Urbanization is a contributing factor to global warming, as asphalt, concrete, and other light-absorbing materials replace vegetated areas, causing an increase in Land Surface Temperature (LST) and creating Surface Urban Heat Islands (SUHI). Although thermal satellite imagery has been a powerful tool in mapping LST and SUHI spatio-temporal changes, the number of studies in Africa, including Egypt, remains limited. Thus, in this research, an automated model was developed in ArcGIS and used to map LST and SUHI and detect Urban Hot Spots (UHS) in Alexandria city, Egypt, using Landsat 8 time series (2013 to 2021). The results revealed an increase of 41.31% in urban areas and a decrease of 49.51% in agricultural areas, a change that was demonstrated by a decline in the Normalized Difference Vegetation Index (NDVI) from 0.84 in 2013 to 0.53 in 2021. Correspondingly, LST and SUHI displayed an increasing pattern, with the highest recorded values observed in 2021. Thus, this study showed the negative impact of urbanization on Alexandria city’s temperature – a city that is already facing a climate catastrophe because of the sea level rise resulting from climate change. Furthermore, the developed estimation model can be similarly useful for climate change researchers and decision makers.
{"title":"Optimal Route Determination Automation System for Covid-19 Medical Waste Disposal Based on 3D Building Modeling","authors":"","doi":"10.52939/ijg.v19i9.2837","DOIUrl":"https://doi.org/10.52939/ijg.v19i9.2837","url":null,"abstract":"Urbanization is a contributing factor to global warming, as asphalt, concrete, and other light-absorbing materials replace vegetated areas, causing an increase in Land Surface Temperature (LST) and creating Surface Urban Heat Islands (SUHI). Although thermal satellite imagery has been a powerful tool in mapping LST and SUHI spatio-temporal changes, the number of studies in Africa, including Egypt, remains limited. Thus, in this research, an automated model was developed in ArcGIS and used to map LST and SUHI and detect Urban Hot Spots (UHS) in Alexandria city, Egypt, using Landsat 8 time series (2013 to 2021). The results revealed an increase of 41.31% in urban areas and a decrease of 49.51% in agricultural areas, a change that was demonstrated by a decline in the Normalized Difference Vegetation Index (NDVI) from 0.84 in 2013 to 0.53 in 2021. Correspondingly, LST and SUHI displayed an increasing pattern, with the highest recorded values observed in 2021. Thus, this study showed the negative impact of urbanization on Alexandria city’s temperature – a city that is already facing a climate catastrophe because of the sea level rise resulting from climate change. Furthermore, the developed estimation model can be similarly useful for climate change researchers and decision makers.","PeriodicalId":38707,"journal":{"name":"International Journal of Geoinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135705980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Small island countries located in the Indian ocean are mostly vulnerable to tsunamis generated from the Makran and Sumatra earthquake sources. A minor inundation was experienced from the 26th December 2004 tsunami caused by the Sumatra Andaman earthquake while the close island of Rodrigues recorded relatively high surges within its coasts. As a tourist destination for its sandy beaches and blue lagoons, most hotels and foreign invested real estates are located mostly within the coastal region, making the Mauritian economic mainstay vulnerable to the slightest tsunami threat. This research study therefore aims at assessing the vulnerability of the northern region of Mauritius namely Grand Bay, under a possible tsunami threat. Assessment has been categorised in three main vulnerability areas namely the building and infrastructure vulnerability, the human life vulnerability and the environmental vulnerability. The methodology set up includes digitalisation of the Grand Bay region using the QGIS software from satellite raster images, showing the demarked area with geospatial and attributes data. These were analysed using the area intersection in the QGIS Software. Vulnerability indexing was calculated using a risk matrix analysis which was in turn mapped in QGIS, showing highly exposed buildings, an account for human lives under major threat and areas that can suffer saline water infiltration as part of the negative environmental impact.
