) for ranking IM alternatives. Finally, tunnel damage is classified into four states employing joint opening as the index for measuring damage, then the seismic fragility analysis is conducted. The results indicate that the optimal IM of a submarine shield tunnel situated in layered liquefiable seabed is sustained maximum velocity (SMV). Furthermore, the comparison between the fragility curves established using SMV and peak ground acceleration (PGA) reveals PGA, a frequently employed IM, notably undervaluing the seismic hazard.

This paper has attempted to determine the weighting levels of the soil and ground motion parameters (engineering bedrock depth (EBd), average shear wave velocity (Vs30), fundamental frequency (f0), peak ground acceleration (PGA), Joyner-Boore distance (Rjb), and epicenter distance (Repi)) in reflecting the actual damage status after the 2023 Kahramanmara & scedil; earthquakes, which have a wide impact area of 11 provinces. The analytical hierarchy method (AHP), a multi-criteria decision-making (MCDM) process, was used to analyze these parameter data sets obtained from 44 Disaster and Emergency Management Presidency of T & uuml;rkiye (AFAD) stations (Gaziantep, Hatay, Kahramanmara & scedil;, and Osmaniye). The priority order of the parameters before the analysis was systematically collected. These parameters were categorized into soil, ground motion and earthquake source-path properties. Considering the literature, these characteristics and their combined effects were systematically weighted with AHP under five groups. According to the weighted groups in the scope of the study, the actual damage data can be determined with a minimum accuracy rate of 70% (Group 1). In comparison, the best performance evaluation was 82% (Group 5). The parameter order and weights in the actual damage data evaluation are suggested as EBd-%28, PGA-%24, Vs30-%19, Rjb-%14, f0-%10, and Repi-%5 considering the very high accuracy rate of Group 5. This suggested weighting allows the rapid and effective estimation of the damage distribution after a possible earthquake only with soil, ground motion and earthquake source-path characteristics, even in cases where reliable structure data cannot be obtained.

Flash floods are one of the most prevalent natural disasters, triggering deadly damage to homesteads, crops, infrastructure, road networks, communications, and the natural environment in the Haor (Wetland) region of Bangladesh. The purpose of the study aims to identify eleven (11) hydro-geomorphological driving factors, namely elevation, slope, aspect, rainfall, land use and land cover (LULC), lithology, soil type, topographic wetness index (TWI), Normalized Difference Vegetation Index (NDVI), distance from the river, and drainage density, which are being explored for mapping flood-prone areas. This research has produced a flash flood susceptibility map using the Analytical Hierarchy Process (AHP) and Analytical Network Process (ANP), which are interactive decision-making approaches under multi-criteria decision analysis (MCDA) in ArcGIS 10.8. The findings of this study showed that the susceptibility to flood hazards differs significantly among the seven Haor districts. As a result of the ANP and AHP, a more significant proportion of the Haor region is moderately susceptible to flooding (8685.09-9275.15 sq. km.), whereas 35.34 %-38.32 % (7069.70-7668.67 sq. km.) accounts for high susceptible to flooding. Furthermore, 200 flood locations were identified in the northeast Haor region, where 140 (70 %) randomly selected floods were used for training, and the remaining 60 (30 %) were employed for validation purposes. The validation results showed that the AHP model had greater prediction accuracy (the area under the receiver operating curve (AUROC) = 92.1 %) than the ANP (AUROC = 88.5%) model. Therefore, the study findings can be helpful for researchers, academics, policymakers, and planners for sustainable flood mitigation strategies, particularly in Haor areas.

Landslides are significant geological hazards in mountainous regions, arising from both natural forces and human actions, presenting serious environmental challenges through their extensive damage to properties and infrastructure, often leading to casualties and alterations to the landscape. This study employed GIS-based techniques to evaluate and map the landslide susceptibility in the Bekhair structure located within the Zagros mountains of Kurdistan, northern Iraq. An inventory map containing 282 landslide occurrences was compiled through intensive field investigations, as well as the interpretation of remote sensing data and Google Earth images. Ten potential influencing factors, including elevation, rainfall, lithology, slope, curvature, aspect, LULC, NDVI, distance to roads and rivers, were selected to construct susceptibility maps by integrating the frequency ratio (FR) and analytical hierarchy process (AHP) approaches, with the goal of understanding how these factors relate to landslides occurrence. The Bekhair core area was divided into 5 hazard zones on the landslide susceptibility maps. The regions classified as very low and low hazard zones are mainly occur in flat or gently sloping plains that characterized by resistant rocks, dense vegetation, minimal rainfall, shallow valleys, and are distant from riverbanks and roads. The areas designated as high and very high hazard zones are found in steep slopes and rough terrain with bare soil, intense weathering, high rainfall, sparse vegetation, highly fractured rocks, deep valleys, and close proximity to construction projects. The moderate hazard zones are mainly located between the other 4 zones across the Bekhair anticline. Results of the susceptibility analysis indicate that the occurrence of landslides in Kurdistan mountains are primarily controlled by factors related to the tectonic structure, surface characteristics and environmental conditions, such as rock lithology (competency), terrain slope, rainfall intensity, and human impacts. The delineation of landslide hazard zones offers important guides for government decision-makers engaged in regional planning, infrastructure development, and the formulation of strategies to mitigate landslides and protect lives and properties in Kurdistan. The accuracy of susceptibility maps was evaluated using the R-index and the AUC-ROC curve. The landslide susceptibility index (LSI) values allocated to different susceptibility classes derived from both FR and AHP models are consistent with the values obtained from the R-index. Moreover, the FR model demonstrated superior performance compared to the AHP model, with a success rate of 85.3% and a predictive rate of 81.2%, in contrast to the AHP model's success rate of 75.2% and predictive rate of 72.4%.

