Local Terrain Research Articles

A novel flood conditioning factor based on topography for flood susceptibility modeling

Flood is one of the most devastating natural hazards. Employing machine learning models to construct flood susceptibility maps has become a pivotal step for decision-makers in disaster prevention and management. Existing flood conditioning factors inadequately account for regional characteristics of flood in the depiction of topography, potentially leading to an overestimation of flood susceptibility in flat areas. Addressing this gap, this study proposes a novel flood conditioning factor, local convexity factor (LCF), to enhance the accuracy of flood susceptibility modeling. Initially, LCF is computed based on a standard normal Gaussian surface to highlight elevation variations in local terrain. Subsequently, LCF is applied to flood susceptibility modeling using seven machine learning models across four distinct basins. Comparative analysis is conducted between flood susceptibility maps with and without the application of LCF to evaluate its impact on flood susceptibility modeling. The results demonstrate that the proposed LCF can enhance the accuracy of flood susceptibility modeling to varying degrees, across the four basins investigated. The Fujiang basin exhibited the most substantial improvement, with its AUC improved from 0.861 to 0.886, Producer’s Agreement improved from 0.869 to 0.899, and Overall Agreement improved from 0.778 to 0.811. Comparation with hydrodynamic inundation maps shows that particularly in relatively flat terrain areas, flood susceptibility maps incorporating LCF offer more precise delineation between flood-prone and non-flood-prone zones. This research holds potential for widespread application in the prediction of flood susceptibility using machine learning models, providing a novel perspective for enhancing their accuracy

Engineering Study of Erosion to Protect the Gometseri Alazani and Pirikiti Alazani Catchment Area (Kakheti region, Akhmeta municipality, Tusheti)

Soil erosion is a global environmental issue that reduces soil productivity, affects water quality, leads to sediment deposition, and increases the likelihood of agricultural land degradation. Combating erosion requires both quantitative and qualitative assessment of potential soil erosion on specific sites, along with knowledge of local terrain, soil types, land use systems, and management practices. From a theoretical and practical point of view, erosion processes are widespread and dangerous in Georgia, especially in mountainous areas. Slopes that have not been eroded or have not formed ravines are rare. Intensive landslides and mudslides are also observed here. Changes in factors associated with erosion are of interest, including the impact of climate change and human activity on components of erosional geosystems, especially on soil cover. Modern geoinformation systems (GIS) provide qualitatively new opportunities for research, modelling and optimization of the use of erosion-prone lands. Despite its wide application in many spheres of human activity, its potential in erosion research has not yet been fully realized. This article discusses about the Gometseri Alazani and Pirikiti Alazani (Akhmeta Municipality, Tusheti) catchment areas. The erosion-landslide processes developed in the research area are studied using the Revised Universal Soil Loss Equation (RUSLE). Erosion-vulnerable areas have been identified, where it is necessary to carry out additional engineering protection measures.

Validation and Analysis of the ICESat-2 ATL11 Product: A Case Study of Lake Vostok

Elevation changes are crucial input data for mass balance assessment based on satellite altimetry. However, due to the accuracy limitations of altimetry data and mass conversion models, a significant uncertainty remains in estimating the mass balance of the Antarctic ice sheet, especially in East Antarctica. The ICESat-2 photon altimetry satellite enhances the precision of ice sheet surface elevation measurements to an accuracy of 2–4 cm. This improvement is particularly beneficial for detecting subtle elevation changes in East Antarctica. The ATL11 product from ICESat-2 offers a time series of ice surface elevations across Antarctica, enabling direct calculation of elevation change rates. To evaluate the capability of the ATL11 product in accurately depicting detailed elevation changes within the local terrain, we selected the Vostok Subglacial Lake as the validation region. Our research involves comparing fitted surface elevation change rates with in-situ data, while also considering surface mass balance, wind, and surface elevation to analyze the factors contributing to small differences in elevation changes within the local lake area. This analysis aims to identify the factors contributing to the minor variations in elevation changes within the local lake area. According to our analysis, the Vostok Lake surface elevation change rate is 2.00 ± 0.77 cm yr−1 from April 2019 to June 2023, with an average period of 356 ± 81 days. The results demonstrate that the ATL11 elevation product sequence has the potential to accurately characterize subtle elevation changes and seasonal variations in the Antarctic ice sheet.