{"title":"Tsunami Vulnerability Assessment of Grand Bay, Mauritius, Using Remote Sensing and Geographical Information System (GIS)","authors":"","doi":"10.52939/ijg.v19i8.2775","DOIUrl":"https://doi.org/10.52939/ijg.v19i8.2775","url":null,"abstract":"Small island countries located in the Indian ocean are mostly vulnerable to tsunamis generated from the Makran and Sumatra earthquake sources. A minor inundation was experienced from the 26th December 2004 tsunami caused by the Sumatra Andaman earthquake while the close island of Rodrigues recorded relatively high surges within its coasts. As a tourist destination for its sandy beaches and blue lagoons, most hotels and foreign invested real estates are located mostly within the coastal region, making the Mauritian economic mainstay vulnerable to the slightest tsunami threat. This research study therefore aims at assessing the vulnerability of the northern region of Mauritius namely Grand Bay, under a possible tsunami threat. Assessment has been categorised in three main vulnerability areas namely the building and infrastructure vulnerability, the human life vulnerability and the environmental vulnerability. The methodology set up includes digitalisation of the Grand Bay region using the QGIS software from satellite raster images, showing the demarked area with geospatial and attributes data. These were analysed using the area intersection in the QGIS Software. Vulnerability indexing was calculated using a risk matrix analysis which was in turn mapped in QGIS, showing highly exposed buildings, an account for human lives under major threat and areas that can suffer saline water infiltration as part of the negative environmental impact.","PeriodicalId":38707,"journal":{"name":"International Journal of Geoinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41651324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Semeru, the most active volcano in Indonesia, erupted again in December 2021. This study aimed to map the impact of damages due to the eruption and map the incompatibility of land use with the Spatial Planning. The study was carried out through multitemporal spatial analysis to map the impact of the eruption damages, while the analysis of suitability and land use direction was carried out by overlaying land use maps with Spatial Planning maps. Sentinel-1B image data were analyzed using a maximum likelihood approach to obtain land use classification before and after the eruption. The results of the study showed that the eruption had an impact on the destruction of 1001.2 Ha of High-Density Forest, 624.9 Ha of Medium-Density Forest, 450.8 Ha of Rice Fields, 436.7 Ha of Agricultural Fields, 91 Ha of Settlements, and 3.1 Ha of Water bodies in Lumajang Regency. The results of the analysis show that in the affected area, there is a spatial plan direction of a residential area of 109.7 Ha. In addition to that, the high impact of the disaster is also due to the incompatibility of land use in the conservation area as a residential area of 515.4 Ha.
{"title":"Spatial Analysis of the Semeru Eruption Disaster Area","authors":"","doi":"10.52939/ijg.v19i8.2783","DOIUrl":"https://doi.org/10.52939/ijg.v19i8.2783","url":null,"abstract":"Semeru, the most active volcano in Indonesia, erupted again in December 2021. This study aimed to map the impact of damages due to the eruption and map the incompatibility of land use with the Spatial Planning. The study was carried out through multitemporal spatial analysis to map the impact of the eruption damages, while the analysis of suitability and land use direction was carried out by overlaying land use maps with Spatial Planning maps. Sentinel-1B image data were analyzed using a maximum likelihood approach to obtain land use classification before and after the eruption. The results of the study showed that the eruption had an impact on the destruction of 1001.2 Ha of High-Density Forest, 624.9 Ha of Medium-Density Forest, 450.8 Ha of Rice Fields, 436.7 Ha of Agricultural Fields, 91 Ha of Settlements, and 3.1 Ha of Water bodies in Lumajang Regency. The results of the analysis show that in the affected area, there is a spatial plan direction of a residential area of 109.7 Ha. In addition to that, the high impact of the disaster is also due to the incompatibility of land use in the conservation area as a residential area of 515.4 Ha.","PeriodicalId":38707,"journal":{"name":"International Journal of Geoinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49230226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Satellite-derived soil moisture fields received attention due to their large spatial coverage and spatial resolution that suits many applications. The sensors used vary from passive (e.g., LANDSAT-8) to active (e.g., SENTINEL-1) with varying accuracy problems. Passive sensing can only determine relative indices between pixels within a vegetation class and not the real value of moisture. Active sensing suffers from the sensitivity of its detecting behaviour to the level of moisture (anomalous backscatter). The above problems impose limitations on the application without frequent ground-based calibration. The paper investigates possible models to improve the estimation of soil moisture using the powers of the two sensors. In addition, a Hydrologic Surface Moisture indicator (HSM) is included as a third source of information. The paper tests modeling combinations of the three soil moisture predictors (Landsat-8, Sentinel-1, and HSM). The models are validated using in-situ measurements. The results showed that Landsat-8 data can be rescaled using HSM to provide the actual soil moisture in the soil. On the other side, it is possible to remove the anomaly from the Sentinel-1 backscatter using either Landsat-8 or HSM. The elimination of the above problems explained a significant portion of the differences between the two sensors.
