Geographic Information System Platform Research Articles

Multi-Scale Analysis of PM2.5 Concentrations in the Yangtze River Economic Belt: Investigating the Combined Impact of Natural and Human Factors

Air pollutants, primarily PM2.5, have inflicted significant harm on public health and sustainable urban development in the Yangtze River Economic Belt (YREB). Previous studies often neglected the coordinated measurement of PM2.5 human and natural factors in this area. Therefore, this paper focuses on the YREB. Using a geographic information system (GIS) platform, along with remote sensing and statistical data spanning from 2000 to 2020, this study employs spatial analysis to uncover the spatial-temporal characteristics of PM2.5 and its spatial agglomeration patterns. Furthermore, this study further employs the spatial panel Durbin model to investigate the natural and anthropogenic factors driving PM2.5 concentrations across multiple scales. The analysis of the results reveals an “M”-shaped change trend in PM2.5 concentrations within the YREB. PM2.5 concentrations exhibit significant spatial agglomeration characteristics, whereby most urban agglomerations are high-pollution areas. Moreover, the changes in PM2.5 concentrations are jointly influenced by several factors, including the secondary industry, urban built-up area, population density, annual precipitation, and NDVI. Furthermore, the dominant factors influencing PM2.5 concentrations in the three major urban agglomerations exhibit both similarities and differences. In addition, for effective governance coordination across regions, policymakers should diligently consider both the shared predominant factors and the varying factors specific to each region in the future. This study expands the research content of watershed PM2.5 collaborative governance, and further provides practical support for other watershed environmental governance and urban sustainable management.

Pavement surface distress evaluation using a terrestrial laser scanner

The accurate measurement of pavement distresses is essential for setting preservation and rehabilitation priorities. The manual measurement of surface distresses is the most commonly used method for evaluating the severity of pavement distresses. However, the manual method has low accuracy and consistency. Thus, in the present study, images of different pavement distresses – potholes, cracking, patching and ravelling of selected sections – were captured using a terrestrial laser scanner (TLS). The surface area of these distresses was measured using the Leica Cyclone software. The obtained results were validated by collecting a similar distress pattern at a different location using the same equipment. The measured surface area was executed in a geographic information system platform to develop an automated database for the prioritisation of selected pavement sections of Mumbai City, India for pavement maintenance. It was observed that the TLS technique could be used effectively for safer and more accurate surface distress measurement.

SIGeR-RV: A Web-Geographic Information System-Based System for Risk Management of Road Networks Exposed to Natural Hazards

The consequences of natural hazards are frequently estimated by the direct cost of recovering damaged infrastructure and the indirect costs to road users, economic activities, and impacts on society. Road networks are crucial in economic systems, logistic chain continuity, accessibility, mobility, and the evacuation of population during and after extreme events. Risk management systems (RMS) are used to estimate the potential consequences of natural events and to assess strategies for risk reduction. These commonly assess hazards, assets exposure, economic losses, and risk mitigation actions, among others. The general framework of RMS can be adapted to different scenarios. Still, local characteristics, such as the types of hazards and physical assets, cannot always be directly adopted from these systems. This study discusses the development of SIGeR-RV, an RMS developed in Chile for road networks exposed to multiple natural hazards. The RMS was implemented in a web-based geographic information system platform able to display hazard maps, quantify risk levels, prioritize mitigation strategies, and estimate direct and indirect costs and social vulnerability. The content and various capacities of the system are detailed, following the steps marked in its framework. This first version of SIGeR-RV considers seismic hazard, volcanic lahars, and hydro-meteorological hazards that affect road platforms, bridges, tunnels, and cut slopes. The system currently serves as a tool for the Ministry of Public Works of Chile and other decision makers to estimate budget requirements for increasing resilience of the road network, identifying vulnerable road segments, and assessing the socioeconomical impacts of risk reduction.

