 Subjects -> AERONAUTICS AND SPACE FLIGHT (Total: 124 journals)
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- A modified temperature based model for estimation of potential
evapotranspiration over Ghataprabha river basin, south India-
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Abstract: Abstract Potential Evapotranspiration (PET) is one of the significant hydrological parameters, but complicated to estimate due to various meteorological parameters involved. Accurate estimation of PET at spatial scale is a major requirement for arid and semiarid agriculture regions such as Ghatapraha river basin for study of water balance. Temperature based models have emerged as promising tools in extracting the spatial distribution of the PET, using remote sensed images. The main objective of the study is, to develop a site-specific temperature-based model for estimation of PET in semiarid regions, applicable to Ghataprabha basin. Performance of five different temperature-based PET models were evaluated by comparing with the standard Penman-Monteith equation for the year 2013 and 2014. The Hargreaves-Samani equation performed better with statistical parameters RMSE, MBE, MPE and R2 of 0.65, 0.43, 10.64 and 0.81 respectively. The model was further calibrated and site-specific modified Hargreaves-Samani equation was developed. The results revealed that spatial PET estimated by modified method based on satellite-derived spatial air temperature data performed better in spatial scale. The proposed approach is new and is advantageous as the spatial distribution of PET can be obtained, with limited data of temperature, where unavailability of the spatial climate data is a major concern.
PubDate: 2023-10-01
- Spatiotemporal associations of mental distress with socioeconomic and
environmental factors in Chicago, IL, 2015–2019-
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Abstract: Abstract Mental distress is an epidemic that endangers global well-being and contributes to various illnesses. In the United States, the prevalence of mental distress has risen rapidly in recent years. However, this topic is understudied in spatial information research, as current literature lacks focus on spatially varying relationships between mental distress and relevant factors, which leads to impediment of prevention and mitigation efforts. Therefore, this study aims for investigating the spatiotemporal relationships of mental distress with crime, housing cost, poverty, air quality. Using the space–time scan statistic, we illustrate the spatiotemporal distribution of mental distress in Chicago, IL. In addition, we employ geographically and temporally weighted regression (GTWR) to find the varying relationships between aforementioned factors and mental distress. Lastly, we compare GTWR to a linear ordinary least squares model to assess the effect of spatial and temporal dependence in found relationships. Our findings indicate that, while the crime rate, housing costs, and poverty explain the prevalence of mental distress over time and space, the space–time variation of PM2.5 is not a predominant determinant of mental distress in Chicago. The practical implications of our work are that planners and policymakers are encouraged to identify spatiotemporal patterns of mental distress so that resources can be directed to the most vulnerable communities. Spatiotemporal modelling, the identification of geographic patterns and relationships, enables novel understanding of societal issues, and is an integral part of spatial information science.
PubDate: 2023-10-01
- Estimating thickness of Zemu glacier, Sikkim (India) using ice-flow
velocity approach: a geoinformatics based perspective-
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Abstract: Abstract In the present scenario of warming climate, overall health of the glaciers along with sea level rise/fall are directly impacted by glacial dynamics. However due to inaccessible high altitude regions and devastating climate, the in-situ observations are hindered via field excursions. The present study incorporated usability of geographical information system based ice-flow velocity approach using glacier surface velocity and slope for estimating thickness of Zemu glacier in Sikkim. The study revealed thickness of 80 ± 9.6 m to 160 ± 19.2 m near snout followed by 240 ± 28.8 m to 320 ± 38.4 m in upper reaches of accumulation zone of Zemu glacier. However due to gentle slope, thickness ranged between 320 ± 38.4 m and > 400 ± 49.2 m (~ 418 ± 50.16 m) was observed in the central trunk or middle reaches of the glacier. An uncertainty of 12% was observed while calculation the glacier thickness. Relationship between glacier velocity and depth has also been established which has shown inverse characteristics due to variability of bed topography and drag effects. Proper validation of results for each study with existing field observations and literatures depicted the utility and correctness of the present study via satellite based observations.