{"title":"Improving the Estimation of Soil Moisture in Semi-Arid Regions Using Data from Different Remote Sensing Techniques","authors":"","doi":"10.52939/ijg.v19i8.2781","DOIUrl":"https://doi.org/10.52939/ijg.v19i8.2781","url":null,"abstract":"Satellite-derived soil moisture fields received attention due to their large spatial coverage and spatial resolution that suits many applications. The sensors used vary from passive (e.g., LANDSAT-8) to active (e.g., SENTINEL-1) with varying accuracy problems. Passive sensing can only determine relative indices between pixels within a vegetation class and not the real value of moisture. Active sensing suffers from the sensitivity of its detecting behaviour to the level of moisture (anomalous backscatter). The above problems impose limitations on the application without frequent ground-based calibration. The paper investigates possible models to improve the estimation of soil moisture using the powers of the two sensors. In addition, a Hydrologic Surface Moisture indicator (HSM) is included as a third source of information. The paper tests modeling combinations of the three soil moisture predictors (Landsat-8, Sentinel-1, and HSM). The models are validated using in-situ measurements. The results showed that Landsat-8 data can be rescaled using HSM to provide the actual soil moisture in the soil. On the other side, it is possible to remove the anomaly from the Sentinel-1 backscatter using either Landsat-8 or HSM. The elimination of the above problems explained a significant portion of the differences between the two sensors.","PeriodicalId":38707,"journal":{"name":"International Journal of Geoinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48119671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The rapid outbreak of coronavirus disease 2019 (COVID-19) has demonstrated the need for the development of new vaccine candidates and therapeutic drugs to fight against the underlying virus, severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2). Currently, no antiviral treatment is available to treat COVID-19, as treatment is mostly directed at relieving the symptoms, and retrospectively, herbal medicinal plants have been used for thousands of years as a medicinal alternative, including for the treatment of various viral illnesses. The aim of this study is to conduct a survey in terms of identifying the area where the population commonly uses the medicinal plant in comparison to the cumulative number of COVID-19 reports in each area, including the classification of medicinal plants by type and a stepwise approach shown in the form of geographic information maps in those areas. An observational study on the cultivation of medicinal plants those folk healers commonly used for healing. beneficial for treatment and strengthening the immunity of the people in 9 provinces of Thailand. According to the situation of the spread of COVID-19, there are people infected in Thailand. In each area where medicinal plants were used, there was a significant positive result when compared to the cumulative COVID-19 incidence; the majority was with the lowest cumulative COVID-19 incidence and the most commonly used medicinal plants, such as Artemisia annua, Harrisonia perforate (Blanco) Merr, Capparis micracantha, Tacca leontopetaloides, Andrographis paniculata, Phyllanthus emblica, Ficus carica, Tiliacora triandra, Terminalia bilaria, and Cannabis indica. This study exercise may lend enough credence to the potential value of Thai medicinal plants (herbs) as possible leads in anti-COVID-19 drug discovery through research and development.
