Geographic Information System Learning Research Articles

A review of integrated use of machine learning algorithms, GIS and remote sensing techniques in the prediction of rainfall patterns and floods in the U. S.

This paper systematically reviews the integrated use of machine learning algorithms, Geographic Information Systems (GIS), and Remote Sensing (RS) techniques in the prediction of rainfall patterns and flood events in the U.S. With increasing climate variability, accurate forecasting of rainfall and flood risks has become critical to safeguarding communities and infrastructure. GIS enables spatial analysis and mapping of flood-prone areas, supporting risk assessment and disaster preparedness. RS contributes real-time satellite imagery and environmental data, essential for tracking rainfall patterns and assessing surface conditions. Machine learning algorithms enhance these technologies by providing predictive modeling capabilities, allowing for more precise forecasts of rainfall intensity and flood potential. This paper explores the synergy between GIS, RS, and machine learning, emphasizing their combined impact on improving flood prediction accuracy and decision-making in disaster management. Key challenges, including data heterogeneity, computational demands, and the integration of diverse datasets, are discussed. Additionally, the paper reviews current U.S. policies on data-sharing and technology adoption, highlighting the need for regulatory frameworks that support innovation while ensuring data privacy and accuracy. Through an analysis of recent studies, this paper presents a comprehensive overview of the advantages and constraints of using these integrated technologies for flood prediction, offering insights into future directions and recommendations for enhancing flood management systems. The review concludes that advancing integrated GIS, RS, and machine learning applications will require addressing data-related challenges and fostering collaborative efforts across agencies to strengthen flood prediction and resilience capabilities in the U.S.

Spatial Analysis of Informal Recyclable Station System for Integration to Formal Solid Waste Management System and Circular Economy–Proposing Integrated Methodology of GIS, Remote Sensing, and Statistical Learning

AbstractRecently, the transition to the circular economy has become environmentally and economically urgent for every single nation in the world. Closing the loops of material is one of the key ideas behind the foundation of a circular economy (CE). The informal recyclable stations (IRSs) within the solid waste management (SWM) system play an important role as the reversed logistic system, being in charge of collecting and trading recyclable solid waste. This study aimed to comprehend the spatial nature of the system of IRSs in Danang city, Hue city, and Hoi An city as representative sites for the whole of central Vietnam and the nation. The integration of geographic information system (GIS), remote sensing, and statistical learning was performed to clarify spatial characteristics and dynamics of the system of IRSs as well as combat status of limitations of available data in developing countries. Results denoted that the system of IRSs was distributed in close proximity to transportation systems and residential areas with low vegetation coverage. Coverage ratios of the system of IRSs did not strongly fluctuate in case the number of IRSs decreased by 80% regarding the 3500 m distance covered. Negative binomial regression proved to be the most congruent model for understanding the prevalence of IRSs in central Vietnam. Population and normalized difference vegetation index were statistically related to prevalence of IRS. While linear regression depicted balance between variance and bias, support vector machine would be applied if prioritized aim is model performance. The results of this study are a scientific base for the management of the IRS system and the integration of this system into a formal SWM system as well as the transition to a CE.

Optimizing Sustainable Community Spatial Layouts and Public Service Facility Accessibility: A GIS and Machine Learning Approach

This paper discusses the integration of Geographic Information Systems (GIS) and machine learning (ML) to enhance community performance based on sustainable urban planning by optimising community layouts and providing better access to public services. GIS is a technology that provides spatial data on the land use and the risks, which can support urban planners to understand the existing urban settlement. On the other hand, ML can predict the future needs of the public services such as transport, education and health care, which can help the planners to allocate the resources. The combined application of GIS and ML can assist urban planners to create resilient, climate-adaptive and socially equitable communities, and can also address the rapid urbanisation issues that can efficiently meet the future community demands. It is a framework that can enhance the accessibility to the public service and develops a socially equitable, environmentally sustainable and optimised community wellbeing, which can provide an all-sided strategy for the city facing the climatic change and demographic change.