Improving horizon computation algorithm with quasirandom sequences

The topographic horizon can be used to estimate shading for applications requiring an accurate solar resource estimate, including the effects of the local terrain. With high-resolution topography data, horizon estimation significantly contributes to the computation time needed for irradiance simulations. Approximate horizon algorithms have been proposed previously, sampling topography in a finite number of directions. This paper extends this idea by presenting a sampling approach based on the quasirandom sequences. The sampling strategy is optimized with 300,000 km2 of topography data from Europe and the USA. We demonstrate that our algorithm is several orders of magnitude faster than the precise horizon calculation and significantly improves the previous approximate algorithms. In particular, at the ground level with a topography resolution of 1 m/pixel, the proposed method achieved the same accuracy as the old approximate method four times faster. The horizon is one of the key methods in geographical information science and a part of many modeling tools in the built environment. Improvements in computation time benefit all applications that require irradiance modeling, e.g., the yield of vehicle- and building-integrated photovoltaic or temperature modeling of buildings.

Downwind Warming of Cities? Inequal Heat Distribution Attributed to Winds

Heat exposure within urban areas is strikingly uneven, posing disproportionate risks to certain communities. While nature-based solutions have gained attention, the role of wind in distributing heat remains less understood in an urban planning scale. This study assessed the interaction between wind direction, speed, and heat advection in Taipei Basin during summers from 2011 to 2020, using data from densely installed meteorological stations. A novel method was developed to capture multiple wind directions while accounting for local terrain and urban effects. Results revealed that wind-induced heat advection is complicated by local terrain and nearby cities in the conurbation, varying heat distribution. Windy conditions were mostly warmer than calm conditions, with north-westerly and westerly winds causing the strongest heat advection. Heat advection increased with wind speeds up to 5.4 m/s and decreased thereafter. Substantial intra-urban differences in heat advection were observed, reaching 4.33°C daytime and 3.34°C nighttime. Upwind areas were not necessarily cooler, while some downwind areas at mountain foot experienced greater warming. Up to 2.6SD of advected heat magnitude was found downwind at night. These findings underscore inequitable heat transfer to areas that do not generate heat and the critical need for wind-sensitive planning for both city-region and inter-urban areas.

Neural Network-Based Fusion of InSAR and Optical Digital Elevation Models with Consideration of Local Terrain Features

InSAR and optical techniques represent two principal approaches for the generation of large-scale Digital Elevation Models (DEMs). Due to the inherent limitations of each technology, a single data source is insufficient to produce high-quality DEM products. The increasing deployment of satellites has generated vast amounts of InSAR and optical DEM data, thereby providing opportunities to enhance the quality of final DEM products through the more effective utilization of the existing data. Previous research has established that complete DEMs generated by InSAR technology can be combined with optical DEMs to produce a fused DEM with enhanced accuracy and reduced noise. Traditional DEM fusion methods typically employ weighted averaging to compute the fusion results. Theoretically, if the weights are appropriately selected, the fusion outcome can be optimized. However, in practical scenarios, DEMs frequently lack prior information on weights, particularly precise weight data. To address this issue, this study adopts a fully connected artificial neural network for elevation fusion prediction. This approach represents an advancement over existing neural network models by integrating local elevation and terrain as input features and incorporating curvature as an additional terrain characteristic to enhance the representation of terrain features. We also investigate the impact of terrain factors and local terrain feature as training features on the fused elevation outputs. Finally, three representative study areas located in Oregon, USA, and Macao, China, were selected for empirical validation. The terrain data comprise InSAR DEM, AW3D30 DEM, and Lidar DEM. The results indicate that compared to traditional neural network methods, the proposed approach improves the Root-Mean-Squared Error (RMSE) ranges, from 5.0% to 12.3%, and the Normalized Median Absolute Deviation (NMAD) ranges, from 10.3% to 26.6%, in the test areas, thereby validating the effectiveness of the proposed method.