{"title":"Geographic Information Database of Herbs against COVID-19 in Thailand: The Medicinal Plants those Folk Healers Commonly Used for Treatment and Boosting People's Immunity","authors":"","doi":"10.52939/ijg.v19i8.2785","DOIUrl":"https://doi.org/10.52939/ijg.v19i8.2785","url":null,"abstract":"The rapid outbreak of coronavirus disease 2019 (COVID-19) has demonstrated the need for the development of new vaccine candidates and therapeutic drugs to fight against the underlying virus, severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2). Currently, no antiviral treatment is available to treat COVID-19, as treatment is mostly directed at relieving the symptoms, and retrospectively, herbal medicinal plants have been used for thousands of years as a medicinal alternative, including for the treatment of various viral illnesses. The aim of this study is to conduct a survey in terms of identifying the area where the population commonly uses the medicinal plant in comparison to the cumulative number of COVID-19 reports in each area, including the classification of medicinal plants by type and a stepwise approach shown in the form of geographic information maps in those areas. An observational study on the cultivation of medicinal plants those folk healers commonly used for healing. beneficial for treatment and strengthening the immunity of the people in 9 provinces of Thailand. According to the situation of the spread of COVID-19, there are people infected in Thailand. In each area where medicinal plants were used, there was a significant positive result when compared to the cumulative COVID-19 incidence; the majority was with the lowest cumulative COVID-19 incidence and the most commonly used medicinal plants, such as Artemisia annua, Harrisonia perforate (Blanco) Merr, Capparis micracantha, Tacca leontopetaloides, Andrographis paniculata, Phyllanthus emblica, Ficus carica, Tiliacora triandra, Terminalia bilaria, and Cannabis indica. This study exercise may lend enough credence to the potential value of Thai medicinal plants (herbs) as possible leads in anti-COVID-19 drug discovery through research and development.","PeriodicalId":38707,"journal":{"name":"International Journal of Geoinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45437902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-01DOI: 10.52939/https://journals.sfu.ca/ijg/index.php/journal/article/view/2779
Infant mortality remains a pressing public health challenge globally. Despite advancements in healthcare, glaring disparities persist, as exemplified in Thailand. This study explored spatial variations in infant mortality rates (IMRs) across Thai provinces, integrating socio-economic, demographic, and health factors. Using data from national databases, we employed univariate and bivariate Local Indicators of Spatial Association (LISA) analyses to visualize spatial disparities, and Moran's I statistic assessed global spatial autocorrelation. Spatial regression models, including Ordinary Least Squares (OLS), Spatial Lag Model (SLM), and Spatial Error Model (SEM), analyzed the associations between IMRs and determinants. Our findings revealed stark IMRs disparities, especially in provinces like Phitsanulok, Narathiwat, and Songkhla. The SEM emerged as the most fitting model, given the data's spatial autocorrelation (R-Squared = 0.46). Crucial factors such as community organization strength, nighttime light, and exclusive breastfeeding were significantly linked to IMRs. Additionally, provinces like Phra Nakhon Si Ayutthaya and Rayong underscored socio-economic challenges, emphasizing the importance of tailored interventions. This study offers valuable insights for crafting targeted strategies, underscoring the pivotal role of geospatial techniques in shaping public health policies in Thailand.
