Mountainous Terrain Research Articles

Real-time rainfall projection in hilly areas using ARLSTM model: a case of Dharamshala, India

Abstract Rainfall forecasting is essential in many industries, including agriculture, hydrology, urban planning, disaster management, and renewable energy. However, typical forecasting approaches frequently fail to reflect the complex spatiotemporal dynamics inherent in rainfall patterns, particularly in hilly areas. Since, the mountainous terrain influences wind patterns, moisture flow, and temperature inversions which affect precipitation patterns, adding challenge to meteorologists. So, designing a predictive forecasting model is essential for people and governments to make preventive measures in time. In recent years, the introduction of Internet of Things (IoT) technology and advances in machine learning (ML) techniques have provided a promising route for improving rainfall prediction accuracy. Real-time data on environmental elements such as temperature, wind speed, air pressure, and humidity can be collected using IoT sensor networks. This research paper investigates the efficacy of the Auto-Regressive Integrated Moving Average - Long Short-Term Memory (ARIMA-LSTM) model for real-time rainfall prediction in hilly towns like Dharamshala. Utilizing real-time data collected from sensors and using edge to preprocess it before sending it to the cloud the study explores the integration of traditional time series analysis techniques with deep learning architectures to enhance predictive accuracy. The propounded model is paralleled against alternative forecasting approaches and shows a significant reduction in RMSE, RRMSE, and MSE. Furthermore, it had a 0% Absolute Percentage Bias (APB), a high Nash-Sutcliffe Efficiency (NSE) of 0.99, and a low Theil's coefficient (U2) of 0.01. These results demonstrate that the ARLSTM model outperforms other forecasting models in terms of rainfall prediction.

Floristic Checklist and Conservation Status of Woody Species in Relation to Gradient in Osomba Hills of Cross River National Park, Nigeria

Extant floristic data checklist and conservation status of woody species were determined in relationship with elevation gradient in Osomba hills of the Cross River National Park, Oban division. A total of 78 species of woody plants in 31 families were identified. The family Fabaceae recorded the highest number of species (13) followed by Malvaceae with 8 species, Annonaceae and Euphorbiaceae had 6 species each, Apocynaceae had 5 species, Irvingiaceae, Ochnaceae, and Olacaceae had 3 species each, Buseraceae, Clusiaceae, Combretaceae, Ebenaceae, Moraceae, Myristicaceae, Rubiaceae and Sapotaceae had 2 species each, Anacardiaceae, Anisophylleaceae, Asteraceae, Bombacaceae, Cecropioaceae, Gentianaceae, Hyperiaceae, Lecythiddaceae, Meliaceae, Passifloraceae, Polygalaceae, Rhizophoraceae, Rutaceae, Simaroubaceae and Violaceae had 1 species each. The investigation of the conservation status of the species using the IUCN redlist data revealed that 1 species (Berlinia korupensis Mackinder& Burgt) is critically endangered (CR), 6 species are near threatened (NT), 7 species are vulnerable (VU), 7 species are not evaluated (NE) and 54 species are of least concerned (LC). Life forms identified included 14 shrubs and 64 tree species in the study area. In the dry season, a total of 29 species including 5 shrubs and 24 trees were identified, while in the rainy season, a total of 57 species including 10 shrubs and 47 trees were identified. Plant diversity along elevation gradient was generally heterogeneous and could be influenced by many factors such as climate, spatial heterogeneity, biotic processes, and evolutionary history. Overall species richness of both shrubs and trees decreased along the elevation gradient whereas the tree species increased with the elevation. Anthropogenic factors and accessibility to the mountainous terrain could also contribute to the patterns of the plant diversity and tree community structure in the study area. Common anthropogenic activities observed were timber harvesting, firewood collections, and harvesting of plants for food and medicinal uses.