A flow-field integrated flight control: dynamic wind tunnel testing and simulation

Abstract Abrupt changes in aircraft attitude due to encountering terrain turbulence or wind shear at low altitudes can directly lead to serious accidents. Therefore, a highly responsive and reliable active attitude stabiliser on board is necessary to counteract low-level severe atmospheric disturbances. However, gust environments caused by local terrain and structures are difficult to represent with typical models, such as the Dryden continuous gust model in free space. As a result, an optimal model-based control design cannot be applied. To address this problem, this paper introduces an adaptive mechanism for updating motion equations based on atmospheric conditions using in-flight surface pressure-field sensing. Additionally, a dynamic wind tunnel experiment system, which can be constructed at universities at a low cost, is developed and described in detail. The effectiveness of the proposed scheme is evaluated through wind tunnel experiments and numerical simulations using a large number of gust samples.

Dendrochemical Analysis as a Method to Identify Mineralized Zones: Insights from the Marathon Cu-PGE Deposit

The Marathon Cu-platinum-group element (PGE) deposit is located within the Eastern Gabbro Series of the Proterozoic Coldwell Alkalic Complex, 10 km north of Marathon, Ontario. The Cu-PGE mineralization occurs within the Two Duck Lake Gabbro, an olivine gabbro intrusion. This geochemical orientation study tested the trace element analysis of tree cores as a method of identifying mineralized versus background areas in the Marathon deposit, focusing on copper (Cu), sulfur (S), palladium (Pd), and silver (Ag). Tree core samples (n=25) were collected with an increment borer from black spruce (Picea mariana) trees and dated by counting growth rings. Fifteen cores, 12 near/overtop the deposit and three distal control samples, were analysed with Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICP-MS) over a 10-year interval (1992-2002). Data normalization to 88Sr like in other dendrochemical studies corrected for tree health and growth effects. 32S, 63Cu, and 105Pd were found to have the highest concentrations at 94.198, 2.224, and 0.055 ppm, respectively, in control sample 23KPA2-3004. This tree is located beside the Pic River and receives large amounts of run-off from the entire Eastern Gabbro Series, possibly enhancing the chemical composition of the tree core. Excluding this anomalous sample, significant concentrations of Cu and Ag were found moving SSW along the deposit, aligning with the direction of movement of the Pleistocene Laurentide Ice Sheet. While this study did not clearly identify mineralized zones in the Marathon deposit, it has identified the possibility of mineralized run-off in the Pic River affecting nearshore tree chemical composition. The influence of local terrain and glacial till must be also considered when using this method. Future research will continue to study the factors influencing the uptake of elements in trees and refine methods for detecting mineralization through tree core analysis.

Tunneling away the relic neutrino asymmetry

The Earth acts as a matter potential for relic neutrinos which modifies their index of refraction from vacuum by δ∼10−8. It has been argued that the refractive effects from this potential should lead to a large O(δ) neutrino-antineutrino asymmetry at the surface of the Earth. This result was computed by treating the Earth as flat. In this work, we revisit this calculation in the context of a perfectly spherical Earth. We demonstrate, both numerically and through analytic arguments, that the flat-Earth result is only recovered under the condition δ3/2kR≫1, where k is the typical momentum of the relic neutrinos and R is the radius of the Earth. This condition is required to prevent antineutrinos from tunneling into classically inaccessible trajectories below the Earth’s surface and washing away the large asymmetry. As the physical parameters of the Earth do not satisfy this condition, we find that the asymmetry at the surface should only be O(δ). While the asphericity of the Earth may serve as a loophole to our conclusions, we argue that it is still difficult to generate a large asymmetry even in the presence of local terrain. Published by the American Physical Society 2024

A comparative study on aquatic environment of two mega reservoirs in the mainstream of Yangtze River