Combining GIS and machine learning for enhanced tsunami risk management: A review of current approaches and unexplored future potential

This review article aims to discuss the current status and future potential of Geographic Information Systems (GIS) in map-based technology of tsunami risk management, especially in seismically active, well-known tsunami regions of the world. It presents GIS technologies for hazard mapping, risk assessment, and information generation for disaster-response operations. These are important tools for accurately mapping vulnerable areas by integrating real-time and historical data to develop accurate forecasts for possible tsunamis. Demographic and geographic data were also analyzed by GIS to determine the optimum route to develop evacuation strategies. A set of case studies demonstrates how GIS improves community resilience by supporting informed decision-making. In addition, suggestions are made for how future steeps as the integration of machine learning techniques as emerging tools for analyzing and classifying complex and vast datasets, which may enhance GIS applications in tsunami risk management to improve the accuracy and utility of these tools.

Prediction of Potential Evapotranspiration via Machine Learning and Deep Learning for Sustainable Water Management in the Murat River Basin

Potential evapotranspiration (PET) is a significant factor contributing to water loss in hydrological systems, making it a critical area of research. However, accurately calculating and measuring PET remains challenging due to the limited availability of comprehensive data. This study presents a detailed sustainable model for predicting PET using the Thornthwaite equation, which requires only mean monthly temperature (Tmean) and latitude, with calculations performed using R-Studio. A geographic information system (GIS) was employed to interpolate meteorological data, ensuring coverage of all sub-basins within the Murat River basin, the study area. Additionally, Python libraries were utilized to implement artificial intelligence-driven models, incorporating both machine learning and deep learning techniques. The study harnesses the power of artificial intelligence (AI), applying deep learning through a convolutional neural network (CNN) and machine learning techniques, including support vector machine (SVM) and random forest (RF). The results demonstrate promising performance across the models. For CNN, the coefficient of determination (R2) varied from 96.2 to 98.7%, the mean squared error (MSE) ranged from 0.287 to 0.408, and the root mean squared error (RMSE) was between 0.541 and 0.649. For SVM, the R2 varied from 94.5 to 95.6%, MSE ranged between 0.981 and 1.013, and RMSE ranged from 0.990 to 1.014. RF showed the best performance, achieving an R2 of 100%, MSE values of 0.326 and 0.640, and corresponding RMSE values of 0.571 and 0.800. The climate and topography data used for all algorithms were consistent, and the results indicate that the RF model outperforms the others. Consequently, The RF model’s superior accuracy highlights its potential as a reliable tool for sustainable PET prediction, supporting informed decision-making in water resource planning. By leveraging GIS, AI, and machine learning, this study enhances PET modeling methodologies, addressing critical water management challenges and promoting sustainable hydrological practices in the face of climate change and resource limitations.

Traffic noise prediction model using GIS and ensemble machine learning: a case study at Universiti Teknologi Malaysia (UTM) Campus.

This study represents a pioneering effort to integrate geographic information systems (GIS) and ensemble machine learning methods to predict noise levels on a university campus. Three ensemble models including random forest (RF), gradient boosting (GB), and extreme gradient boosting (XGB) were developed to predict traffic noise based on data collected over a 4-week period at the Universiti Teknologi Malaysia (UTM) campus. Noise measurements were obtained during peak morning hours (7:30 to 9:30 a.m.) on weekdays within the UTM campus in Johor. Additional predictor variables, including data from the digital elevation model (DEM) and land use, were incorporated to capture the complex nonlinear relationships influencing noise levels. The models were optimized through hyperparameter tuning, resulting in high precision, as evidenced by performance metrics such as the coefficient of determination (R2), mean absolute error (MAE), and mean squared error (MSE). The XGB model emerged as the most accurate, with R2 = 0.96, MAE = 0.9, and MSE = 0.3. Noise maps generated using the inverse distance weighting (IDW) interpolation technique highlighted the spatial distribution of noise levels, classified into five classes considering WHO standards. The findings identified distance from roads, the number of light vehicles, and proximity to green areas as the most significant predictors. However, challenges remain in accurately predicting noise levels associated with other predictors. The outcomes of the study indicate the superior performance of the XGB model compared to the GB and RF models. The study recommends several measures to manage and control noise pollution on the UTM campus, including raising awareness, regulating and enforcing vehicle speed limits, reevaluating land use, installing sound insulation systems, and planting trees and vegetation buffer zones around and within educational buildings.