Exploring the Influence of Urban Density and Mountainous Topography on Local Wind Patterns: An experimental study of Hongkong, China

The urban wind flow of the canopy layer has a significant impact on the urban climate, as well as the ventilation, pollutant diffusion, and thermal comfort. Therefore, the related studies are gaining attention in the last decade. The purpose of this study is to figure out the impacts of urban morphology and mountain topography on the urban wind flow, taking the pedestrian level height of Hong Kong as an example. Three wind tunnel test series were conducted, exploring scenarios with/without proposed high-rise buildings, as well as urban areas in two large city centers with/without upstream mountain terrain. Results indicate a 300m high-rise building affects wind flow up to approximately 125m upstream, yielding a 12% of velocity increase. Street wind speed peaks when the approaching wind aligns within 25° of the street's orientation, with building height and deviation on street sides significantly impacting wind speeds compared to street width. Additionally, the results reveal that mountain height primarily influences the reduction of pedestrian-level wind velocity, with less effect from distance and mountain slope in relation to the urban area.

Geotechnical and Geophysical Characterization of the Aklesal Dablyang Landslide: Implications for Slope Stability

Landslides constitute one of the principal perils in Nepal, particularly within its hilly and mountainous terrains, where a confluence of geological fragility and climatic extremities engenders precarious landscapes. Such hazards precipitate considerable loss of life and property. This investigation centers on the Aklesal Dablyang landslide in Baglung district, a potent menace to local infrastructure, agricultural domains, and human lives. By deploying a synthesis of geotechnical (laboratory-based soil analysis) and geophysical (Electrical Resistivity Tomography (ERT)) methodologies, the intrinsic properties of the soil and rock substrata within the landslide precinct were meticulously examined. The findings reveal that the landslide comprises predominantly loose colluvial deposits with elevated moisture levels, resulting in reduced shear strength and heightened failure susceptibility. The study accentuates the pivotal influence of hydrological phenomena such as surface runoff and groundwater seepage in aggravating slope destabilization. These results underscore the exigency for efficacious risk mitigation strategies to diminish landslide impacts on vulnerable communities. The Aklesal Dablyang landslide exemplifies the intricate interplay of geological and hydrological dynamics within Nepal’s complex topographical context. This research delineates the geotechnical and geophysical determinants of slope stability, highlighting the prevalence of loose colluvial deposits exacerbated by substantial moisture content, which attenuates shear strength and heightens vulnerability to mass movement. ERT analyses divulged a stratigraphy dominated by clayey sand interspersed with cobbles and boulders, which exhibit pronounced susceptibility to mass displacement during intense monsoonal precipitation—a phenomenon exacerbated by climate change. Anthropogenic interventions, including deficient drainage systems and substandard construction methodologies, further destabilize slopes by escalating pore-water pressure and diminishing soil cohesion. The study accentuates the imperative for integrative risk management paradigms, encompassing resilient engineering solutions, hydrological controls, and community collaboration, to bolster resilience against such geo-hazards.

Spatiotemporal analysis of roadway terrains impact on large truck driver injury severity outcomes using random parameters with heterogeneity in means and variances approach

This study employs a partially temporally constrained modeling approach to examine spatiotemporal variations in driver injury severity in single-vehicle large truck crashes across different terrains in California, allowing for a nuanced understanding of how specific factors influencing injury outcomes may change over time. Utilizing crash data from January 1st, 2015, to December 31st, 2017, obtained from the Highway Safety Information System, this study categorizes terrains as flat, rolling, and mountainous terrain and employs a random parameter multinomial logit model with heterogeneity in means and variance to account for potential heterogeneity in crash injury outcomes. This approach helps understand how different terrains influence injury severities while allowing for parameter variability across observations. The analysis is further enriched by likelihood ratio tests to verify the stability and temporal transferability of the model estimates across different terrains and years. Notably, the study identifies truck overturning as the first and second event in a crash as a consistent parameter influencing injury severity across all years, emphasizing its importance regardless of terrain or time in single-vehicle large truck crashes. Furthermore, this study takes into account a wide range of variables, including driver characteristics, crash attributes, roadway characteristics, vehicle features, and environmental and temporal aspects. The findings highlight the importance of terrain-specific elements in traffic safety assessments and the need for focused measures to reduce serious injuries in truck crashes. The out-of-sample simulation revealed a significant increase in minor and severe injuries when flat terrain parameters were replaced with those from rolling or mountainous terrains. This research not only contributes to the existing literature by detailing the dynamics of injury severity in single-vehicle large truck crashes but also announces the utility of partially temporally constrained models in enhancing traffic safety management strategies.