婴儿死亡率仍然是全球公共卫生面临的紧迫挑战。尽管医疗保健取得了进步，但明显的差距依然存在，泰国就是一个例子。这项研究综合了社会经济、人口和健康因素，探讨了泰国各省婴儿死亡率的空间变化。利用国家数据库的数据，我们采用单变量和双变量局部空间关联指标（LISA）分析来可视化空间差异，Moran的I统计量评估了全球空间自相关。包括普通最小二乘法（OLS）、空间滞后模型（SLM）和空间误差模型（SEM）在内的空间回归模型分析了IMR与决定因素之间的关联。我们的研究结果揭示了明显的IMR差异，尤其是在Phitsanulok、Narathiwat和Songkhla等省。考虑到数据的空间自相关（R平方=0.46），SEM成为最适合的模型。社区组织强度、夜间光线和纯母乳喂养等关键因素与IMR显著相关。此外，Phra Nakhon Si Ayutthaya和Rayong等省强调了社会经济挑战，强调了量身定制干预措施的重要性。这项研究为制定有针对性的战略提供了宝贵的见解，强调了地理空间技术在泰国公共卫生政策制定中的关键作用。
{"title":"Spatial Association and Modeling of Infant Mortality in Thailand, 2020","authors":"","doi":"10.52939/https://journals.sfu.ca/ijg/index.php/journal/article/view/2779","DOIUrl":"https://doi.org/10.52939/https://journals.sfu.ca/ijg/index.php/journal/article/view/2779","url":null,"abstract":"Infant mortality remains a pressing public health challenge globally. Despite advancements in healthcare, glaring disparities persist, as exemplified in Thailand. This study explored spatial variations in infant mortality rates (IMRs) across Thai provinces, integrating socio-economic, demographic, and health factors. Using data from national databases, we employed univariate and bivariate Local Indicators of Spatial Association (LISA) analyses to visualize spatial disparities, and Moran's I statistic assessed global spatial autocorrelation. Spatial regression models, including Ordinary Least Squares (OLS), Spatial Lag Model (SLM), and Spatial Error Model (SEM), analyzed the associations between IMRs and determinants. Our findings revealed stark IMRs disparities, especially in provinces like Phitsanulok, Narathiwat, and Songkhla. The SEM emerged as the most fitting model, given the data's spatial autocorrelation (R-Squared = 0.46). Crucial factors such as community organization strength, nighttime light, and exclusive breastfeeding were significantly linked to IMRs. Additionally, provinces like Phra Nakhon Si Ayutthaya and Rayong underscored socio-economic challenges, emphasizing the importance of tailored interventions. This study offers valuable insights for crafting targeted strategies, underscoring the pivotal role of geospatial techniques in shaping public health policies in Thailand.","PeriodicalId":38707,"journal":{"name":"International Journal of Geoinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49566749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The existence of aquaculture in Marine Science Techno Park (MSTP), Jepara requires good-quality water. Remote sensing is the right solution to conduct routine, cost-effective, and wide-ranging monitoring. This study aims to estimate the Total Suspended Solid (TSS) and chlorophyll-a (Chl-a) values based on Sentinel-2 imagery. The reflectance values used are from Sentinel-2A (http://marine.copernicus.eu/acquisitions on 14 September and 30 October 2022). The TSS estimation algorithm used is a single band (red), while for Chl-a, it uses the sum of four visible bands namely blue, green, red, and near-infrared. The predicted TSS values from the Sentinel-2 ranged from 16.65-144.78 mg/L (average 44.59 mg/L) and Chl-a was 1.65-5.57 µg/L (average 3.35 µg/L) and 0.59 - 5.284 µg/L (average 2.49 µg/L) in September. While the TSS and Chl-a in-situ in October 2022, ranged from 48.80 - 78.20 mg/L (mean 55.81 mg/L) and 0.882 - 4.736 mg/L (average 3.10 mg/L). The performance of the algorithm used in this study is not suitable for implementation in this study regarding the low prediction error estimation values as follows: RMSE, bias, and MAPE values for TSS are 21.17 mg/L, -10.76, and 31.52%, respectively; and for Chl-a are 1.04 µg/L, 0.25, and 35.83%, respectively. Thus, a special algorithm needs to be developed for the coastal waters of Teluk Awur.