PubDate: 2023-10-01
- Advancement in navigation technologies and their potential for the
visually impaired: a comprehensive review-
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Abstract: Improvements in technology and navigation tools are leading to more affordable and effective solutions to assist individuals with visual impairments. The progress made in navigation technology has the potential to increase inclusivity for the visually impaired in education, social, and workforce settings. After conducting a thorough review of the literature, we have identified key issues and concluded that collaboration among healthcare professionals, caregivers, programmers, engineers, and policymakers is essential for successfully developing navigation projects for the visually impaired. This study highlights different advances and relevant topics in the development of location-based applications for individuals with visual impairments. Our paper involved an extensive search of eight journal databases spanning from 1993 to 2021. We screened 4550 titles, analyzed 560 abstracts, and ultimately reviewed 35 full-text papers, resulting in the examination of 20 papers. Our findings indicate that the advancement of navigation technology can positively affect the quality of life of visually impaired individuals, particularly through assistive technology, mobile applications, and web services. Dot Waker, Nearby Explorer, Get There, and Google Maps are the most commonly used navigation systems by visually impaired individuals. Overall, our research suggests that continued development in navigation-assisted applications can significantly benefit the visually impaired community.
PubDate: 2023-10-01
- Deep learning-based framework for vegetation hazard monitoring near
powerlines-
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Abstract: Abstract The increasing popularity of drones has led to their adoption by electric utility companies to monitor intrusive vegetation near powerlines. The study proposes a deep learning-based detection framework compatible with drones for monitoring vegetation encroachment near powerlines which estimates vegetation health and detects powerlines. Aerial image pairs from a drone camera and a commercial-grade multispectral sensor were captured and processed into training and validation datasets which were used to train a Generative Adversarial Network (Pix2Pix model) and a Convolutional Neural Network (YoLov5 model). The Pix2Pix model generated satisfactory synthetic image translations from coloured images to Look-Up Table (LUT) maps whiles the YoLov5 obtained good performance for detecting powerlines in aerial images with precision, recall, mean Average Precision (mAP) @0.5, and mAP0.5:0.95 values are 0.82, 0.76, 0.79 and 0.56 respectively. The proposed vegetation detection framework was able to detect locations of powerlines and generate NDVI estimates represented as LUT maps directly from RGB images captured from aerial images which could serve as a preliminary and affordable alternative to relatively expensive multispectral sensors which are not readily available in developing countries for monitoring and managing the presence and health of trees and dense vegetation within powerline corridors.
PubDate: 2023-10-01
- GIS-based sinkhole susceptibility mapping using the best worst method
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Abstract: Abstract Sinkholes are among karst forms and their formation is continuous and their identification is essential in several fields of life, such as water resources management, environmental hazards management, and tourism. This study aimed to identify the sinkholes and the sinkhole susceptibility in the Bistoon-Parav karst region, Iran. Ten sinkhole causative factors, precipitation, temperature, evaporation, lithology, soil type, slope, latitude, fault, stream and vegetation were involved in the sinkhole susceptibility model applying the best worst method, and we also determined the importance of the factors. The final sinkhole susceptibility map was produced by the weighted summing up the factors based on the variable importance. Lithology was the most important factor with 31.52% in the formation of sinkholes. The validation step was executed with a sinkhole database based on visual interpretation of high-resolution imagery. Finally, the receiver operating characteristic (ROC), completeness, correctness and quality index were applied to validate the performance of the sinkhole susceptibility map model. According to the validation parameters, the value of the ROC, completeness, correctness and quality was 81.90%, 100%, 59.41% and 59.41%, respectively. Thus, it can be said that the produced model shows acceptable performances for sinkhole susceptibility mapping. Also, this model showed that almost 7.4% of the region has the potential to become a sinkhole in the future.