A Fuzzy Spatial Multiple Criteria Analysis Methodology for Solid Waste Landfill Siting

The process of siting municipal solid waste landfills in Greece faces significant challenges due to land resource limitations, the country’s mountainous and water-permeable terrain, and strong public opposition. This study introduces a novel methodology for optimizing landfill sites on Lemnos Island in the North Aegean Sea using a Fuzzy Spatial Multiple Criteria Analysis (FSMCA) approach. By combining fuzzy sets theory, Geographic Information Systems (GIS), Analytic Hierarchy Process (AHP), spatial autocorrelation, spatial clustering and sensitivity analysis, this methodology addresses the uncertainties and complexities inherent in landfill siting. The decision problem is structured hierarchically into five levels to manage multiple criteria effectively. Criteria weights are determined using AHP, with discrete criteria graded according to Greek and EU guidelines, and continuous criteria evaluated through fuzzy sets theory. The region’s suitability is assessed using multiple criteria analysis, revealing that 9.7% of Lemnos Island is appropriate for landfill placement. Sensitivity analysis confirms the robustness of the methodology to changes in criteria weights. The case study demonstrates the practical application and benefits of FSMCA in a real-world scenario, underscoring its potential to improve sustainable waste management practices and inform policy making.

Characterizing Asymptotic Dependence between a Satellite Precipitation Product and Station Data in the Northern US Rocky Mountains via the Tail Dependence Regression Framework With a Gibbs Posterior Inference Approach

ABSTRACTThe use of satellite precipitation products (SPP) allows for precipitation information to be collected nearly globally, but questions remain regarding their ability to reproduce extreme precipitation over mountainous terrain. In this work, we assess the ability of the precipitation estimation from remotely sensed information using artificial neural networks‐climate data record (PERSIANN‐CDR) to capture daily precipitation extremes by comparing PERSIANN‐CDR with corresponding station data in the summer at remote locations in the northern US Rocky Mountains of Wyoming, Idaho, and Montana. The assessment utilizes the regular variation framework from extreme value theory and consists of two parts: (1) evaluating the extent to which PERSIANN‐CDR can capture precipitation extremes through inference on an asymptotic dependence parameter, concluding that the level of asymptotic dependence is moderate throughout the region; (2) developing a tail dependence regression modeling framework and a Gibbs posterior approach for inference to investigate the degree to which elevation and topographic heterogeneity impact the level of asymptotic dependence, finding that the inclusion of a set of meteorological covariates, when combined with the PERSIANN‐CDR output, yields an increased level of asymptotic dependence with station data.

Land surface temperature trends derived from Landsat imagery in the Swiss Alps

Abstract. The warming of high mountain regions caused by climate change is leading to glacier retreat, decreasing snow cover, and thawing permafrost, all of which have far-reaching effects on ecosystems and societies. Landsat Collection 2 provides multi-decadal land surface temperature (LST) data, principally suited for large-scale monitoring at high spatial resolution. In this study, we assess the potential to extract LST trends using Landsat 5, 7, and 8 time series. We conduct a comprehensive comparison of both LST and LST trends with data from 119 ground stations of the Intercantonal Measurement and Information System (IMIS) network, located at high elevations in the Swiss Alps. The direct comparison of Landsat and IMIS LST yields robust satellite data with a mean accuracy and precision of 0.26 and 4.68 K, respectively. For LST trends derived from a 22.6-year record length, as imposed by the IMIS data, we obtain a mean accuracy and precision of −0.02 and 0.13 K yr−1, respectively. However, we find that Landsat LST trends are biased due to unstable diurnal acquisition times, especially for Landsat 5 and 7. Consequently, LST trend maps derived from 38.5-year Landsat data exhibit systematic variations with topographic slope and aspect that we attribute to changes in direct shortwave radiation between different acquisition times. We discuss the origin of the magnitude and spatial variation of the LST trend bias in comparison with modeled changes in direct shortwave radiation and propose a simple approach to estimate the LST trend bias. After correcting for the LST trend bias, the remaining LST trend values average between 0.07 and 0.10 K yr−1. Furthermore, the comparison of Landsat- and IMIS-derived LST trends suggests the existence of a clear-sky bias, with an average value of 0.027 K yr−1. Despite these challenges, we conclude that Landsat LST data offer valuable high-resolution records of spatial and temporal LST variations in mountainous terrain. In particular, changes in the mountain cryosphere, such as glacier retreat, glacier debris cover evolution, and changes in snow cover, are preserved in the LST trends and potentially contribute to improved prediction of permafrost temperatures with large spatial coverage. Our study highlights the significance of understanding and addressing biases in LST trends for reliable monitoring in such challenging terrains.