{"title":"Estimation of Chlorophyll–a and Total Suspended Solid Based on Observation and Sentinel-2 Imagery in Coastal Water Teluk Awur, Jepara-Indonesia","authors":"","doi":"10.52939/ijg.v19i8.2777","DOIUrl":"https://doi.org/10.52939/ijg.v19i8.2777","url":null,"abstract":"The existence of aquaculture in Marine Science Techno Park (MSTP), Jepara requires good-quality water. Remote sensing is the right solution to conduct routine, cost-effective, and wide-ranging monitoring. This study aims to estimate the Total Suspended Solid (TSS) and chlorophyll-a (Chl-a) values based on Sentinel-2 imagery. The reflectance values used are from Sentinel-2A (http://marine.copernicus.eu/acquisitions on 14 September and 30 October 2022). The TSS estimation algorithm used is a single band (red), while for Chl-a, it uses the sum of four visible bands namely blue, green, red, and near-infrared. The predicted TSS values from the Sentinel-2 ranged from 16.65-144.78 mg/L (average 44.59 mg/L) and Chl-a was 1.65-5.57 µg/L (average 3.35 µg/L) and 0.59 - 5.284 µg/L (average 2.49 µg/L) in September. While the TSS and Chl-a in-situ in October 2022, ranged from 48.80 - 78.20 mg/L (mean 55.81 mg/L) and 0.882 - 4.736 mg/L (average 3.10 mg/L). The performance of the algorithm used in this study is not suitable for implementation in this study regarding the low prediction error estimation values as follows: RMSE, bias, and MAPE values for TSS are 21.17 mg/L, -10.76, and 31.52%, respectively; and for Chl-a are 1.04 µg/L, 0.25, and 35.83%, respectively. Thus, a special algorithm needs to be developed for the coastal waters of Teluk Awur.","PeriodicalId":38707,"journal":{"name":"International Journal of Geoinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45073888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Improving the quality of positioning for safe navigation has been investigated over the last two decades by multi-sensor integration techniques. Although considerable improvements have been obtained, occurring of faults in measurement or dynamic models could degrade the performance of such integrated systems. These faults are un-modeled and may occur with different magnitudes and directions throughout the navigation time. In this study, the magnitude and direction under the presence of single and double faults in tight GPS/INS measurement and dynamic model were analyzed using the detection, identification, and adaptation method (DIA). Furthermore, the influence of the correlation between fault tests when single and double faults occur has also been investigated. The results show that under the presence of single faults, the fault test correctly identifies the faulty measurement/state. However, since there is a correlation between the fault tests, the faulty measurement/state pulls other measurements/states in different directions. When multiple faults test is implemented, several wrong identifications occur. This results from the correlation between the fault test for measurements/states pair and causing fault separability impossible when elements intersect between two measurements/state pairs.
{"title":"Analysis of Single and Double Faults Direction and Magnitude in Measurement and State Models of Tight GPS/INS System","authors":"A. Almagbile, A. Al-Rawabdeh","doi":"10.52939/ijg.v19i7.2745","DOIUrl":"https://doi.org/10.52939/ijg.v19i7.2745","url":null,"abstract":"Improving the quality of positioning for safe navigation has been investigated over the last two decades by multi-sensor integration techniques. Although considerable improvements have been obtained, occurring of faults in measurement or dynamic models could degrade the performance of such integrated systems. These faults are un-modeled and may occur with different magnitudes and directions throughout the navigation time. In this study, the magnitude and direction under the presence of single and double faults in tight GPS/INS measurement and dynamic model were analyzed using the detection, identification, and adaptation method (DIA). Furthermore, the influence of the correlation between fault tests when single and double faults occur has also been investigated. The results show that under the presence of single faults, the fault test correctly identifies the faulty measurement/state. However, since there is a correlation between the fault tests, the faulty measurement/state pulls other measurements/states in different directions. When multiple faults test is implemented, several wrong identifications occur. This results from the correlation between the fault test for measurements/states pair and causing fault separability impossible when elements intersect between two measurements/state pairs.","PeriodicalId":38707,"journal":{"name":"International Journal of Geoinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45865806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A flood is a natural catastrophe that causes heavy damage not only to people but also to properties. To prevent and mitigate flood damage, an accurate flood susceptibility map that reveals highly potential flood-prone areas is essential. This study aims to construct flood susceptibility maps in the Huong Khe district using three machine learning algorithms, namely the K - Nearest Neighbour (KNN), the Support Vector Machine (SVM) and Artificial Neural Network (ANN). Training and testing datasets were extracted from Sentinel-1 SAR images. Seven causative factors were selected as input for predictive models after removing high-correlation factors and unimportant factors through a rigorous screening process by analyzing the Pearson correlation coefficient (PCC) and calculating the information gain ratio (InGR). The model's hyperparameters were found by grid search algorithm integrated 5-fold cross-validation. The three optimal flood susceptibility models showed excellent performance, with very high accuracy indices in the training and testing phases, over 90% of overall accuracy and UAC values. High and very high susceptibility classes on flood susceptibility maps accounted for around 18% of the total study area and were mainly located in residential and agricultural areas. Thus, there is a need to make proper land use planning for these areas to reduce damage in flood seasons.