With the mega dams being built in large rivers around the world for water resources utilization and flood prevention, their environmental impacts such as algae bloom are increasingly concerned by the professionals and the publics, but the mechanism controlling the occurrence of algae are still unclear. Along the mainstream of the Yangtze River, world-class reservoirs have been put into operation successively. Among these reservoirs, the Three Gorges Reservoir (TGR) and the cascade reservoirs (CRs) are adjacent but the conditions of algae bloom in the tributary bays of them are totally different. To explore the reasons of the distinguished situations and factors relating to the high frequent algae bloom in the tributary bays of the TGR, a comparative study was carried out between the TGR and its neighbored reservoir out of the CRs based on field survey investigation, water quality records and literature review. The result reveals the considerable influences of geological and climatic factors on the hydrodynamic processes and environmental problems happening in the tributary bays of mega reservoirs. The two reservoirs exhibit significant differences in physical properties such as the hydrodynamic condition, terrain characteristics and climate. The field-investigation suggests that the water columns in the tributary bays of the CRs tend to experience weak vertical stratification. The analysis shows that this is the result of the combining result of the local terrain characteristics and the cooler, rainier and cloudier climate in the CRs region which significantly affect the hydro-thermal dynamic processes in the tributary bays. With the low level of nutrient and strong vertical mixing, the growth of the algae is constrained and these should be the significant reasons of the low frequency of algae bloom in the tributary bays of the CRs.

Chandrayaan-3 APXS elemental abundance measurements at lunar high latitude.

The elemental composition of the lunar surface provides insights into mechanisms of the formation and evolution of the Moon1,2. The chemical composition of lunar regolith have so far been precisely measured using the samples collected by the Apollo, Luna and Chang'e 5 missions, which are from equatorial to mid-latitude regions3,4; lunar meteorites, whose location of origin on the Moon is unknown5,6; and the in situ measurement from the Chang'e 3 and Chang'e 4 missions7-9, which are from the mid-latitude regions of the Moon. Here we report the first in situ measurements of the elemental abundances in the lunar southern high-latitude regions by the Alpha Particle X-ray Spectrometer (APXS) experiment10 aboard the Pragyan rover of India's Chandrayaan-3 mission. The 23 measurements in the vicinity of the Chandrayaan-3 landing site show that the local lunar terrain in this region is fairly uniform and primarily composed of ferroan anorthosite (FAN), a product of the lunar magma ocean (LMO) crystallization. However, observation of relatively higher magnesium abundance with respect to calcium in APXS measurements suggests the mixing of further mafic material. The compositional uniformity over a few tens of metres around the Chandrayaan-3 landing site provides an excellent ground truth for remote-sensing observations.

Optimal Mapping of Soil Erodibility in a Plateau Lake Watershed: Empirical Models Empowered by Machine Learning

Soil erodibility (K) refers to the inherent ability of soil to withstand erosion. Accurate estimation and spatial prediction of K values are vital for assessing soil erosion and managing land resources. However, as most K-value estimation models are empirical, they suffer from significant extrapolation uncertainty, and traditional studies on spatial prediction focusing on individual empirical K values have neglected to explore the spatial pattern differences between various empirical models. This work proposed a universal framework for selecting an optimal soil-erodibility map using empirical models enhanced by machine learning. Specifically, three empirical models, namely, the erosion-productivity impact calculator model (K_EPIC), the Shirazi model (K_Shirazi), and the Torri model (K_Torri) were used to estimate K values. Random Forest (RF) and Gradient-Boosting Decision Tree (GBDT) algorithms were employed to develop prediction models, which led to the creation of three K-value maps. The spatial distribution of K values and associated environmental covariates were also investigated across varying empirical models. Results showed that RF achieved the highest accuracy, with R2 of K_EPIC, K_Shirazi, and K_Torri increasing by 46%, 34%, and 22%, respectively, compared to GBDT. And distinctions among environmental variables that shape the spatial patterns of empirical models have been identified. The K_EPIC and K_Shirazi are influenced by soil porosity and soil moisture. The K_Torri is more sensitive to soil moisture conditions and terrain location. More importantly, our study has highlighted disparities in the spatial patterns across the three K-value maps. Considering the data distribution, spatial distribution, and measured K values, the K_Torri model outperformed others in estimating soil erodibility in the plateau lake watershed. This study proposed a framework that aimed to create optimal soil-erodibility maps and offered a scientific and accurate K-value estimation method for the assessment of soil erosion.