A research on data integration and application technology of urban comprehensive pipe gallery based on three‐dimensional geographic information system platform

AbstractThree‐dimensional Geographic Information System (3D GIS) technology is often used in geology, mining, hydrology and other fields. It is also used in intelligent management of urban comprehensive pipe gallery projects, because the real environment with the sky, ground, and underground can be intuitively simulated in the virtual reality environment by 3D GIS platform. Through the research on data integration and application technology of urban comprehensive pipe gallery, the 3D model of comprehensive pipe gallery project data can be created based on Building Information Modelling and GIS technologies, and a comprehensive underground pipelines network management information system has been developed to realise the integration of various data, including 3D data of comprehensive pipe gallery model and underground pipe network model, 2D or 3D GIS data of the ground and underground etc. On this basis, the functions of intelligent planning and approval management, the safety analysis and disaster prevention planning and decision‐making of urban comprehensive pipe gallery projects can be further realised. The research results have been applied to the planning and construction process management of the comprehensive pipe gallery project in the sub‐centre of Beijing.

Using digital technologies to plan and manage the pipelines network in city

AbstractPromoting the intelligent level of urban pipelines network planning and management is an important way to improve the rational development and utilisation of urban underground space and ensure the safe operation of urban infrastructure systems. The research on using the rapid and visual expression technology of 3D pipelines network model data, which has the functions of techno‐economic indexes calculation and automatic comparison of underground pipeline construction projects, effectively avoids possible errors in manual calculation and comparison and improves the efficiency and accuracy of the digital declaration process for project planning. In the 3D Geographic Information Systems (GIS) platform environment, this research help to develop many analysis and approval functions based on the data of the underground pipelines network model and the integrated pipes gallery Building Information Modelling, such as evaluating the compliance, feasibility and scientific of the planning scheme of the underground pipeline construction project, comparing the completion data with the data approved by the administrative departments of the project, evaluating the consistency of them and judging the compliance of the project completion data. The application results of some actual projects cases show that this research can improve the efficiency of planning declaration and approval analysis.

ASSESSMENT OF CROPLAND TRANSITION TO SOLAR FARMS AND OTHER LAND USE/COVER USING RS, GIS AND ANN-CA

Abstract. The balance between food security and energy security is a national issue of extreme importance. A more stable supply of electricity could be achieved as solar farms expand but at the expense of losing some of the prime agricultural lands which endangers availability of sufficient agricultural produce. This study aims to use ANN-Cellular Automata (CA) via the Geographic Information System (GIS) platform and remote sensing (RS) data to assess the impact of cropland transition to solar farms and other land use/land cover (LULC). Several remotely sensed data were processed including MODIS land cover data (MCD12Q1), VIIRS nighttime lights (VNL v2.1), Advanced Himawari Imager Shortwave Radiation (AHI-SWR) product, and population density (LandScan) as inputs to the Cellular Automata-Artificial Neural Network (CA-ANN) model to simulate LULC changes in Tarlac Province, Philippines via the Modules for Land Use Change Evaluation (MOLUSCE) plugin in QGIS. For years 2019, 2023 and 2027 with 2015 as the base year, results showed an increasing trend for savannas and grassland with ΔLULC values of +11.4% to +15.1% and +0.2% to 3.5%, respectively. Meanwhile, a decreasing trend is observed for built-up/water, forest, and cropland with ΔLULC values of −3.0% to −6.3%, −8.5% to −21.1%, and −3.9% to −4.2%, respectively. Results also showed a conversion of a 100-ha area of croplands to solar farm from year 2019 to 2023 which translates to an estimated monetary loss from agricultural produce due to solar farm conversion amounting to Php 7,584,720.00 (~USD 138,000) which is equivalent to the total average annual income of about 67 families in Tarlac. Lastly, the simulated 2027 LULC map showed pixels with unrealistic conversions from solar farm (year 2023) to cropland (year 2027). To improve the model, it is recommended to add more spatial data to effectively capture factors that may contribute to the expansion of solar farms in the future. Moreover, high resolution LULC maps (vector maps if available) can be used instead of a course resolution satellite-derived raster data. Nonetheless, this study has demonstrated the use of RS, GIS and machine learning techniques to model cropland conversion to solar farms and other LULC classes. Results from this study can provide scientific data to policy makers, solar industry players and other relevant stakeholders in doing technoeconomic assessment of solar farm development and expansion considering its effect on energy security and food security towards national sustainable development.