PubDate: 2023-10-01
- Examining the relationship between socioeconomic structure and urban
transport network efficiency: a circuity and spatial statistics based
approach-
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Abstract: Abstract Access to urban public transportation services is crucial for all city residents. Undoubtedly, more efficient public transportation services should be provided for the needy ones. The study aims to develop a simple yet efficient analytical approach to spatially determine the urban areas that receive inefficient public transportation services. In this study, the spatial distribution of the efficiency of public transportation and its relation to socioeconomic variables (per capita income level and population density) are examined at the neighborhood in Izmir, Turkey, level using circuity. The results from univariate and bivariate Global and Local Moran’s I analyses reveal that the high-efficiency levels are spatially clustered, Higher-income neighborhoods have better public transportation systems compared to lower-income ones. According to Bivariate Local Moran’s I analyses, among the 348 neighborhoods at least 31 and at most 81 neighborhoods are either in a High-High or Low-Low cluster for the four time periods considered. As for the relationship between circuity and density, at least 21 and at most 75 neighborhoods are a part of a cluster. The fact that there is a significant relationship between the efficiency of public transportation and socioeconomic variables calls for alteration in the planning policies regarding urban public transportation supply. Although the variation in public transport efficiency levels across the neighborhoods can partly be attributed to physical conditions, the city should provide equal accessibility and efficiency regardless of the socio-economic status of the neighborhoods. The findings can well be generalized for cities of similar sizes in developing countries.
PubDate: 2023-10-01
- Smart greenhouse management system with cloud-based platform and IoT
sensors-
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Abstract: Abstract This paper presents an IoT-based intelligent greenhouse management system that utilizes various sensors and a fuzzy adaptive PID controller to efficiently manage greenhouse temperature and humidity. The system also includes a cloud-based platform for real-time data visualization and a mobile app for remote control. A clustering algorithm pre-processes the data and eliminates duplicates and inconsistencies. The novelty of this paper lies in the use of a fuzzy adaptive PID controller and a clustering algorithm in an IoT-based intelligent greenhouse management system to efficiently manage greenhouse temperature and humidity. These technologies improve the accuracy of the system and enable increased crop yield and reduced energy usage. Additionally, the use of a cloud-based platform and mobile app for real-time data visualization, analysis, and device control represents an innovative approach to greenhouse management. Overall, this system has the potential to revolutionize greenhouse management and enable sustainable agriculture.
PubDate: 2023-10-01
- Spatiotemporal association between weather and Covid-19 explored by
machine learning-
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Abstract: Abstract The Covid-19 epidemic led to loss of the lives of many people in the world and had a very negative impact on the mental and physical health of humans. One of the effective ways to preventive strategies regarding is to study the impact of climatic parameters. This research introduces a new spatiotemporal methodology to explore the association between Covid-19 and hourly data of weather. This methodology developed based on machine learning using unsupervised clustering method. Six counties considered for finding association and the cities that have similar climatic temporal changes clustered and compared with cities that have similar number of Covid-19 cases. For this goal, a new model is developed for finding similarities between clusters, which indicates the association between weather and Covid-19. The result shows similarities are about 57% for wind speed, 63% for temperature, 63% for surface pressure, and 42% for elevation. Then result evaluated sing Kendall’s tau_b and Spearman’s rho which shows the proposed methodology has an acceptable result.
PubDate: 2023-10-01
- Aggravation of CoVID-19 infections due to air pollutant concentrations in
Indian cities-
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Abstract: Abstract The CoVID-19 infections began rising worldwide during the initial weeks of March 2020, reacting to which the Government of India called for nationwide lockdown for ~ 3 weeks. The concentration of pollutants during the lockdown were compared with pollution levels recorded during the preceding year for the same time frame. A direct relationship was established between the high level of air pollutants (PM2.5, PM10, NO2 and SO2) and CoVID-19 infections being reported in the Indian cities. The correlation indicates that the air pollutants like PM2.5, PM10, NO2 and SO2 are aggravating the number of casualties due to the CoVID-19 infections. The transmission of the virus in the air is in the form of aerosols; and hence places which are highly polluted may see a proportionate rise in CoVID-19 cases The high-level exposure of PM2.5 over a long period is found to be significantly correlated with the mortality per unit confirmed CoVID-19 cases as compared to other air pollutant parameters like PM10, NO2 and SO2.