Spatiotemporal dynamics and transformation of the Parana State coastline: A 34-year analysis using RS, GIS, and machine learning

The coastal zone is a critical and vibrant area within coastal cities. Analyzing the changes in the coastal zone of Parana State helps determine the spatiotemporal evolution patterns and driving forces of this region, providing guidance for resource protection and utilization. This study employs remote sensing (RS), Geographic Information System (GIS), and Machine Learning techniques, using Landsat data sets with eight distinct scenes from 1990 to 2024, to extract and analyze the coastline. Specifically, methodologies such as Shoreline Extraction, the Coastline Diversity Index (CDI), and the Coastline Utilization Index (CUI) were applied to assess the coastline's characteristics. The spatiotemporal characteristics of the coastline development in Parana are meticulously examined to ensure accuracy. The findings reveal that: 1) Over the past 34 years, the coastline of Parana State has shown a consistent increasing trend. Natural coastlines have been declining by 45% annually, while artificial coastlines have been increasing by approximately 55% annually. 2) The nature of the shoreline is transitioning from natural to sea-reclamation, engineering-enhanced, and degraded forest. 3) The coastline changes have occurred in three phases: The Built-up Land phase, the Port Petrochemical phase, and the Reducing Coastal Erosion phase. Significant shoreline extension is observed near both biological and ecological coasts. 4) The primary factors influencing the coastline alterations in Parana State include natural forces, human activities, and regional policies. These insights into the spatiotemporal dynamics of the Parana coastline can inform better coastal management and conservation strategies.

Application of GIS and Artificial Intelligence in Military Operations: Prospects and Challenges

Geographic Information System (GIS) and Remote Sensing have been considered significant in the military due to their spatiality in nature. Recent military developments have seen various military institutions depending on spatial mapping tools, for the purpose of command, control, communication and coordination in military operations. In this study, the qualitativeanalytical method was used to illustrate the applications of GIS in military operations, drawing lessons from land based military developments from selected studies. An online survey was conducted to extract information from a sample of forty (40) students undertaking War Studies at the University of Zimbabwe, who were conveniently selected for the study. In-depth interviews were also done with two (2) military experts from the university. Data collected was analyzed using descriptive statistics and qualitative interpretation of recent developments in military GIS. The benefits of GIS and machine learning in military operations included access to information; improved data management; information dissemination; improved terrain analysis; object, target or pattern recognition; quick data processing algorithms; autonomous systems; and predictive analysis. The study also notes some challenges associated with GIS and machine learning in military operations include expensive to set up which constituted of respondents; training requirements; cyber-security; software issues; offensive in nature, as well as misinterpretation of situations. The study recommends the need for human-AI collaboration and the development of a regulatory framework at national, regional and international level in ensuring sustainable implementation of GIS technology in military operations.