Herbicide selection through GIS-based groundwater quality maps

Reducing consumption or increasing the efficiency of chemical inputs in the agriculture sector has been among the most important issues which are in line with sustainable use strategies. Despite some environmental impacts, chemical herbicides are widely used to control the weed population of crops due to advantages such as saving application costs, time, and labor force. Groundwater is the most common type of solvent for applying herbicides in farming systems. Hence, this research was conducted to investigate the effect of groundwater quality parameters (acidity and water hardness) on the suitability of herbicides registered in wheat fields of Khorasan-Razavi province in Iran. For this purpose, threshold-based water hardness and acidity classified maps were produced through the geographical information system (GIS)-based techniques, and the groundwater suitability of the study area was investigated for the application of various herbicides (narrow-leaf, broad-leaf, and dual-purpose). The results showed that most of the groundwater in the study area was alkaline (with an average value of pH 7.82) and hard (water hardness >200 PPM). It is shown that in the areas where the groundwater is alkaline and hard, the chemical weed management of wheat fields is extremely restricted. In this regard, it is shown that by simultaneously considering the two qualitative properties of groundwater (pH and water hardness), only two herbicides (Puma super and Atlantis) can be recommended without worrying about the decrease in their efficiency, while the rest of the herbicides, maybe are not applicable (in a strict mode) or can be used more conservatively through strongly advised suggestions. The study also revealed that water hardness compared to acidity has generally a more negative impact on the efficacy of wheat herbicides. In this research, a map of the amount of ammonium sulfate (AMS) needed to overcome the adverse effects of water hardness on the effectiveness of herbicides is produced. Overall, the present study suggests that by providing the raster map of groundwater quality parameters we can precisely locate the suitable areas where proper herbicides should be taken into account to, in turn, avoid any serious risks of leaving a huge amount of chemicals in the environment. From a practical point of view, such kind of information can also be uploaded into the Web-Based GIS Platforms or be implemented in software applications.

Route selection for road vehicle real-driving emissions test using GIS

The European Commission has developed a Real-Driving Emissions (RDE) test for road vehicles that complies with the urban, rural and motorway criteria to ensure the exhaust emission produced could be accurately evaluated. Therefore, careful measures are taken in selecting the best routes to ensure the testing fulfils the targeted criteria. As there is no common method exist to select the testing route, analysis on the testing route selection was implemented using Geographic Information System (GIS) platform that is capable to deal with large vector data. Thus, the proposed framework facilitates the testing route selection process which is difficult to determine without a proper spatial analysis of network data. Specific route information comprises of road categories, route speed range and route classes were collected from the Open StreetMap (OSM) database. The generated route density and the topological route network structure were used to further analyse the suitability route as the requirement of Real-Driving Emissions test. As a result, five candidates of Real-Driving Emissions test route have been selected in Malaysia as a case study using GIS platform.

Logistics 4.0 in the agri-food supply chain with blockchain: a case study

ABSTRACT Blockchain is an emerging technology with the potential to radically transform logistics in the highly complex environment of the agri-food supply chain. The key challenges of the agri-food sector include overcoming the limitations (time, cost and quality) to developing global and efficient transparency and traceability of food, eliminating expensive intermediaries, and ensuring no disruption between suppliers and retailers. This paper describes a practical approach by not only using smart contracts to integrate various stakeholders but also incorporating a Public Participation Geographic Information System platform to ensure traceability of the operations conducted in an agri-food supply chain. As a factor in improving distribution and minimising intermediaries, third-party logistics participation is included in this research. Through a case study, the entities and interactions between processes within a blockchain system based on a smart contract are identified. The findings reveal an innovative blockchain approach to logistics in the agri-food supply chain.

Evaluating the influence of environmental variables on the length-weight relationship and prediction modelling in flathead grey mullet, Mugil cephalus Linnaeus, 1758.

