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Abstract Urban gas pipelines pose significant risks to public safety and infrastructure integrity, necessitating thorough risk assessment methodologies to mitigate potential hazards. This study investigates the dynamics of population distribution, demographic characteristics, and building structures to assess the risk associated with gas pipelines. Using geospatial analysis techniques, we analyze population distribution patterns during both day and night periods. Additionally, we conduct an in-depth vulnerability assessment considering multiple criteria maps, highlighting areas of heightened vulnerability in proximity to gas pipelines and older buildings. This study incorporated the concept of individual risk and the intrinsic parameters of gas pipelines to develop a hazard map. Hazard analysis identifies areas with elevated risks, particularly around main pipeline intersections and high-pressure zones. Integrating hazard and vulnerability assessments, we generate risk maps for both day and night periods, providing valuable insights into spatial risk distribution dynamics. The findings underscore the importance of considering temporal variations in risk assessment and integrating demographic and structural factors into hazard analysis for informed decision-making in pipeline management and safety measures.

This study addresses the critical need for comprehensive multi-pollution modeling in urban environments by employing Geospatial Artificial Intelligence (GeoAI) techniques. Specifically, it aims to elucidate the variability of air and noise pollution levels in Tehran, Iran, and develop precise multi-pollution susceptibility maps. A spatial database was compiled, encompassing annual average data (2019–2022) of the Air Quality Index (AQI) and Equivalent Continuous Sound Level (Leq), along with 19 influential factors related to both pollutants. The study utilized the CatBoost machine learning algorithm, enhanced with Grey Wolf Optimizer (GWO) and Harris Hawks Optimization (HHO) algorithms, to model and predict pollution patterns. Key quantitative achievements include a remarkable 15% reduction in Root Mean Square Error (RMSE) for air pollution and a 12% reduction in noise pollution when using the CatBoost-HHO configuration compared to the standalone CatBoost algorithm. Validation of the multi-pollution maps utilizing the Area Under the Curve (AUC) analysis showed high accuracy, with the CatBoost-HHO achieving AUC values of 0.943 for air pollution and 0.936 for noise pollution, outperforming CatBoost-GWO and standalone CatBoost models. These results underscore the efficacy of our approach in accurately identifying and mapping multi-pollution hotspots, contributing valuable insights for urban environmental management and public health preservation.

ABSTRACTThis study focuses on determining the optimal distance metric in the K-Nearest Neighbors (KNN) algorithm for spatial modelling of floods. Four distance metrics of the KNN algorithm, namely KNN-Manhattan, KNN-Minkowski, KNN-Euclidean, and KNN-Chebyshev, were utilized for flood susceptibility mapping (FSM) in Estahban, Iran. A spatial database comprising 509 flood occurrence points extracted from satellite images and 12 factors influencing floods was created for analysis. The particle swarm optimization (PSO) algorithm was employed for hyperparameter optimization and feature selection, considering eight influential factors as modelling inputs. The modelling results revealed that the KNN-Manhattan algorithm exhibited superior accuracy (root mean squared error (RMSE) = 0.169, mean absolute error (MAE) = 0.051, coefficient of determination (R2) = 0.884, and area under the curve (AUC) = 0.94) compared with the other algorithms for identifying flood-prone areas. The KNN-Minkowski algorithm followed closely, with an RMSE of 0.175, MAE of 0.056, R2 of 0.876, and AUC of 0.939. The KNN-Euclidean algorithm achieved an RMSE of 0.183, MAE of 0.061, R2 of 0.842, and AUC of 0.929, whereas the KNN-Chebyshev algorithm achieved an RMSE of 0.198, MAE of 0.075, R2 of 0.842, and AUC of 0.924.

Background Bluetooth low energy (BLE)-based contact-tracing applications were widely used during the COVID-19 pandemic. However, the use of only the received signal strength feature for proximity calculations may not be adaptable to different virus variants or scalable for other potential epidemic diseases. Objective This study presents a novel framework in regard to evaluating and classifying personal exposure risk that considers both contact features, which include distance and length of contact, and environment features, which include crowd size and the number of recently infected cases in the environment. The framework utilizes a fuzzy expert system that is adaptable to different virus variants. Methods The proposed method was tested on two viruses with different close contact features, which used four membership functions and 256 fuzzy rule sets. Results The proposed framework classified personal exposure risks into four classes, which include low, medium, high, and too high risk. The empirical results showed that the fuzzy logic-based approach reduced the number of false positive cases and demonstrated better accuracy and precision than the current BLE-only approaches. Conclusions The proposed framework provides a more practical and adaptable method in regard to assessing exposure risks in real-world scenarios. It has the potential to be scalable and adaptable to different virus variants and other potential epidemic diseases by considering both contact and environment features. These findings may be useful in order to develop more effective digital contact-tracing applications and policies.