PubDate: 2023-10-01
- A systematic review for assessing the impact of climate change on
landslides: research gaps and directions for future research-
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Abstract: Abstract The magnitude and intensity of landslides due to changing climate have created environmental and socio-economic implications for society. Through an in-depth analysis of the existing research on landslides in a changing climate from 1996 to 2021, this paper aims to carry out bibliometric and thematic analyses, identify the research gaps in the existing literature, and suggest a future framework for climate change-induced landslide risk assessment and mitigation. The data for review was collected from the Web of Science and Scopus platforms using a set of relevant keywords. After meeting the exclusion and inclusion criteria, 200 studies were finally selected to analyze the current state of research. The findings revealed that most of the reviewed studies focused on economic vulnerability to landslides, while social and ecological aspects of vulnerability at the micro-scale were scant in the past literature. Uncertainty in landslide-climate modeling, lack of advanced models for predicting landslide risk, and lack of early warning systems were identified as the major research gaps. A holistic methodological approach is proposed for assessing landslide risk and devising landslide mitigation strategies. The identified research gaps and the proposed framework may help in the future progression of climate change-induced landslide research in spatial information science.
PubDate: 2023-09-29
- Spatial analysis of the factors impacting on the spread of Covid-19 in the
neighborhoods of Zanjan, Iran-
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Abstract: Abstract This research aims to analyze the factors and distribution pattern of the covid-19 virus in the localities of Zanjan city, because the outbreak of this pandemic has put the lives of many citizens at risk. Spatial statistics determine the temporal-spatial pattern of 21,638 people infected with the virus between Feb. 22 and Mar. 22, 2020. The geographic weighted regression and spatial autocorrelation have been used to find the relationship between the factors of the spread of the virus in the localities of Zanjan and its distribution. The results of spatial autocorrelation show that the spread of the Covid-19 pandemic in the localities of Zanjan is clustered and the concentration is more in vulnerable neighborhoods and informal settlements. Therefore, based on the results of the weighted geographic regression, the class nature of the effects and consequences of this pandemic is undeniable; a matter that is linked with independent and labor jobs, population density, unemployment, underlying diseases, the number of people in a room, household density in residential units, illiteracy, sex ratio; and appropriate policies free from discrimination in order to increase the economic equality of these neighborhoods will be effective in reducing the mortality of this disease.
PubDate: 2023-09-21
- AI-driven drowned-detection system for rapid coastal rescue operations
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Abstract: Abstract Recent observations indicate that nearly 50% of the public frequently visit coastal areas during weekends, seeking the health benefits of natural sunlight and fostering familial bonds. Notably, a significant portion of these visitors are unaware of swimming techniques or face other physical challenges, rendering them vulnerable to drowning, especially in areas lacking adequate lifeguard support or immediate medical emergency services. This study introduces an advanced drowned-detection device that employs a deep learning algorithm, grounded in artificial intelligence architecture, to swiftly detect and address potential drowning incidents. The system is particularly vigilant towards high-risk groups, such as children and the elderly. Upon detecting a threat, it autonomously deploys drones equipped with inflatable rescue tubes and notifies local authorities. Preliminary results suggest that our proposed model can effectively rescue a drowning individual in under 7 min, highlighting its prospective utility in curtailing swimming-related fatalities worldwide. This research underscores the need for technological intervention to enhance safety measures at coastal destinations and seeks to raise awareness about the importance of well-established lifeguard support.
PubDate: 2023-09-15
- From pixels to patterns: review of remote sensing techniques for mapping
shifting cultivation systems-
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Abstract: Abstract Shifting agriculture is a complex system requiring organized categorization and measurement due to its widespread practice in tropics and impact on greenhouse gases. Remote sensing technologies and participatory mapping drive efforts to map these areas. This study provides a methodological overview of mapping techniques used for shifting cultivation from 2000 to present, covering local to global studies, identifying trends in remote sensing-based estimation of spatio-temporal patterns in these landscapes. Spatial resolution, classification method, ground truthing and data collection frequency were found to be important factors in mapping these mosaics. While most studies originate from Asia, there are also significant contributions from the Americas and Africa. Highlighting the intricacy of these systems, techniques such as spectral indices, cultivation stage differentiation, time-series data, and agent-based modeling have been used. Ground truthing from various sources validates and ensures accuracy. Other methods like object-based analysis, participatory approaches, image differencing, and neural networks also enhance comprehension of these areas. These insights will benefit related studies for assessing these landscapes at varying scales to enhance their management. Further collaborative research is essential to create automated methods and consistent protocols for monitoring these systems.