Application of Aeromagnetic Survey in Detecting Potential Mineralization Zones Around Dongzhongla Deposit, Gangdese Metallogenic Belt

The Dongzhongla deposit is a skarn-type lead–zinc ore deposit located in the eastern segment of the Gangdese metallogenic belt, situated in the Xizang province, China. The high-altitude mountainous terrain of the region poses significant challenges to ground-based exploration. To facilitate more accurate mineral exploration in the deposit and its surrounding area, a high-resolution airborne magnetic survey was conducted over the mining area and its periphery. The airborne magnetic data were processed using derivative and Euler deconvolution methods, yielding results that reflect the geological structural features of the study area. By integrating the geological characteristics of the ore deposit, we inferred that the areas of magnetic anomaly extensions and the peripheries of other magnetic anomalies are favorable zones for mineralization, providing positive leads for further mineral exploration.

Study of the application of UAV and LiDAR to topographic survey of mountainous forest areas – Case study of Win 3 Wind Power Plant Project, Huong Hoa District, Quang Tri Province, Vietnam

Abstract Unmanned Aerial Vehicle (UAV) technology in conjunction with aerial photography and aerial Light Detection and Ranging (LiDAR) was used for topographic surveying of the Win 3 Wind Power Project area, a complex mountainous terrain situated in the Huong Hoa district of Quang Tri province, Vietnam with the primary objective to produce a map with a scale of 1:1,000 with a contour interval of 1.0 m, accompanied by 3D point clouds, a Digital Elevation Model (DEM), and an orthomosaic. Employing the UAV LiDAR system of DJI Matrice 300 RTK, we conduct ed survey flights, achieving a high point density laser scanning of 229 points/m2 and capturing ultra-high-resolution imagery at 4.22 cm/pixel. The integration of aerial LiDAR and photographic data results in a rich and detailed information repository regarding the surveyed terrain demonstrating that UAV LiDAR technology is a robust tool for geographic data collection, particularly in challenging terrain conditions in Vietnam. We also evaluated the accuracy of the created topographic map, achieving results that surpass the technical requirements and quality standards of the project. This underscores the potential and effectiveness of UAV LiDAR technology in the field of large-scale geographic data collection and mapping in Vietnam.

Airborne lidar intensity correction for mapping snow cover extent and effective grain size in mountainous terrain

ABSTRACT Differentially mapping snow depth in mountain watersheds from airborne laser altimetry is a valuable hydrologic technique that has seen an expanded use in recent years. Additionally, lidar systems also record the strength of the returned light pulse (i.e. intensity), which can be used to characterize snow surface properties. For near-infrared lidar systems, return intensity is relatively high over snow and inversely related to the effective grain size, a primary control on snow albedo. Raw intensity is also sensitive to laser range and incidence angle, however, and requires a correction for snow property retrieval that is especially pertinent in mountainous terrain. Here, we describe a workflow to correct the intensity using the plane trajectory, lidar scan angle, and lidar-derived topography. As a proof of concept for snow retrievals, we apply the workflow to an airborne 1064 nm lidar flight over a snow-covered mountain basin in the Colorado Rockies. Corrected intensity was empirically related to reflectance before delineating snow extent and retrieving grain size. Relative to the traditional snow classification derived from optical imagery, the lidar-derived snow extent covered 5.4% more area due to the fine resolution point cloud and absence of shadows common in optical imagery. The lidar-derived grain size retrievals had a MAE of 32 µm compared to those from field spectroscopy, which translated to a 1% error in snow albedo. We found high incidence angles yielded an overcorrection in intensity that introduced a high bias in the grain size distribution and, therefore, suggest using an incidence angle threshold (40°). Developing methods specifically for quantitative snow surface property retrievals from lidar intensity is timely and relevant as aerial lidar is increasingly being used to map snow depth for hydrologic and cryospheric studies.