Fish stocks that are grown under diverse environmental conditions have different biometric relationships and growth patterns. The biometric length-weight relationship (LWR) is an essential fishery assessment tool, as fish growth is continuous and depends on genetic and environmental factors. The present study attempts to understand the LWR of the flathead grey mullet, Mugil cephalus Linnaeus, 1758, from different locations. The study area encompassed its distribution in the wild across freshwater location (one), coastal habitats (eight locations), and estuaries (six locations) in India to determine the relationship between various environmental parameters. Specimens (n = 476) of M. cephalus were collected from commercial catches and the length and weight of individual specimens were recorded. Monthly data from the study locations were extracted for nine environmental variables from the datasets downloaded from the Physical Oceanography Distributed Active Archive Center (PO.DAAC) and the Copernicus Marine Environment Monitoring Service (CMEMS) over 16 years (2002 to 2017) on the Geographical Information System platform. The parameters of the LWR, intercept 'a' and slope or regression coefficient 'b', varied from 0.005321 to 0.22182 and 2.235 to 3.173, respectively. The condition factor ranged from 0.92 to 1.41. The partial least squares (PLS) score scatter plot matrix indicated differences in the environmental variables between the locations. PLS analysis of the regression coefficient and environment parameters revealed that certain environment variables viz., sea surface temperature, salinity, dissolved oxygen, nitrate, and phosphate, played a positive role. However, chlorophyll, pH, silicate, and iron played a negative role in influencing weight growth across various locations. The results revealed that the M. cephalus specimens from three locations, Mandapam, Karwar, and Ratnagiri, possessed significantly higher fitness to their environment than those from the other six locations. The PLS model can be used to predict weight growth under the various environmental conditions of different ecosystems. The three identified locations are useful sites for the mariculture of this species considering their growth performance, the environmental variables, and their interactions. The results of this study will improve the management and conservation of exploited stocks in regions affected by climate change. Our results will also aid in making environment clearance decisions for coastal development projects and will improve the efficiency of mariculture systems.

WebGIS-Based Real-Time Surveillance and Response System for Vector-Borne Infectious Diseases.

The diseases transmitted through vectors such as mosquitoes are named vector-borne diseases (VBDs), such as malaria, dengue, and leishmaniasis. Malaria spreads by a vector named Anopheles mosquitos. Dengue is transmitted through the bite of the female vector Aedes aegypti or Aedes albopictus mosquito. The female Phlebotomine sandfly is the vector that transmits leishmaniasis. The best way to control VBDs is to identify breeding sites for their vectors. This can be efficiently accomplished by the Geographical Information System (GIS). The objective was to find the relation between climatic factors (temperature, humidity, and precipitation) to identify breeding sites for these vectors. Our data contained imbalance classes, so data oversampling of different sizes was created. The machine learning models used were Light Gradient Boosting Machine, Random Forest, Decision Tree, Support Vector Machine, and Multi-Layer Perceptron for model training. Their results were compared and analyzed to select the best model for disease prediction in Punjab, Pakistan. Random Forest was the selected model with 93.97% accuracy. Accuracy was measured using an F score, precision, or recall. Temperature, precipitation, and specific humidity significantly affect the spread of dengue, malaria, and leishmaniasis. A user-friendly web-based GIS platform was also developed for concerned citizens and policymakers.

GIS-based revised universal soil loss equation for estimating annual soil erosion: a case of lower Kulsi basin, India

Assessment and estimation of soil loss is a fundamental aspect of land and water resource conservation and management practices as it provides necessary information in the course of watershed-level development of a region. The soil loss model of Wischmeier and Smith, popularly known as the Revised Universal Soil Loss Equation, was selected to estimate soil loss in the lower Kulsi river basin due to its simplicity, versatility, and flexibility nature method in the Geographic information system platform. Most original governmental datasets, mainly daily gauge rainfall from 2009 to 2018, satellite images for land use land cover, digital elevation model of Shuttle Radar Topographic Mission for topographic factor, and National Bureau of Soil Survey and Land-use Planning, India soil map were utilized to estimate the average annual soil erosion. The estimated average annual soil erosion ranges from 0.0 to 6.45 thousand t ha−1y−1, grouped into low, moderate, high, and very high risk of soil erosion. A basin area of 36.235 km2 (1.85%) basin area was identified as high to very high zones of soil erosion risk and needed immediate conservation measures to reduce the erosion risk.