Uncertainty plays a key role in hydrological modeling and forecasting, which can have tremendous environmental, economic, and social impacts. Therefore, it is crucial to comprehend the nature of this uncertainty and identify its scope and effects in a way that enhances hydrological modeling and forecasting. During recent decades, hydrological researchers investigated several approaches for reducing inherent uncertainty considering the limitations of sensor measurement, calibration, parameter setting, model conceptualization, and validation. Nevertheless, the scope and diversity of applications and methodologies, sometimes brought from other disciplines, call for an extensive review of the state-of-the-art in this field in a way that promotes a holistic view of the proposed concepts and provides textbook-like guidelines to hydrology researchers and the community. This paper contributes to this goal where a systematic review of the last decade's research (2010 onward) is carried out. It aims to synthesize the theories and tools for uncertainty reduction in surface hydrological forecasting, providing insights into the limitations of the current state-of-the-art and laying down foundations for future research. A special focus on remote sensing and multi-criteria-based approaches has been considered. In addition, the paper reviews the current state of uncertainty ontology in hydrological studies and provides new categorizations of the reviewed techniques. Finally, a set of freely accessible remotely sensed data and tools useful for uncertainty handling and hydrological forecasting are reviewed and pointed out.

Uncontrolled wildfires pose a significant threat, potentially causing extensive damage to biodiversity, soil quality and human resources. It's crucial to swiftly detect and predict these wildfires to minimize their catastrophic consequences. To address this, our research introduces a wildfire prediction model that ranks cities based on risk leveraging multi-criteria decision-making (MCDM) to systematically assess conflicting factors in decision-making. This model integrates wildfire risks into a city's resilience strategy, utilizing fuzzy set theory to manage imprecise data and uncertainties. As part of this approach, we compile a new dataset encompassing weather patterns, vegetation types, terrain features and population density across various Californian cities. Ultimately, the model assesses and ranks the wildfire risk for each city in California.

A series of hydrogeologic framework model (HFM)-based steady- and transient-state numerical simulations is performed first using a coupled subsurface flow-transport numerical model to analyze groundwater flow and salt transport in an actual three-dimensional complex coastal aquifer system before and during groundwater pumping. A series of analytic hierarchy process (AHP)-based multi-criteria evaluations is then performed applying a multi-criteria decision-making approach to determine optimal pumping location and rate for a new pumping well in the complex coastal aquifer system during groundwater pumping. The complex coastal aquifer system is composed of six anisotropic fractured porous geologic media (five rock formations and one fault) and three isotropic porous geologic media (three soil formations) and shows high geometric irregularity and significant heterogeneity and anisotropy of the nine geologic media. Results of the steady-state numerical simulations show successful model calibration with 26 measured groundwater levels and two observed seawater intrusion front lines. The latter two are determined by spatial interpolation and extrapolation of electrical conductivity logging data and electrical resistivity survey data, respectively. Based on the status and prospect of necessary water uses and available groundwater resources, the field observations of groundwater and seawater intrusion, and the analyses of the steady-state numerical simulation after the model calibration, six candidate pumping locations are selected for the new pumping well. In addition, from six preliminary individual transient-state numerical simulations, maximum pumping rates at the six candidate pumping locations are calculated first, and a set of six incremental candidate pumping rates is then assigned at each of the six candidate pumping locations. Results of the transients-state numerical simulations show that groundwater flow and salt transport are spatially and temporally changed, and seawater intrusion is further intensified by groundwater pumping. In addition, the magnitudes of such spatial and temporal changes and intensification are significantly different depending on the candidate pumping locations and rates. Results of the steady- and transient-state numerical simulations also show that both complexity (geometric irregularity, heterogeneity, and anisotropy including the fault) and topography have significant effects on the spatial distributions and temporal changes of groundwater flow and salt transport in the coastal aquifer system before and during groundwater pumping. In addition, results of statistical estimations of the mesh Peclet and Courant numbers confirm acceptabilities of minimizing numerical dispersion in the steady- and transient-state numerical simulations. Based on the analyses of the transient-state numerical simulations, eight multiple criteria are chosen to judge, prioritize, and rank the six candidate pumping locations and six candidate pumping rates for optimal pumping. Results of the multi-criteria evaluations determine the optimal pumping location and rate for the new pumping well among the six candidate pumping locations and six candidate pumping rates. In addition, results of consistency checks confirm consistencies of judgments in the multi-criteria evaluations. Numerical simulations with successful model calibration show that spatial and temporal changes in groundwater flow and salt transport significantly depend on candidate pumping locations and rates Statistical estimations of the mesh Peclet and Courant numbers confirm acceptabilities of minimizing numerical dispersion in the numerical simulations Multi-criteria evaluations determine optimal pumping location and rate, and consistency checks confirm consistencies of judgments in the multi-criteria evaluations