Rail noise comparison of FTA methodology with CadnaA modeling at Bronx development site

For environmental studies, identifying road and rail noise is important in projecting potential impacts from increased operations and also in identifying the window/wall attenuation needed to reduce outdoor to indoor noise levels. The Federal Transit Administration's (FTA) transit noise and vibration impact assessment procedures provide spreadsheets for estimating noise levels at specific receptor points. However, computer models such as SoundPLAN and CadnaA are also available. Because these computer models have complex input requirements and account for local terrain, they are rarely used for large corridor studies, but they are well suited to smaller sites. This study compares the FTA procedures with CadnaA modeling at a development site in the Bronx, NY, that is bounded by multiple rail lines and is proximate to a major highway. Discussion of results presents the advantages and disadvantages of each approach and identifies the conditions under which each approach is most appropriate.

Transport Characteristics and Potential Source Areas of PM2.5 in Yangquan City Under Different Pollution Levels

Based on the PM2.5 monitoring data, NCEP data, and the meteorological data of the weather situation analysis at the corresponding time in Yangquan City from 2020 to 2022, using the HYSPLIT4 backward trajectory model, multi-station potential source contribution factor analysis （MS-PSCF） and trajectory density analysis （TDA） were introduced to study the differentiation and classification of PM2.5 transport channels and potential sources in Yangquan City. The results showed that： ① The PM2.5 pollution in Yangquan was mainly concentrated in Yangquan and Pingding, whereas the pollution in Yuxian was relatively light. The proportion of days with different pollution levels and the average and maximum values of PM2.5 concentration in Yangquan and Pingding were significantly higher than those in Yuxian, and the distribution characteristics of PM2.5 were closely related to the local special terrain. ② The amount of PM2.5 pollution and the concentration of PM2.5 in different pollution levels were the highest in light wind weather. The influence of east-west regional transport on PM2.5 pollution times and PM2.5 concentration of Yangquan and Pingding was obvious, and the contribution of east wind was significant. The influence of local pollution sources was the main factor in the moderate pollution weather in Yuxian County. ③ There were four main ground conditions for the generation and maintenance of moderate or above pollution weather： warm low pressure type （22%）, high pressure front （bottom） type （54%）, high pressure back type （14%）, and pressure equalization field （10%）. High pressure front （bottom） type was the main ground situation causing the increase in PM2.5 concentration. There were two types of upper air conditions, namely, flat westerly flow type （78%） and northwest flow type （22%）. The upper westerly flow type was the main upper air condition that caused the increase in PM2.5 concentration. ④ The results of transport channels and potential source areas of PM2.5 with different pollution levels obtained by MS-PSCF and TDA were consistent. The main transport channels of PM2.5 were the northeast, southeast, and northwest channels, whereas the northeast and southeast channels were short-distance transport routes, which were the main routes causing the increase in PM2.5 concentration. The northwest channel was consistent with the northwest dust transport channel, belonging to long-distance transmission. The main potential source areas of PM2.5 pollution were located in the central and western parts of Hebei and the southeast part of Hebei, the northeast part of Henan and its junction with the southwest part of Shandong, and the southeast part of Shanxi.

Unravelling controls on multi‐source‐to‐sink systems: A stratigraphic forward model of the early–middle Cenozoic of the SW Barents Sea