Mountain Logistics: A Systematic Literature Review and Future Research Directions

Background: The sustainable development of mountain areas, which have fragile ecosystems, has increasingly attracted the attention of researchers and practitioners. Logistics systems are crucial in supporting these regions and addressing mountainous terrain’s unique challenges. While many studies have examined aspects of mountain logistics, a comprehensive and systematic review of the field is still lacking. Design/Methodology/Approach: This paper aims to fill the gap by systematically reviewing the existing literature on mountain logistics using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology. Results/Conclusions: We identify four main research foci: design of logistics infrastructure or vector, optimization of logistics systems, safety in logistics systems, and impact of logistics systems on mountain communities. In addition to categorizing these themes, we conduct a detailed descriptive analysis of published studies in this domain. Our findings highlight significant research gaps, particularly in integrating digital technologies, sustainable mass transportation solutions, and logistics systems’ socioeconomic and environmental impacts. We propose targeted directions for future research to advance sustainable logistics practices in mountain regions.

A forensic overview of deaths in mountainous terrain.

Deaths at high altitudes may arise from a range of quite disparate entities including trauma (e.g. falls), environmental factors (e.g. hypothermia and hypoxia), and pre-existing medical conditions (e.g. coronary artery disease). Unique conditions include high altitude pulmonary edema (HAPE) and high altitude cerebral edema (HACE) which may contribute to lethal mechanisms or precipitate a traumatic event. Forensic issues that may arise in these cases are logistical problems due to remote locations, sometimes with failure to find a body, delay in discovery and/or repatriation, prior local autopsies being performed with embalming, and the non-specificity of pathology markers. Traumatic deaths involve blunt force impacts, falls, suffocation, crush asphyxia and hypothermia. The assessment of cases requires careful integration of the autopsy findings with the medical history of the decedent and an accurate description of their behaviour in the hours leading up to death.

Preparing for war by fostering alpine winter tourism: Hermann Czant and his ideas on infrastructure for fighting in high altitude

Neither in historiography of alpine (winter) tourism nor in that on the First World War in mountainous regions has (Austro-)Hungarian mountaineer and officer Hermann Czant received a lot of attention, although he was amongst those, who promoted alpine winter tourism not least as an important training for military operations in a war in mountainous terrain such as the Alps or the Carpathians. This article analyses the ideas of Hermann Czant on the relationship between mountaineering, future military operations in high altitude and the construction of infrastructure in mountainous terrain and claims that he was probably looking for recapturing the Carpathians lost by Hungary in 1918–1920 through the fostering of alpine (winter) tourism and the building of infrastructure related to this process. At the same time he was very much aware of the integrative as well as disintegrative impact of the building of infrastructure in a mountainous environment. Therefore human agency and conduct took precedence over more or less permanent civilian or military infrastructure in such areas.

Precise Geoid Determination in the Eastern Swiss Alps Using Geodetic Astronomy and GNSS/Leveling Methods.