The importance of knowledge organization and information retrieval techniques has been evident throughout human history, becoming even more crucial in the digital age. While computer systems and the web have facilitated information retrieval, challenges arose with the increasing volume of data. The introduction of Semantic Web technologies aimed to enhance precision and accuracy by converting the web into a structured data format. The Cultural Heritage (CH) community has been at the forefront of adopting Semantic Web practices to promote interoperability and shared understanding. In this study, we present a comprehensive conceptual framework that spans cultural heritage, information modeling, and information retrieval. Our model addresses early solutions in knowledge organization systems, highlighting the evolution from classification systems and controlled vocabularies to the significance of metadata schemas. We delve into the limitations of traditional knowledge organization systems and the necessity of formal ontologies, particularly in the cultural heritage domain. The comparative analysis of CRM vs. EDM, ontology-based metadata interoperability, and ontology technologies elucidate our contributions to the field. This paper outlines the process from the initial steps of adopting Semantic Web technologies in the CH domain to the latest developments in CH information retrieval. In this paper, we also reviewed intelligent applications and services developed in the CH domain after establishing semantic data models and Knowledge Organization Systems. Finally, challenges and possible future research directions are discussed. The findings revealed that GLAMs (Galleries, Libraries, Archives, and Museums) are excellent and comprehensive sources of CH information. The CH community has put in a lot of time and effort to develop data models and knowledge organization tools; now it’s time to use this valuable resource to construct smart applications that are still in their early phases. This could benefit the CH industry even more.

Abstract Wireless capsule endoscopy (WCE) enables imaging and diagnostics of the gastrointestinal (GI) tract to be performed without any discomfort. Despite this, several characteristics, including efficacy, tolerance, safety, and performance, make it difficult to apply and modify widely. The use of automated WCE to collect data and perform the analysis is essential for finding anomalies. Medical specialists need a significant amount of time and expertise to examine the data generated by WCE imaging of the patient’s digestive tract. To address these challenges, several computer vision-based solutions have been designed; nevertheless, they do not achieve an acceptable level of accuracy, and more advancements are required. Thus, in this study, we proposed four multi-classification deep learning (DL) models i.e., Vgg-19 + CNN, ResNet152V2, Gated Recurrent Unit (GRU) + ResNet152V2, and ResNet152V2 + Bidirectional GRU (Bi-GRU) and applied it on different publicly available databases for diagnosing ulcerative colitis, polyps, and dyed-lifted polyps using WCE images. To our knowledge, this is the only study that uses a single DL model for the classification of three different GI diseases. We compared the classification performance of the proposed DL classifiers in terms of many parameters such as accuracy, loss, Matthew's correlation coefficient (MCC), recall, precision, negative predictive value (NPV), positive predictive value (PPV), and F1-score. The results revealed that the Vgg-19 + CNN outperforms the three other proposed DL models in classifying GI diseases using WCE images. The Vgg-19 + CNN model achieved an accuracy of 99.45%. The results of four proposed DL classifiers are also compared with recent state-of-the-art classifiers and the proposed Vgg-19 + CNN model has performed better in terms of improved accuracy.

Accurate and rapid plant disease detection is critical for enhancing long-term agricultural yield. Disease infection poses the most significant challenge in crop production, potentially leading to economic losses. Viruses, fungi, bacteria, and other infectious organisms can affect numerous plant parts, including roots, stems, and leaves. Traditional techniques for plant disease detection are time-consuming, require expertise, and are resource-intensive. Therefore, automated leaf disease diagnosis using artificial intelligence (AI) with Internet of Things (IoT) sensors methodologies are considered for the analysis and detection. This research examines four crop diseases: tomato, chilli, potato, and cucumber. It also highlights the most prevalent diseases and infections in these four types of vegetables, along with their symptoms. This review provides detailed predetermined steps to predict plant diseases using AI. Predetermined steps include image acquisition, preprocessing, segmentation, feature selection, and classification. Machine learning (ML) and deep understanding (DL) detection models are discussed. A comprehensive examination of various existing ML and DL-based studies to detect the disease of the following four crops is discussed, including the datasets used to evaluate these studies. We also provided the list of plant disease detection datasets. Finally, different ML and DL application problems are identified and discussed, along with future research prospects, by combining AI with IoT platforms like smart drones for field-based disease detection and monitoring. This work will help other practitioners in surveying different plant disease detection strategies and the limits of present systems.