AbstractSource‐to‐sink dynamics are subjected to complex interactions between erosion, sediment transfer and deposition, particularly in an evolving tectonic and climatic setting. Here we use stratigraphic forward modelling (SFM) to predict the basin‐fill architecture of a multi‐source‐to‐sink system based on a state‐of‐the‐art numerical approach. The modelling processes consider key source‐to‐sink parameters such as water discharge, sediment load and grain size to simulate various sedimentary processes and transport mechanisms reflecting the dynamic interplay between erosion in the catchment area, subsidence, deposition and filling of the basin. The Cenozoic succession along the SW Barents Shelf margin provides a key area to examine controls on source‐to‐sink systems along a transform margin that developed during the opening of the North Atlantic when Greenland and Eurasian plates were separated (ca. 55 Ma onwards). Moreover, the gradual cooling which culminated in major glaciations in the northern hemisphere during the Quaternary (ca. 2.7 Ma), has affected the spatio‐temporal evolution of the sediment routing along the western Barents Shelf margin. This study aims to characterize the relative importance of different source areas within the source‐to‐sink framework through SFM. In the early Eocene, the SW Barents Shelf experienced a relatively equal sediment delivery from three principal source areas: (i) Greenland to the north, (ii) the Stappen High to the east, representing a local source terrain, and (iii) a major southern source (Fennoscandia). In the middle Eocene, our best‐fit modelling scenario suggests that the northern and the local eastern sources dominated over the southern source, collectively supplying large amounts of sand into the basin as evidenced by the submarine fans in Sørvestsnaget Basin. In the Oligocene (ca. 33 Ma) and Miocene (ca. 23 Ma), significant amounts of sediments were sourced from the east due to shelf‐wide uplift. Finally, this study highlights the dynamic nature and controls of sediment transfer in multi‐source‐to‐sink systems and demonstrates the potential of SFM to unravel tectonic and climatic signals in the stratigraphic record.

Challenges of Using Publicly-Available Hospital Data to Quantify Health Effects from Wildfire Plumes in the East San Francisco Bay Area Communities of California, USA

In the summer and fall of 2018 and 2020, major wildfires in Northern California (USA) impacted the San Francisco Bay Area. The remote 2018 and 2020 wildfires produced the highest PM2.5 concentrations experienced in the Tri-Valley of the East Bay Area during those two years. The Tri-Valley is composed of the San Ramon, Amador, and Livermore Valleys, surrounded by local terrain that creates a small airshed. In 1967, the California Air Resources Board created 15 Air Basins defined by regional geography, topographic and meteorological conditions. Airshed is sometimes synonymous with an urban-scale component of an Air Basin. We use airshed as a Tri-Valley component of the Bay Area Air Basin. This airshed spans across two counties (Contra Costa and Alameda) and encompasses four cities: San Ramon, Dublin, Pleasanton, and Livermore. PurpleAir (PA) sensors provided good geographic coverage of variation in PM2.5 in the Tri-Valley airshed. Several studies have established significant health effects from wildfire plumes by associating daily hospital visits with PM2.5 air quality data at local and regional scales. We hypothesize that during the wildfire smoke periods of 2018 and 2020 in the Tri-Valley area, there was an increase in hospitalizations and ED visits for respiratory (asthma and COPD-related) health effects, as compared to the same time periods during years with less fire activity. The primary goal of this study was to confirm health effects from wildfire plumes on a community scale using 5 years of publicly-available health data. However, with only monthly hospitalization data available, directly linking respiratory hospital and emergency department (ED) visits with PM2.5 concentrations was unsuccessful. Also, because COVID-19 masked all other causes of hospital visits in 2020, that year was ultimately eliminated from this study. However, visits during November 2018 being much higher than any other November in 2016, 2017, and 2019 implied a potential cause and effect. Daily hospitalization and air quality data are required to quantify any relationship by regression analysis. These findings help inform future studies on the health effects of air quality at community scales. KEYWORDS: PM2.5 Air Quality; Air Pollutant Exposure; Air Quality Monitoring; Wildfire Smoke; Respiratory Health; PurpleAir Sensors

Outdoor Radon Dose Rate in Canada's Arctic amid Climate Change.

Decades of radiation monitoring data were analyzed to estimate outdoor Radon Dose Rates (RnDRs) and evaluate climate change impacts in Canada's Arctic Regions (Resolute and Yellowknife). This study shows that the RnDR involves dynamic sources and complex environmental factors and processes. Its seasonality and long-term trends are significantly impacted by temperatures and soil-and-above water contents. From 2005 to 2022, Yellowknife's RnDR increased by +0.35 ± 0.06 nGy/h per decade, with the fastest increases occurring in cold months (October to March). The rise is largely attributable to water condition changes over time in these months, which also caused enhanced soil gas emissions and likely higher indoor radon concentrations. In Resolute, the RnDR increased between 2013 and 2022 at +0.62 ± 0.19 nGy/h (or 16% relatively) per decade in summer months, with a positive temperature relationship of +0.12 nGy/h per °C. This work also demonstrates the relevance of local climate and terrain features (e.g., typical active layer depth, precipitation amount/pattern, and ground vegetation cover) in researching climate change implications. Such research can also benefit from using supporting monitoring data, which prove effective and scientifically significant. From the perspective of external exposure to outdoor radon, the observed climate change effects pose a low health risk.