Astrogeodetic deflections of the vertical (DoVs) are close indicators of the slope of the geoid. Thus, DoVs observed along horizontal profiles may be integrated to create geoid undulation profiles. In this study, we collected DoV data in the Eastern Swiss Alps using a Swiss Digital Zenith Camera, the COmpact DIgital Astrometric Camera (CODIAC), and two total station-based QDaedalus systems. In the mountainous terrain of the Eastern Swiss Alps, the geoid profile was established at 15 benchmarks over a two-week period in June 2021. The elevation along the profile ranges from 1185 to 1800 m, with benchmark spacing ranging from 0.55 km to 2.10 km. The DoV, gravity, GNSS, and levelling measurements were conducted on these 15 benchmarks. The collected gravity data were primarily used for corrections of the DoV-based geoid profiles, accounting for variations in station height and the geoid-quasigeoid separation. The GNSS/levelling and DoV data were both used to compute geoid heights. These geoid heights are compared with the Swiss Geoid Model 2004 (CHGeo2004) and two global gravity field models (EGM2008 and XGM2019e). Our study demonstrates that absolute geoid heights derived from GNSS/leveling data achieve centimeter-level accuracy, underscoring the precision of this method. Comparisons with CHGeo2004 predictions reveal a strong correlation, closely aligning with both GNSS/leveling and DoV-derived results. Additionally, the differential geoid height analysis highlights localized variations in the geoid surface, further validating the robustness of CHGeo2004 in capturing fine-scale geoid heights. These findings confirm the reliability of both absolute and differential geoid height calculations for precise geoid modeling in complex mountainous terrains.

Combining Remote Sensing Data and Geochemical Properties of Ultramafics to Explore Chromite Ore Deposits in East Oltu Erzurum, Turkey

The present research’s main objective was to apply thorough exploration approaches that combine remote sensing data with geochemical sampling and analysis to predict and identify potential chromitite locations in a complex geological site, particularly in rugged mountainous terrain, and differentiate the ultramafic massif containing chromitite orebodies from other lithologies. The ultramafic massif forming the mantle section of the Kırdağ ophiolite, located within the Erzurum–Kars Ophiolite Zone and emerging in the east of Oltu district (Erzurum, NE Turkey), was selected as the study area. Optimum index factor (OIF), false-color composite (FCC), decorrelation stretch (DS), band rationing (BR), minimum noise fraction (MNF), and principal and independent component analyses (PCA-ICA) were performed to differentiate the lithological features and identify the chromitite host formations. The petrography, mineral chemistry, and whole-rock geochemical properties of the harzburgites, which are the host rocks of chromitites in the research area, were evaluated to verify and confirm the remote sensing results. In addition, detailed petrographic properties of the pyroxenite and chromitite samples are presented. The results support the existence of potential chromitite formations in the mantle section of the Kırdağ ophiolite. Our remote sensing results also demonstrate the successful detection of the spectral anomalies of this ultramafic massif. The mineral and whole-rock geochemical features provide clear evidence of petrological processes, such as partial melting and melt–peridotite interactions during the harzburgite formation. The chromian spinels’ Cr#, Mg#, Fe3+, Al2O3, and TiO2 concentrations indicate that the harzburgite formed in a fore-arc environment. The Al2O3 content and Mg# of the pyroxenes and the whole-rock Al2O3/MgO ratio and V contents of the harzburgite are also compatible with these processes. Consequently, the combined approaches demonstrated clear advantages over conventional chromitite exploration techniques, decreasing the overall costs and supporting the occurrence of chromite production at the site.

A Spatially‐Distributed Machine Learning Approach for Fractional Snow Covered Area Estimation

AbstractSnowpack in mountainous areas often provides water storage for summer and fall, especially in the Western United States. In situ observations of snow properties in mountainous terrain are limited by cost and effort, impacting both temporal and spatial sampling, while remote sensing estimates provide more complete spacetime coverage. Spatial estimates of fractional snow covered area (fSCA) at 30m are available every 16 days from the series of multispectral scanning instruments on Landsat platforms. Daily estimates at 463m spatial resolution are also available from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on the Terra satellite. Fusing Landsat and MODIS fSCA images creates high resolution daily spatial estimates of fSCA that are needed for various uses: to support scientists and managers interested in energy and water budgets for water resources and to understand the movement of animals in a changing climate. Here, we propose a new machine learning approach conditioned on MODIS fSCA, as well as a set of physiographic features, and fit to Landsat fSCA over a portion of the Sierra Nevada USA. The predictions are daily 30m fSCA. The approach relies on two stages of spatially‐varying models. The first classifies fSCA into three categories and the second yields estimates within (0, 100) percent fSCA. Separate models are applied and fitted within sub‐regions of the study domain. Compared with a recently‐published machine learning model (Rittger, Krock, et al., 2021), this approach uses spatially local (rather than global) random forests, and improves the classification error of fSCA by 16%, and fractionally‐covered pixel estimates by 18%.