A REVIEW OF 3D SPATIAL DATA MODELS FOR SURFACE AND SUBSURFACE INTEGRATION OF SPATIAL OBJECTS

Abstract. Three dimensional (3D) spatial data models have been known as a geographic information system platform for representing the dimensionality of spatial objects with respect to the real world. They can be used to mimmick the real-world objects above the ground, on the ground and underground. A 3D spatial data model that can integrate surface and subsurface elements will have a significant impact on engineering, spatial and urban planning, and the built environment. Such an impact includes good infrastructure development, proper planning, result-oriented installation of utilities, location-specific excavation of existing utilities, and cost-effective economy resulting from minimal damage of infrastructures. This article explores and documents the state-of-the-art with respect to 3D spatial data models specifically for the integration of surface and subsurface objects based on a thorough review of the literature in academic articles, technical reports, and web-based materials. To better understand the scope, this paper reviews the different 3D spatial modelling approach for surface spatial objects, subsurface spatial objects, and with keen focus on surface and subsurface spatial objects integration. The Open Geospatial Consortium (OGC) has played an important role in data integration and has a core space in 3D spatial data model evolution. One of the recent data models for such task is CityJSON due to its simplicity in data storage, visualization, manipulation, and data update compared to CityGML. The parameters influencing the choice of models can be attributed to the availability of data structure, applicability, exchange format and relevance. Based on the range of 3D spatial data model reviewed, a unified model is expedient for integration of surface and subsurface spatial objects.

Earthquake hotspot and coldspot: Where, why and how?

• Global earthquake hotspot and coldspot were identified. • Statistical methods of hotspot analysis and optimized hotspot analysis. • Benioff/subduction zones are the most potential hotspot zone. • Optimized hotspot analysis emerges as the most probabilistic method. Global tectonic activities are playing an important role in the occurrences of devastating earthquakes and related long-term changes in the earth's system surface. However, the plate tectonics processes and their interaction with the earth's crust are very much complex, and it is a subject of unending debate. Therefore, tectonism-induced landslide, tsunami, liquefaction, and fire are significant earthquake-related hazards, which have a larger potential and overwhelming impact on life and infrastructural properties throughout the world. In this study, we have emphasized the identification of earthquake hotspot and coldspot zones considering historical earthquake data across the plate boundary of the world. Here, a total of 7773 historical earthquake points were collected as input parameters with three-moment magnitude (M w ) classes (<4.5, 4.5-6.0, and >6.0). Two statistical methods namely hotspot analysis (Getis-Ord GI*) and optimized hotspot analysis were used in the detection of global earthquake hotspot and coldspot zones using the geographic information system (GIS) platform. Hotspot and coldspot zone are identified under 99%, 95%, and 90% confidence levels. Alongside, here we have also discussed the paradigm, evidence of tectonic, and historical earthquakes, and how and why they are formed with the help of the existing theoretical constraints. The result indicates that the Pacific ring of fire, Peru-Chile Trench, and the mid-Atlantic oceanic ridge is fall in the hotspot zones of 99%, 95%, and 90% confidence levels.

Groundwater potential mapping in hard rock terrain using remote sensing, geospatial and aeromagnetic data