A Context-Aware Navigation Framework for Ground Robots in Horticultural Environments.

Environmental mapping and robot navigation are the basis for realizing robot automation in modern agricultural production. This study proposes a new autonomous mapping and navigation method for gardening scene robots. First, a new LiDAR slam-based semantic mapping algorithm is proposed to enable the robots to analyze structural information from point cloud images and generate roadmaps from them. Secondly, a general robot navigation framework is proposed to enable the robot to generate the shortest global path according to the road map, and consider the local terrain information to find the optimal local path to achieve safe and efficient trajectory tracking; this method is equipped in apple orchards. The LiDAR was evaluated on a differential drive robotic platform. Experimental results show that this method can effectively process orchard environmental information. Compared with vnf and pointnet++, the semantic information extraction efficiency and time are greatly improved. The map feature extraction time can be reduced to 0.1681 s, and its MIoU is 0.812. The resulting global path planning achieved a 100% success rate, with an average run time of 4ms. At the same time, the local path planning algorithm can effectively generate safe and smooth trajectories to execute the global path, with an average running time of 36 ms.

Landslide Susceptibility Analysis and Hazard Zonation in Nuwakot District, Nepal

Landslide mostly occurred in highly rugged topography, erosional steeply sloping land form and fragile geological structural in mountainous region. Landslide susceptibility describe the likelihood relation of occurring landslide in an area controlled with their local terrain condition. Landslide susceptibility analysis (LSA) work is used to determine the spatial distribution of landslides prone area and applicable to predict the future landslides occurrences, which is vital for averting and mitigating regional landslide disasters. LSA. In this study, LSA is carried with the bivariate analysis incorporating twelve causative factors of landslide. The frequency ratio (FR) technique were used for computing the relative frequency (RF) as priority rank and entropy index (E) as weight of causative factor. Result of LSA shows that the probability of occurring landslides is 80 percent, with 27 percent of that risk being high risk, 38 percent being medium risk, and 15 percent being low risk. The prediction accuracy of the model is 87.89 percent, showing reliable and satisfactorily validation rates with good accuracy. So, the present study demonstrates that the quantitative assessment methods explored may have a promising potential for landslide assessment and prediction in the high hill and Himalaya regions.

Development and Application of a New Open-Source Integrated Surface–Subsurface Flow Model in Plain Farmland

Accurately characterizing rainfall runoff processes in plain farmland, especially at the plot scale with significant micro-topographic features, has presented challenges. Integrated surface–subsurface flow models with high-precision surface flow modules are appropriate tools, yet open-source versions are rare. To address this gap, we proposed an open-source integrated surface–subsurface flow model called the FullSWOF-Plain model, in which the one-dimensional subsurface module Hydrus-1D was integrated with a modified two-dimensional surface water flow module (FullSWOF-2D) using the sequential head method. Various experimental scenarios were simulated to validate the model’s performance, including two outflow cases (i.e., 1D and 2D) without infiltration, a classical one-dimensional infiltration case, and two typical rainfall events at the experimental field. The results demonstrate the accuracy of this proposed model, with the Nash–Sutcliffe efficiency (NSE) of the simulated discharge exceeding 0.90 in the experimental field case and the root mean squared error (RMSE) values for soil moisture at five depths consistently below 0.03 cm3/cm3. However, we observed a lag in the simulated response time of soil moisture due to the neglect of preferential flow. The micro-topography significantly influenced ponding time and ponding areas. Lower local terrain normally experienced earlier surface ponding. Scattered surface ponding water first occurred in the ditch and followed in the relatively low areas in the main field. The concentration process of surface runoff exhibited hierarchical characteristics, with the drainage ditch contributing the most discharge initially, followed by the connection of scattered puddles in the main field, draining excess surface water to the ditch through rills. This quantitative study sheds light on the impact of micro-topography on surface runoff in plain farmland areas.