PubDate: 2023-09-15
- Mobile wireless ad-hoc network routing protocols comparison for real-time
military application-
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Abstract: Abstract Mobile wireless Ad-hoc has become more popular because it forms quickly, has an easy setup, and has easy extensibility. The mobile ad-hoc wireless networks can be further classified according to their applications as follows: Regular user ad-hoc networks are commercial communication that applies to vehicles to help avoid collisions and accidents and live connections to transfer data from car to car. Another application is disaster rescue ad-hoc networking, usually used when a normal infrastructure network is destroyed by storms, earthquakes, tsunamis, etc. Nowadays, a lot of applications, particularly those related to the military and emergency situations, rely on mobile ad hoc wireless networks, where security needs are more challenging to provide than in regular networks. We present the tactical network needs for the military. This platform attempts to assess the possible advantages of mobile ad hoc networks in tactical military applications. This work proposes route discovery using reactive (on-demand) routing protocols where nodes need to just transfer data. This eliminates the requirement for each node to store and maintain any routing tables. This study presents and contrasts the benefits and drawbacks of two fundamental mobile ad hoc routing systems (AODV and DSR). Both protocols are On-Demand routing techniques, and when data needs to be sent, the discovery phase begins. The results of the simulation, the AODV routing approach outperforms the DSV routing method under identical simulated conditions.
PubDate: 2023-09-14
- Deep learning-based heat optimization techniques for forecasting indoor
temperature changes-
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Abstract: Abstract The traditional climate compensator technique requires extensive modifications and significantly relies on manual knowledge when it comes to winter heating in buildings. This paper suggests a deep-learning-based optimization procedure for heating systems to improve the initial control strategy. To begin, we recommend using a deep MTDN (Multiple Time Difference Network) to deduce the thermodynamic laws governing the dynamics of indoor temperature changes and make future predictions about the temperature in a given space. The network is and follows physical rules; next, M is used as a simulator, and the evaluation index representing the human thermal reaction issued as the appropriate reward item. Next, a strategy optimizer based on the SAC (Soft Actor-Critic) learning thought algorithm is used to train the simulator to realize a stable and excellent heating control strategy; finally, experiments are designed using real data from a Tianjin heat exchange station to evaluate the simulator’s ability to predict future outcomes. It is confirmed that the simulator not only has high prediction accuracy but also adheres to the laws of physics and that the strategy optimizer learned approach could guarantee more stable and comfortable indoor temperature in multiple periods of random sampling, as compared to the original strategy.
PubDate: 2023-09-09
- Mapping hotspots of tuberculosis cases with validation on site in Gombak,
Selangor, Malaysia-
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Abstract: Abstract Tuberculosis (TB) is regarded as one of the leading causes of death globally. The control strategies and resource allocation need to be prioritized in high risk areas by implementing accurate mapping of spatial heterogeneity of the disease. This cross-sectional study aimed to identify the hotspots of TB cases with validation on site in Gombak district, Malaysia. The 3325 cases of TB from 1st January 2013 to 31st December 2017 were collected from the MyTB web and Tuberculosis Information System database. The data includes individual’s ID, date of diagnosis, and patient’s address. The coordinate of each patient’s address was geocoded using Google Earth and then they were georeferenced with the base map of Gombak using geographic information system. Getis-Ord Gi* statistics was used to identify the hotspots of TB cases. The hotspots analysis were validated by capturing pictures of the locations during field visit and compared with the hotspot map. The hotspots of TB cases were consistently distributed at the southwestern part of Gombak, with 99% confidence level that includes 136 points across 5-year period, where the prison was located. Other location of hotspots includes apartments, hostels, markets, factories, and schools. The hotspots shifted gradually from the northwestern to the southwestern parts of the district. This study revealed heterogeneity in the spatial distribution of TB across vulnerable and densely populated areas, thus able to conduct early screening, initiate the treatment, and improve the intervention programme. In the future, inclusion of associated risk factors of the disease based on genotyping of the isolates was recommended to track the TB transmission from different sources to the hotspot’s location.