• Geospatial techniques applied in integrated water resources management. • Remote sensing data reveal groundwater recharge zones. • Aeromagnetic data have a higher depth of investigation than remote sensing data. • Integrating remote sensing and geophysical data can surpass depth probing methods. The need for water security in different regions of the world has led to the deployment of remote sensing (RS) and geographic information systems (GIS) as decision support tools with geophysical methods. In this study, the remote sensing, geospatial and aeromagnetic data were integrated for mapping the groundwater potential at the University of Ilorin, Nigeria in West Africa. Several remote sensing and geospatial datasets (geomorphology, lineament density, slope, rainfall, land use/land cover, soil type and drainage density) were enhanced, weighted, prioritised and ranked using the analytical hierarchy process (AHP) technique. Subsequently, the thematic datasets were integrated into a GIS platform to produce a groundwater potentiality zonation (GWPZ) map. Visualisation of the corrected aeromagnetic data was improved by using some image enhancement techniques (filters) to produce the magnetic anomaly maps that revealed gross and subtle subsurface features. Multiple validation of the GWPZ map was achieved using the magnetic anomalies maps, borehole groundwater potential indices (GWPI) and the receiver operating characteristic (ROC) curves. The GWPZ map generated was classified into three groundwater potential classes with different spatial distributions. These include moderate (60.6%), high (5.9%), and low (33.5%). The magnetic anomalies maps agreed well with the remotely sensed models through surface-subsurface lineaments superposition. The area under curve (AUC) of the ROC showed that the predictive rate of the GWPZ model was 0.73. This value suggested that the model satisfactorily predicted the groundwater potential of the study area. Thus, this present study demonstrated the relevance of geospatial and geophysical techniques for regional groundwater potential mapping at the assessment phase of integrated water resources management towards providing a better understanding of the hydrogeology for easy decision-making and better groundwater management.

Morphological characteristics of Kanthatmakur vagu Watershed of Warangal District: Using Geographical Information System (GIS)

The present study relates to the morphometric analysis of Kanthatmakur vagu watershed using GIS. The GIS platform facilitates the analysis of various morphometric parameters. This watershed has higher order of 5th order. The present study has area of about 440.4 Km2 with total length of basin is 45.5 Km. Total number of streams in the basin is 635, ranging from 10 to 319. The basin area exhibits dendritic drainage pattern with bifurcation ratio ranges from 0.1 to 9.2, with mean bifurcation ration of 3.4. Length of stream of all orders are 481.8 km. Length of overland slow is 0.55 km and perimeter of the basin found to be 119.7 km. Areal aspects such as drainage density is calculated as 1.1, drainage frequency is 1.4 and drainage texture calculated as 5.3. Infiltration number of the watershed is 0.8. The watershed has 0.3 form factor ratio, 0.5 elongation ration and 0.4 circularity ratio. Relief of the watershed is 261m and ratio of relief is 6.3. The characteristics of morphological analysis of the Kanthatmakur vagu watershed has peak flow, is an elongated and less probable to flooding as well as moderate to slightly steep slope. In the watershed infiltration is high and run off will be low.

Spatiotemporal dynamic decoupling states of eco-environmental quality and land-use carbon emissions: A case study of Qingdao City, China

Spatiotemporal dynamic association between eco-environmental quality and land-use carbon emissions (LCE) is an indispensable part of regional and city planning and land management and a necessary condition for maintaining sustainable development. With the increasing awareness of the importance of sustainable development, quantifying the relationship between eco-environmental quality and LCE needs to be improved. Based on raster datasets of carbon emissions from fossil fuels, remotely sensed environmental variables, and land-use and land-cover change, this study aimed to combine an improved model of LCE, remote sensing ecological index (RSEI), and decoupling theory along with the help of the Google Earth Engine (GEE) and geographical information system platforms to explore the spatiotemporal patterns and trends of the eco-environmental quality and LCE over Qingdao City in the coastal economic belt of China for 2005–2019. There are three significant findings in this study. First, during 2005–2019, RSEI in the study area showed an overall upward trend (0.4365 in 2005 and 0.5378 in 2019), with a significant difference in the spatial distribution. Second, during 2005–2019, the total LCE value showed an increasing trend (4.028 million tonnes (Mt) in 2005 and 7.929 Mt. in 2019), with the study area acting as a carbon source. Third, the study area exhibited the four decoupling states of mainly weak decoupling (WD), expansive negative decoupling (END), strong decoupling (SD), and strong negative decoupling (SND). Finally, space management policies should be formulated based on different spatiotemporal characteristics and decoupling states to achieve carbon emission reductions and high-quality development.