Automated building damage assessment and large‐scale mapping by integrating satellite imagery, GIS, and deep learning

AbstractEfficient and accurate building damage assessment is crucial for effective emergency response and resource allocation following natural hazards. However, traditional methods are often time consuming and labor intensive. Recent advancements in remote sensing and artificial intelligence (AI) have made it possible to automate the damage assessment process, and previous studies have made notable progress in machine learning classification. However, the application in postdisaster emergency response requires an end‐to‐end model that starts with satellite imagery as input and automates the generation of large‐scale damage maps as output, which was rarely the focus of previous studies. Addressing this gap, this study integrates satellite imagery, Geographic Information Systems (GIS), and deep learning. This enables the creation of comprehensive, large‐scale building damage assessment maps, providing valuable insights into the extent and spatial variation of damage. The effectiveness of this methodology is demonstrated in Galveston County following Hurricane Ike, where the classification of a large ensemble of buildings was automated using deep learning models trained on the xBD data set. The results showed that utilizing GIS can automate the extraction of subimages with high accuracy, while fine‐tuning can enhance the robustness of the damage classification to generate highly accurate large‐scale damage maps. Those damage maps were validated against historical reports.

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.

Exploring the Spatial Effects of Built Environment on Quality of Life Related Transportation by Integrating GIS and Deep Learning Approaches

Understanding the quality of life related to transportation plays a crucial role in enhancing commuters’ quality of life, particularly in daily trips. This study explores the spatial effects of built environment on quality of life related to transportation (QoLT) through the combination of GIS application and deep learning based on a questionnaire survey by focusing on a case study in Sukhumvit district, Bangkok, Thailand. The Geographic Information System (GIS) was applied for spatial analysis and visualization among all variables through a grid cell (500 × 500 sq.m.). In regard to deep learning, the semantic segmentation process that the model used in this research was OCRNet, and the selected backbone was HRNet_W48. A quality-of-life-related transportation indicator (life satisfaction) was implemented through 500 face-to-face interviews and the data were collected by a questionnaire survey. Then, multinomial regression analysis was performed to demonstrate the significant in positive and negative aspects of independent variables (built environment) with QoLT variables at a 0.05 level of statistical significance. The results revealed the individuals’ satisfaction from a diverse group of people in distinct areas or environments who consequently perceived QoLT differently. Built environmental factors were gathered by application of GIS and deep learning, which provided a number of data sets to describe the clusters of physical scene characteristics related to QoLT. The perception of commuters could be translated to different clusters of the physical attributes through the indicated satisfaction level of QoLT. The findings are consistent with the physical characteristics of each typological site context, allowing for an understanding of differences in accessibility to transport systems, including safety and cost of transport. In conclusion, these findings highlight essential aspects of urban planning and transport systems that must consider discrepancies of physical characteristics in terms of social and economic needs from a holistic viewpoint. A better understanding of QoLT adds important value for transportation development to balance the social, economic, and environmental levels toward sustainable futures.

Teaching social determinants through geographic information system mapping.

Many institutions in undergraduate medical education have developed unique curricula to teach social determinants of health (SDOH). Geographic information system (GIS) mapping is one tool that learners could use to understand our built environment and its correlation with health outcomes through data analysis, visualization and active learning. At the University of North Carolina School of Medicine, medical students participate in a 4-year longitudinal curriculum on social and health systems science with the final year dedicated to self-directed learning. This final year course incorporates a GIS-based online module to help students apply their understanding of the health impacts of SDOH. Students create online maps with simulated patient data and identify 'hotspots' with map overlays using ArcGIS software. Students write reflections on their maps based on the implications of SDOH. Thematic analysis of these reflections identified patterns within the narrative data. From March 2020 to February 2021, 148 fourth-year medical students participated in the GIS learning module. Five major themes were identified: Explored Social Determinant Topics, Inclusion of Geo-mapping in Curriculum, Utility of Geo-mapping in Healthcare, Future Application of ArcGIS for Personal Use, Impressions of ArcGIS Software. Students showed engagement and interest in the exercise, and responses were overall positive. Responses showed understanding of the application of ArcGIS and demonstrated knowledge of social determinants of health. A self-directed, active learning online module using GIS mapping offers a generally popular, eye-opening and unique method for teaching SDOH in undergraduate medical education.