Under the effects of saltwater intrusion from rising sea water levels, climate change, and socioeconomic issues, the Nga Nam district in Vietnam has suffered damage to its agriculture and changes in agricultural land use. This study aimed to investigate the factors that influenced land use changes and to propose approaches to limit the changes in agricultural land use. The damage caused by saltwater intrusion on agricultural production was evaluated via the use of secondary data collected from the Department of Infrastructure Economics of the Nga Nam district in the period of 2010-2021. The results show that during the 2010-2015 period, agricultural production areas were affected in 2010, 2012, and 2015. In the period of 2015-2021, the trend of saltwater intrusion along the damaged area remarkably decreased due to the work of saltwater-preventing structures. In this period, the area of annual plants increased, while that of fruit trees decreased. In the area comprising annual plants, the area using the triple rice land use type converted into an area using the double rice and double rice-fish ones. Lands for fruit trees transitioned from mixed farming to specialized farming to raise the economic efficiency for farmers. These changes were affected by four main factors: the physical factor, the economy, society, and the environment. The environmental and economic factors were seen to play the most important role as drivers of changes in land use. The factors of saltwater intrusion and acid-sulfate-contaminated soil, consumer markets, floods, drought, profit, and investments were noted to be significant drivers in agricultural land use change. Thus, both structural and non-structural approaches are suggested to inhibit the safeguard changes in the future.

Floods are a widespread natural disaster with substantial economic implications and far-reaching consequences. In Northern Pakistan, the Hunza-Nagar valley faces vulnerability to floods, posing significant challenges to its sustainable development. This study aimed to evaluate flood risk in the region by employing a GIS-based Multi-Criteria Decision Analysis (MCDA) approach and big climate data records. By using a comprehensive flood risk assessment model, a flood hazard map was developed by considering nine influential factors: rainfall, regional temperature variation, distance to the river, elevation, slope, Normalized difference vegetation index (NDVI), Topographic wetness index (TWI), land use/land cover (LULC), curvature, and soil type. The analytical hierarchy process (AHP) analysis assigned weights to each factor and integrated with geospatial data using a GIS to generate flood risk maps, classifying hazard levels into five categories. The study assigned higher importance to rainfall, distance to the river, elevation, and slope compared to NDVI, TWI, LULC, curvature, and soil type. The weighted overlay flood risk map obtained from the reclassified maps of nine influencing factors identified 6% of the total area as very high, 36% as high, 41% as moderate, 16% as low, and 1% as very low flood risk. The accuracy of the flood risk model was demonstrated through the Receiver Operating Characteristics-Area Under the Curve (ROC-AUC) analysis, yielding a commendable prediction accuracy of 0.773. This MCDA approach offers an efficient and direct means of flood risk modeling, utilizing fundamental GIS data. The model serves as a valuable tool for decision-makers, enhancing flood risk awareness and providing vital insights for disaster management authorities in the Hunza-Nagar Valley. As future developments unfold, this study remains an indispensable resource for disaster preparedness and management in the Hunza-Nagar Valley region.

The Qinghai-Tibet Plateau (QTP), where is underlain by the highest and most extensive mid-altitude permafrost, is undergoing more dramatic climatic warming than its surrounding regions. Mapping the distribution of permafrost is of great importance to assess the impacts of permafrost changes on the regional climate system. In this study, we applied logistic regression model (LRM) andmulti-criteria analysis (MCA) methods to map the decadal permafrost distribution on the QTP and to assess permafrost dynamics from the 1980s to 2000s. The occurrence of permafrost and its impacting factors (i.e., climatic and topographic elements) were constructed from in-situ field investigation-derived permafrost distribution patterns in 4 selected study regions. The validation results indicate that both LRM and MCA could efficiently map the permafrost distribution on the QTP. The areas of permafrost simulated by LRM and MCA are 1.23 x 10(6) km(2) and 1.20 x 10(6) km(2), respectively, between 2008 and 2012. The LRM and MCA modeling results revealed that permafrost area has significantly decreased at a rate of 0.066 x 10(6) km(2) decade(-1) over the past 30 years, and the decrease of permafrost area is accelerating. The sensitivity test results indicated that LRM did well in identifying the spatial distribution of permafrost and seasonally frozen ground, and MCA did well in reflecting permafrost dynamics. More parameters such as vegetation, soil property, and soil moisture are suggested to be integrated into the models to enhance the performance of both models. (C) 2018 Published by Elsevier B.V.