ABSTRACTMost countries and territories worldwide are affected by coronavirus disease 2019 (COVID-19), and some cities have become known as epicenters owing to high outbreaks. Because of the changeable and unknown nature of the virus, managers of different cities could learn from the experiences of cities that have been successful in controlling COVID-19 instead of wasting time exploring different methods. It would be even more beneficial if they analyzed the experiences of similar cities. The similarity of such cities could be examined within a geographic information system based on various criteria. This study investigated the similarities among eight cities – Wuhan, Tehran, Bergamo, Madrid, Paris, Daegu, New York, and Berlin – in terms of the COVID-19 situation (target) in these locations based on proximity factors, weather, and demographic criteria. First, the factor and target layers were prepared, and then similar cities were identified using a similarity model and different distance metrics. The results were aggregated using the Copeland method because of the different outcomes for each metric. The most similar city was identified for each selected city, and its similarity level was determined based on these criteria. The results suggested the following pairs of similar cities: Wuhan–Berlin, Tehran–Berlin, Daegu–Wuhan, Bergamo–Madrid, Paris–Madrid, and New York–Paris based on COVID-19 related data up to 15 April 2020 (target T1), and Daegu–Wuhan, Tehran–Madrid, Bergamo–Paris, Berlin–Paris, and New York–Madrid up to 8 December 2021 (target T2) with a minimum and maximum similarity rate of 82.85% and 92.36%, respectively. For similar cities, the most similar factors among the proximity criteria are the distance from bus and metro stations; among weather, the criteria are humidity and pressure; and among demographics, the criteria are male and female population ratios, literacy ratio, and death ratio from asthma and cancer, with a minimum and maximum difference of 0% and 64.94%, respectively. In addition, according to the random forests ranking results (with root mean squared error = 0.23), temperature, distance from the bank, and gender were the most important criteria for the eight studied cities. Identifying these important factors helps to determine hotspots or places of future outbreaks to choose control strategies according to the cultural and ecological conditions of each city.

This research aimed to predict soil’s physical and chemical properties with a state-of-the-art hybrid model based on deep learning algorithms and optical satellite images in a region in the north of Iran. As dependent data, 317 soil samples (0–30 cm) were collected in field surveying and analyzed by the soil and water research institute for their physical (clay, silt, and sand) and chemical [electrical conductivity (EC), organic carbon (OC), phosphorus (P), soil reaction (pH), and potassium (K)] properties. Based on independent data, 23 remote sensing (RS) parameters (extracted from Landsat 8 optical images), 17 topographical parameters [extracted from the digital elevation model (DEM)], and four climatic parameters (derived from the meteorological organization). Spatial prediction of physical and chemical properties was implemented using a convolutional neural network (CNN), recurrent neural network (RNN), and hybrid CNN-RNN models. The evaluation results indicated that the hybrid CNN-RNN model had higher accuracy in all soil properties, followed by the RNN and CNN models. In the hybrid CNN-RNN model, pH (0.0206), EC (0.0958 dS/m), silt (0.0996%), P (0.1078 ppm), K (0.1185 ppm), sand (0.1360%), OC (0.1361%), and clay (0.1419%) had higher prediction accuracy, as determined by the root mean-squared error (RMSE) index. The hybrid CNN-RNN model proved to be the most effective for soil property prediction in this region. This finding underscores the potential of deep learning techniques in harnessing RS data for precise soil property mapping, with implications for land management and agricultural practices.

Rainfall monsoons and the resulting flooding have always been cataclysmic disasters that have heightened global concerns in light of climate change. Flood susceptibility modeling is an indirect method for reducing flood disaster losses. This study aimed to improve flood susceptibility modeling by developing a sequential ensemble (extreme gradient boosting (XGBoost)) model utilizing three swarm-based algorithms (bacterial foraging optimization (BFO), cuckoo search (CS), and artificial bee colony (ABC) algorithms). Initially, an integration of optical (Landsat-8) and radar (Sentinel-1) satellite images were used to monitor the flooded areas during the July 2022 monsoon in the Kazerun region, Iran. A total of 1358 flood occurrence points were considered from the monitored flood areas; 70% (952 points) and 30% (406 points) were used for modeling and evaluating the models, respectively. Based on flood points and thirteen spatial criteria influencing floods, four flood models ((XGBoost, XGBoost-ABC, XGBoost-BFO, and XGBoost-CS)) were used to generate a flood susceptibility map (FSM). According to the results, the XGBoost-CS (area under the curve (AUC) = 0.96), XGBoost-BFO (AUC = 0.953), XGBoost-ABC (AUC = 0.941), and XGBoost (AUC = 0.939) models have greater accuracy in flood susceptibility modeling, respectively. The results indicated that the models coupled with the three metaheuristic algorithms (XGBoost-ABC, XGBoost-BFO, and XGBoost-CS) exhibited higher flood susceptibility modeling accuracy than the standalone XGBoost model.