A Hybrid Approach for Mass Valuation of Residential Properties through Geographic Information Systems and Machine Learning Integration

Geographic Information Systems (GIS) and Machine Learning methods are now widely used in mass property valuation using the physical attributes of properties. However, locational criteria, such as as proximity to important places, sea or forest views, flat topography are just some of the spatial factors that affect property values and, to date, these have been insufficiently used as part of the valuation process. In this study, a hybrid approach is developed by integrating GIS and Machine Learning for mass valuation of residential properties. GIS‐based Nominal Valuation Method was applied to carry out proximity, terrain, and visibility analyses using Ordnance Survey and OpenStreetMap data, than land value map of Great Britain was produced. Spatial criteria scores obtained from the GIS analyses were included in the price prediction process in which global and spatially clustered local regression models are built for England and Wales using Price Paid Data and Energy Performance Certificates data. Results showed that adding locational factors to the property price data and applying a novel nominally weighted spatial clustering algorithm for creating a local regression increased the prediction accuracy by about 45%. It also demonstrated that Random Forest was the most accurate ensemble model.

Research into the Optimal Regulation of the Groundwater Table and Quality in the Southern Plain of Beijing Using Geographic Information Systems Data and Machine Learning Algorithms

The purpose of this paper is to provide new ideas and methods for the sustainable use of groundwater in areas with serious groundwater overexploitation and serious groundwater pollution. Geographic information systems (GIS) were combined with machine learning algorithms, water resources optimization technology, and groundwater numerical simulation to optimize the regulation of the groundwater table and quality beneath the Daxing District in the southern plain of Beijing. By collecting local consumption and supply data and observations of the groundwater table and quality in the connected aquifer beneath Daxing for the years 2006–2020, the corresponding water demands and groundwater impact were extrapolated for the years 2021–2025 based on the basis of the existing development model. Through the combination of GIS and machine learning algorithms, the NO3-N concentration of local groundwater monitoring points in wet years, normal years, and dry years were predicted. With respect to NO3-N pollution, three new groundwater exploitation regimes were devised, which we numbered 1 to 3. The optimal allocation of water resources was then calculated for wet year, typical year, and dry year scenarios for the year 2025. By comparing the water shortage, groundwater utilization rate, and NO3-N pollution under the new groundwater exploitation regimes, the optimal groundwater exploitation mode for the three different types of hydrological year was determined. The results indicate that NO3-N pollution was greatly reduced after the adoption of the optimal regimes and that the groundwater table demonstrated rapid recovery. These results can be of great help in realizing the management, supervision, and regulation of groundwater by combining GIS with machine learning algorithms.

Interrogating the Installation Gap and Potential of Solar Photovoltaic Systems Using GIS and Deep Learning

Non-renewable-resource consumption and global greenhouse-gas (GHG) emissions are critical issues that pose a significant threat to sustainable development. Solar energy is a promising source to generate renewable energy and an appealing alternative electricity source for households. The primary goal of this research is to detect the rooftops that have no solar photovoltaic (PV) system deployed on them but that receive moderate to high solar-energy radiation using the Geographic Information System (GIS) and deep-learning techniques. Although various studies have been conducted on this subject, not many addressed these two issues simultaneously at a residential level. Identifying the installed solar PV systems in a large area can be expensive and time-consuming work if performed manually. Therefore, the deep-learning algorithm is an emerging alternative method to detect objects using aerial images. We employed the Single-Shot-Detector (SSD) model with the backbone of residual neural network 34 (ResNet34) to detect the solar PV systems and used GIS software to compute solar isolation and calculate the electricity production estimate (EPE) of each rooftop. Our results show that the SSD model detected 6010 solar panels on 4150 properties with an accuracy of 78% and observed that there were 176 Statistical Area 1s (SA1s) that had no rooftops with solar PV systems installed. Moreover, the total electricity production from the suitable area was estimated at over 929.8 Giga Watt-hours (GWhs) annually. Finally, the relation between solar-PV-system density and EPE was also identified using the bivariant correlation technique. Detecting the existing solar PV systems is useful in a broad range of applications including electricity-generation prediction, power-plant-production management, uncovering patterns between regions, etc. Examination of the spatial distribution of solar-energy potential in a region and performing an overlay analysis with socio-economic factors can help policymakers to understand the explanation behind the pattern and strategize the incentives accordingly.