Effects of oncoming wind speed and turbulence intensity on wind characteristics of a simplified hill and ridge

Supertall buildings and long-span bridges are significantly affected by wind-induced vibrations, and the wind fields in mountainous areas are highly complex and influenced by the oncoming wind speed and turbulence intensity. To accurately determine the variation patterns of wind characteristics in mountainous areas under different oncoming wind speeds and turbulence intensities, large eddy simulation (LES) was employed to analyze wind fields over simplified hill and ridge models. By setting different basic wind speeds (5, 10, 15, and 20 m/s) and turbulence intensities (1%, 3%, 5%, and 10%) at the inlet, the variation patterns of wind characteristics over the simplified hill and ridge under atmospheric boundary layer inflow were studied, revealing wind field flow mechanisms. The results indicate that the wind characteristics on the leeward side of the simplified hill and ridge are significantly influenced by the oncoming wind speed and turbulence intensity. Increasing the oncoming wind speed and turbulence intensity leads to decreased wind profile deceleration, reduced changes in wind direction and attack angles, and increased wind speed amplification factor. In addition, the turbulence fluctuations, power spectra, and coherence function between two points on the leeward side increase with the oncoming wind speed and turbulence intensity. The turbulent integral scale decreases with an increase in the oncoming wind speed and turbulence intensity. As the wind speed and turbulence increase, the size of the recirculation bubble gradually decreases, with its center moving closer to the wall surface. In the vorticity field, the number of smaller-scale three-dimensional turbulent vortices near the hill and ridge increases. These differences in flow characteristics are the fundamental causes of the changes in wind characteristics. High wind speeds and turbulence intensities typically result in higher kurtosis and skewness, with significant non-Gaussian characteristics in the fluctuating wind. Traditional wind load design specifications for building architecture based on a Gaussian distribution may not be applicable to mountainous terrain. In practical engineering, the influences of the oncoming wind speed and turbulence intensity on wind characteristics should be fully considered.

Evolution of Helicopter Services and Their Development From a Medical Standpoint: Nepal

Nepal is a landlocked country in the heart of the Himalayan region. Its remote villages and mountainous terrain provide a unique set of challenges in health care. As a result, the last decade has seen major developments in the use of helicopters in retrieval medicine. This has helped to improve accessibility to health care in even the most rural areas. Despite the assorted challenges, the helicopter emergency medical service in Nepal is a part of the medical system. This article provides a comprehensive overview of the history of helicopter and air medical services in Nepal.

Differential evolution with multi-strategies for UAV trajectory planning and point cloud registration

The present study introduces a novel adaptive algorithm, MELSHADE-cnEpSin, which aims to enhance the performance of LSHADE-cnEpSin, which is not only stands out as one of the most competitive versions of differential evolution but also holds the distinction of being one of the CEC winner algorithms. Compared to the original methodology, four main distinctions are presented. To begin with, we adopt an adaptive selection mechanism (ASM) of crossover rate Cr value based on the external archive to rechoose a suitable value. In the next place, a nonlinear population reduction strategy using Sigmoid function is employed to improve population distribution. Additionally, a restart strategy is implemented to mitigate the risk of algorithmic convergence towards suboptimal solutions. Furthermore, the performance of MELSHADE-cnEpSin was evaluated using standard CEC2017 and CEC2022 test suites in conjunction with nine CEC-winning algorithms (L-SHADE, EBOwithCMAR, AGSK, LSHADE-SPACMA, LSHADE-cnEpSin, ELSHADE-SPACMA, EA4eig, MadDE and APGSK-IMODE) as well as two novel algorithms (ACD-DE and MIDE). Furthermore, MELSHADE-cnEpSin was effectively employed to address the challenge of UAV trajectory planning in intricate mountainous terrain and underwent simulation with point cloud registration cases utilizing a rapid global registration dataset, thereby showcasing the potential of MELSHADE-cnEpSin in tackling real-world optimization problems.