Behaviour of Precariously Balanced Rocks under Seismic Excitation

A naturally occurring geological formation is precariously balanced rocks (PBRs) which might easily topple by an earthquake with severe ground motion. These rocks have been at the same place for many years. Strong ground accelerations have not occurred in the area for thousands of years as shown by the zone of unstable rocks. So, they are significant in both the engineering and seismological domains since they provide source of data for the maximum earthquake ground motion occurring at a site over the rock’s life time. The precariously balanced rock taken for this study is Krishna Butter Ball which is in Mahabalipuram, Chengalpattu district. In the numerical analysis, the peak ground acceleration at which the Krishna butter ball may topple is obtained. To identify the possible future earthquake, deterministic seismic hazard analysis has been carried out for Chengalpattu district. For this analysis, fault details have been obtained from Geological Survey of India. Arc map 10.1 version in Geographical Information System (GIS) platform is used for the interpretation of data like fault length, source (fault) to site (Mahabalipuram) distance from the fault details given by GSI. From the results of Deterministic Seismic Hazard Analysis (DSHA), it is observed that the possible future earthquake may have the magnitude in the range of 6.03 to 7.9. The computed peak ground acceleration (PGA) value corresponding to those magnitude ranges from 0.027g to 0.49g. The maximum PGA value 0.49g corresponds to the Palar river fault which is just 28.53 km away from the Krishna butter ball. From the results of numerical analysis, the behavior of Krishna butter ball for three different PGA i.e. 0.05g, 0.1g, 0.15g has been observed. Newmark sliding block analysis said that the rock may topple at the PGA of 0.35g. From this it can be concluded that the toppling acceleration of Krishna butter ball is relatively low.

Integrating Remote Sensing and GIS Techniques for Accurate Mapping and Analysis of Oil Palm Plantation Distribution in Kelantan: A Case Study

The research conducted in Kelantan focused on analysing the distribution of oil palm plantations using remote sensing data and ArcGIS, a Geographic Information System (GIS) platform. The demand for accurate and up-to-date information on oil palm plantations has been increasing due to advancements in technology and the need for effective management of the environment. The study aimed to compare the distribution of oil palm plantations in 2016 and 2021 by using vegetation analysis techniques such as Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Soil-Adjusted Vegetation Index (SAVI). Remote sensing data from Landsat 8, specifically Bands 5, 4, and 2, were utilized to derive these vegetation indices. Ground-truthing data, obtained through GPS coordinates, were employed to increase the accuracy of the analysis. The expansion of oil palm plantations and non-oil palm areas was assessed using the Supervised Classification Maximum Likelihood method. The distribution data of oil palm plantations is highly sought after by oil palm plantation companies and serves public and private purposes, contributing to environmental monitoring and promoting sustainable practices.

A Spatial Decision Support System for Prioritizing Repair Interventions on Power Networks

The business continuity of services provided by Critical Infrastructures is vital in order to ensure the security, the economy and the public’s health of a Nation. Delays and bad recovery strategies after disasters or failures can lead to impairing impacts in terms of injury to people, environmental pollution and loss of time, money and resources. In such a context, the adoption of a spatial Decision Support System (DSS) might play a crucial role in order to help operators to adopt the best recovery strategy in the shortest possible time frame. Current approaches do not consider the problem of assigning an intervention location to a maintenance crew and do not account for the effective time needed for emergency intervention. In this paper we develop a novel spatial multi-criteria DSS methodology for prioritizing repair interventions on power networks. The multi criteria strategy is solved by the adoption of Incomplete Analytic Hierarchy Process (AHP) which computes holistic assignment costs as the result of the combination of multiple and possibly conflicting metrics of cost.Then, we use the holistic costs as the basis for a task assignment phase that is based on the Hungarian algorithm. The proposed strategy has been implemented as a module in the Decision Support System, namely Critical Infrastructure Protection Risk Analysis and Forecast (CIPCast), whose outputs are represented on a web-based Geographic Information System (GIS) platform. The effectiveness of the proposed multi-criteria strategy has been validated via a real case study on the Rome City electrical distribution network.