Effectiveness of learning materials implementation for spatial thinking ability in geographic information systems (GIS) course

Geography education is realized in learning that combines the study of physical and human geography in a spatial context. GIS Learning in universities is directed to be able to equip students in the use of spatial information which must be accompanied by the ability to manage it cognitively. This study aimed to assess the effectiveness of Spatial Thinking Ability learning materials development. The model used in this research and development study was the Dick & Carey model. The field trial was carried out by experimental research using a Quasi Experiment model. The trial design was Post-test Only, Non-Equivalent Control Group Design. The trial was carried out on sixth-semester undergraduate students at Muhammadiyah Surakarta who had taken a GIS course with as many as 41 students. The activity took place in March 2018. In this study, some students were given treatment in spatial thinking ability learning. The result shows a U value of 56 and a W value of 209. When converted to a Z value, the value is -3.943. Sig value or P-Value of 0.000 <0.05.

Merging GIS and Machine Learning Techniques: A Paper Review

GIS (Geographic Information Systems) data showcase locations of earth observations or features, their associated attributes and spatial relationships that exist between such observations. Analysis of GIS data varies widely and may include some modeling and predictions which are usually computing-intensive and complicated, especially, when large datasets are involved. With advancement in computing technologies, techniques such as Machine learning (ML) are being suggested as a potential game changer in the analysis of GIS data because of their comparative speed, accuracy, automation, and repeatability. Perhaps, the greatest benefit of using both GIS and ML is the ability to transfer results from one database to another. GIS and ML tools have been used extensively in medicine, urban development, and environmental modeling such as landslide susceptibility prediction (LSP). There is also the problem of data loss during conversion between GIS systems in medicine, while in geotechnical areas such as erosion and flood prediction, lack of data and variability in soil has limited the use of GIS and ML techniques. This paper gives an overview of the current ML methods that have been incorporated into the spatial analysis of data obtained from GIS tools for LSP, health, and urban development. The use of Supervised Machine Learning (SML) algorithms such as decision trees, SVM, KNN, and perceptron including Unsupervised Machine Learning algorithms such as k-means, elbow algorithms, and hierarchal algorithm have been discussed. Their benefits, as well as their shortcomings as studied by several researchers have been elucidated in this review. Finally, this review also discusses future optimization techniques.

Mapping the spatial distribution of the dengue vector Aedes aegypti and predicting its abundance in northeastern Thailand using machine-learning approach