{"title":"Flood Susceptibility Mapping Using Machine Learning Algorithms: A Case Study in Huong Khe District, Ha Tinh Province, Vietnam","authors":"D. L. Nguyen, T. Chou, T. Hoang, M. H. Chen","doi":"10.52939/ijg.v19i7.2739","DOIUrl":"https://doi.org/10.52939/ijg.v19i7.2739","url":null,"abstract":"A flood is a natural catastrophe that causes heavy damage not only to people but also to properties. To prevent and mitigate flood damage, an accurate flood susceptibility map that reveals highly potential flood-prone areas is essential. This study aims to construct flood susceptibility maps in the Huong Khe district using three machine learning algorithms, namely the K - Nearest Neighbour (KNN), the Support Vector Machine (SVM) and Artificial Neural Network (ANN). Training and testing datasets were extracted from Sentinel-1 SAR images. Seven causative factors were selected as input for predictive models after removing high-correlation factors and unimportant factors through a rigorous screening process by analyzing the Pearson correlation coefficient (PCC) and calculating the information gain ratio (InGR). The model's hyperparameters were found by grid search algorithm integrated 5-fold cross-validation. The three optimal flood susceptibility models showed excellent performance, with very high accuracy indices in the training and testing phases, over 90% of overall accuracy and UAC values. High and very high susceptibility classes on flood susceptibility maps accounted for around 18% of the total study area and were mainly located in residential and agricultural areas. Thus, there is a need to make proper land use planning for these areas to reduce damage in flood seasons.","PeriodicalId":38707,"journal":{"name":"International Journal of Geoinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44771924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Landslides are natural hazards that pose a significant threat to human lives and infrastructure. Landslide susceptibility mapping aims to classify areas at risk of landslides. Multi-Criteria Decision Making (MCDM) algorithms have the advantage of incorporating expert opinions, while Statistics and Machine Learning models demonstrate greater objectivity. This study compares three representative models, namely Analytic Hierarchy Process (AHP), Frequency Ratio (FR), and Random Forest (RF), for developing a landslide susceptibility model in Van Yen District, Yen Bai Province. The classification points for landslides were divided into a 70% training set and a 30% testing set. Thirteen conditioning factors were used to evaluate the landslide's influences. The results show that the AHP and FR models perform well with AUC = 0.842 and AUC = 0.852, respectively, while the RF model outperforms them with AUC = 0.949. The study demonstrates the applicability of these models for analyzing landslide susceptibility in the research area, highlighting the strong potential of machine learning models.
{"title":"Comparison of Multi-Criteria Decision Making, Statistics, and Machine Learning Models for Landslide Susceptibility Mapping in Van Yen District, Yen Bai Province, Vietnam","authors":"","doi":"10.52939/ijg.v19i7.2743","DOIUrl":"https://doi.org/10.52939/ijg.v19i7.2743","url":null,"abstract":"Landslides are natural hazards that pose a significant threat to human lives and infrastructure. Landslide susceptibility mapping aims to classify areas at risk of landslides. Multi-Criteria Decision Making (MCDM) algorithms have the advantage of incorporating expert opinions, while Statistics and Machine Learning models demonstrate greater objectivity. This study compares three representative models, namely Analytic Hierarchy Process (AHP), Frequency Ratio (FR), and Random Forest (RF), for developing a landslide susceptibility model in Van Yen District, Yen Bai Province. The classification points for landslides were divided into a 70% training set and a 30% testing set. Thirteen conditioning factors were used to evaluate the landslide's influences. The results show that the AHP and FR models perform well with AUC = 0.842 and AUC = 0.852, respectively, while the RF model outperforms them with AUC = 0.949. The study demonstrates the applicability of these models for analyzing landslide susceptibility in the research area, highlighting the strong potential of machine learning models.","PeriodicalId":38707,"journal":{"name":"International Journal of Geoinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41246362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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