PubDate: 2023-09-07
- Spatiotemporal analysis of air pollutants and river turbidity over
Varanasi region, India during COVID-19 second wave-
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Abstract: Abstract World Health Organization (WHO) on 30th January 2020 declared a Public Health Emergency of International Concern during the spread of novel coronavirus in the early months of 2020 across 188 countries. The Indian government has imposed a 21-day lockdown during the first Coronavirus Virus Disease – 19 (COVID-19) wave starting from 25th March to 14th April initially. The state-wise lockdown was again imposed during the second wave (mid-march) to curb the spread. The present study focused on the effect of the lockdown during the COVID-19 s wave on the spatiotemporal variability of air pollutants in the Varanasi region, and on turbidity levels of the Ganga river using remote sensing. A decreasing trend for the selected air pollutants (NO2, SO2, CO, and HCHO), and turbidity levels were observed during the lockdown period which revealed improved air as well as water quality. The results of the present study provide robust insight into air and water quality measurements with methodological advancement in pollution susceptibility studies and can be used to achieve futuristic observations of patterns of turbidity levels. These results indicate that avoiding poor transportation planning and adopting sustainable plans across the urban agglomerations in India could reduce a significant amount of air pollution levels.
PubDate: 2023-08-25
- Deep convolutional neural network to predict ground water level
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Abstract: Abstract In contrast to the atmosphere and fresh surface water, which can only briefly store water, the natural water cycle may use groundwater as a “reservoir” that stores water for extended periods. Even though there is a considerable degree of variation and complexity in the subsurface environment, there is a minimal availability of data from the field. Both of these challenges were faced by those who used models that were based on actual reality. Statistical modelling gradually improved the accuracy of the model’s calibration. Groundwater has become an increasingly important resource for supplying the water requirements of a rising global population. The fact that there is such a large stockpile allows it to be used once again, even during dry seasons or droughts. This article presents a deep convolutional neural network-based model for predicting groundwater levels. As part of the experimental setup, 174 satellite pictures of groundwater are included in the input data set. Images are preprocessed using the CLAHE method. The CNN, SVM, and AdaBoost methods make up the classification model. The results have shown that CNN can classify things correctly 98.5 per cent of the time. Precision and Recall rate of Deep CNN is also better for ground water image classification.
PubDate: 2023-08-25
- COVID-19: adverse population sentiment and place-based associations with
socioeconomic and demographic factors-
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Abstract: Abstract During the COVID-19 pandemic, increased adverse sentiment such as, fear, panic, anxiety was observed among the public in the United States of America (USA) apart from physical suffering and death. Authorities may find guidance for anticipation and explanation of such secondary threats by analyzing population sentiment on social media. We performed sentiment analysis (SA) using georeferenced tweets in the contiguous USA during the first two waves of COVID-19 (01 November 2019–15 September 2020). We classified the tweets into “adverse” and “non-adverse” sentiment and computed daily counts for both classes at the county-level. Utilizing clustering and Bayesian regression approaches, we analyzed the place-based demographic and socioeconomic covariates of sentiment. We detected 12 clusters that exhibited elevated adverse sentiment and discovered that higher unemployment, male population, and poverty was associated with increased odds of adverse sentiment in Tweets. Conversely, counties with higher COVID-19 case rates, rurality, and elderly population were associated with reduced odds. Pandemic preparedness, response and mitigation measures may benefit from knowledge of the geography of adverse sentiment. Combining spatial clustering and regression benefits the understanding COVID-19, as well as epidemiology in general.
PubDate: 2023-08-23
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