BackgroundMapping the spatial distribution of the dengue vector Aedes (Ae.) aegypti and accurately predicting its abundance are crucial for designing effective vector control strategies and early warning tools for dengue epidemic prevention. Socio-ecological and landscape factors influence Ae. aegypti abundance. Therefore, we aimed to map the spatial distribution of female adult Ae. aegypti and predict its abundance in northeastern Thailand based on socioeconomic, climate change, and dengue knowledge, attitude and practices (KAP) and/or landscape factors using machine learning (ML)-based system. MethodA total of 1066 females adult Ae. aegypti were collected from four villages in northeastern Thailand during January–December 2019. Information on household socioeconomics, KAP regarding climate change and dengue, and satellite-based landscape data were also acquired. Geographic information systems (GIS) were used to map the household-based spatial distribution of female adult Ae. aegypti abundance (high/low). Five popular supervised learning models, logistic regression (LR), support vector machine (SVM), k-nearest neighbor (kNN), artificial neural network (ANN), and random forest (RF), were used to predict females adult Ae. aegypti abundance (high/low). The predictive accuracy of each modeling technique was calculated and evaluated. Important variables for predicting female adult Ae. aegypti abundance were also identified using the best-fitted model. ResultsUrban areas had higher abundance of female adult Ae. aegypti compared to rural areas. Overall, study respondents in both urban and rural areas had inadequate KAP regarding climate change and dengue. The average landscape factors per household in urban areas were rice crop (47.4%), natural tree cover (17.8%), built-up area (13.2%), permanent wetlands (21.2%), and rubber plantation (0%), and the corresponding figures for rural areas were 12.1, 2.0, 38.7, 40.1 and 0.1% respectively. Among all assessed models, RF showed the best prediction performance (socioeconomics: area under curve, AUC = 0.93, classification accuracy, CA = 0.86, F1 score = 0.85; KAP: AUC = 0.95, CA = 0.92, F1 = 0.90; landscape: AUC = 0.96, CA = 0.89, F1 = 0.87) for female adult Ae. aegypti abundance. The combined influences of all factors further improved the predictive accuracy in RF model (socioeconomics + KAP + landscape: AUC = 0.99, CA = 0.96 and F1 = 0.95). Dengue prevention practices were shown to be the most important predictor in the RF model for female adult Ae. aegypti abundance in northeastern Thailand. ConclusionThe RF model is more suitable for the prediction of Ae. aegypti abundance in northeastern Thailand. Our study exemplifies that the application of GIS and machine learning systems has significant potential for understanding the spatial distribution of dengue vectors and predicting its abundance. The study findings might help optimize vector control strategies, future mosquito suppression, prediction and control strategies of epidemic arboviral diseases (dengue, chikungunya, and Zika). Such strategies can be incorporated into One Health approaches applying transdisciplinary approaches considering human-vector and agro-environmental interrelationships.

A Modelling Tool for Rainfall-triggered Landslide Susceptibility Mapping and Hazard Warning based on GIS and Machine Learning

The landslide susceptibility mapping and hazard warning are widely adopted tools by the government, stakeholders and the public for landslide disaster preparedness and emergency planning. This study presented a modelling tool based on geographic information system (GIS) and machine learning to aid the two-step modelling procedure. The machine learning methods including artificial neural networks, support vector machines, and logistic regression were integrated into the GIS environment for modelling landslide susceptibility to simplify and automate the routines of model training, verification and prediction. Then, the meta-element model was employed to take the landslide susceptibility, antecedent effective rainfall and 24-hour forecasted rainfall as inputs to determine the landslide hazard level. The architecture to deploy the established meta-element model for real-time landslide hazard warning was also proposed. A study case in Chunan, China was selected to demonstrate the applicability of the modelling tool to aid landslide susceptibility mapping and real-time hazard warning in response to a typhoon event. The developed modelling tool was desired to evolve into cloud computing architecture to facilitate easy-to-reuse and uplift its scalability.

GIS Distance Learning during the COVID-19 Pandemic (Students’ Perception)

The paper deals with distance learning of geographic information systems (GIS) at the Department of Geography and Regional Development FNS CPU in Nitra using various educational materials and the JitsiMeet application during the COVID-19 pandemic. Students of bachelor study had GIS lessons in face-to-face instruction in a computer room for half of a semester. In the middle of the semester, the COVID-19 pandemic broke out in Slovakia and the face-to-face instruction had to be reoriented to the distance form of learning. The first month of distance learning took place with the help of textbooks and video tutorials, which were sent to students. The task of the students that month was to study a new subject, then students were sent a questionnaire where they had to express their opinion on learning GIS using video tutorials and textbooks, but also indicate whether they had facilities for online learning (PC, audio and video outputs, Internet) as well as to express their views and experiences with online learning as such. Although after evaluating the questionnaire, up to 71% of students answered that they had learned a new subject from the video tutorials and 57% also from GIS textbooks, several percent of students were still unfit for this form of learning. That is why we went to lessons with the free JitsyMeet app, which allows for video conferencing, screen sharing, chat, and more. The aim of the paper is to point out the perception of distance GIS learning by university students during the COVID-19 pandemic.