The advent of human emotion data availability signifies a pivotal advancement, propelling intelligent systems to elevate human-technology interaction. The fusion of emotions with pioneering technologies such as artificial intelligence (AI), geospatial information systems (GIS), and extended reality (XR) heralds a groundbreaking era of emotion-driven intelligent systems, poised to revolutionize urban applications. In this context, our research carves a distinctive niche by engaging in a comprehensive review of pertinent literature, culminating in the formulation of an incisive 3-layer framework. Encompassing the bedrock of emotion analysis, seamless technology integration, intricate privacy considerations, and pragmatic implementation, this framework serves as a beacon for future endeavors. Most notably, we introduce the trailblazing concept of emotion-intelligent systems, an outcome of the harmonious blend of emotion data and pioneering technologies. Our research navigates the intricate terrain of cultivating emotionally sentient intelligent systems, deftly addressing complexities and surmounting challenges to forge a path towards a more emotionally attuned generation. With transformative implications, these avant-garde systems stand poised to reshape human-technology interfaces, thereby beckoning novel avenues for future exploration.

Abstract Cultural heritage (CH) reflects on the history of a society and its traditions and it is treated as the nation’s memory and identity. Digitizing and web, beside its benefits, brought some challenges in disseminating and retrieving CH information, which has heterogeneous content varying widely in type and properties yet encompassing rich semantic links. Semantic web technologies, especially ontologies, provide a common understanding inside a domain that helps sharing knowledge and interoperability. They can be very helpful in data modeling for a better information retrieval compared to relational databases as they take into account the semantics of information, guarantee reusability, and make information machine-readable that can offer more flexibility to intelligent services and applications. CH community is one of the first domains to make use semantic web technologies to deal with this issue. CIDOC CRM is the most used and famous ontology in CH domain, which is an ISO standard since 2006. Heritage sites are composed of many points of interest that attract visitors to find out about them. However, information about a particular POI is complex and interconnected with other people, events, and objects. In this paper, we aim to develop a POI-based data model for heritage sites in Iran using concepts from CIDOC CRM integrated with GeoSPARQL, the standard ontology in geospatial field, to incorporate spatial semantics with heritage information. This way the user can freely explore their preferred information about the places they desire. This can make it possible to use the data model for location-based services and applications in heritage sites.

In today’s world, technology has assumed an indispensable role in everyday life. However, though developments in the Internet of Things (IoT) have laid a foundation for achieving different educational purposes, IoT is not being employed to its full potential. A separate issue is that the topic of geometry has been neglected or received only limited attention in schools, particularly in the early years. Moreover, as geometry is of paramount importance, the idea of teaching geometry in early childhood should be investigated. This paper proposes an IoT-based approach for teaching basic geometric shapes to children aged 5 to 6. Specifically, five nodes were designed to act as the vertices of shapes. In addition, educational videos comprising three sections —identification, recognition, and recollection — were provided. Using a sample of 35 boys the proposed solution was assessed in terms of its effect on learning outcomes and the learning process using pre-test, simultaneous test, and post-tests. The findings revealed a noticeable improvement in learning outcomes. Furthermore, the proposed approach made learning enjoyable, which, compared to the traditional approach toward learning geometric shapes, is considered an improvement.

Education is a vital part of the development of society and it is changing over time in terms of methods, content, concepts, and models. Recently, it has been increasingly prevalent to benefit from the potentialities of Artificial Intelligence (AI) in addressing educational issues. In this research, the current state of the art of the integration of AI in K-12 education was provided. Specifically, different parts of education in which AI was employed along with the related AI categories were discussed according to different K-12 grades and courses. Additionally, technologies and environments that contributed to employing AI in education were discussed. To this end, a systematic literature review was conducted on articles and conference papers published between 2011 and 2021 in the Web of Science and Scopus databases. As the result of the initial search, 2075 documents were extracted and based on inclusive criteria and 210 documents were identified for further investigation. AI applications were categorized into Student performance, Teaching, Selection, and Behavior tasks, and Other. Machine Learning (ML) and Intelligent Tutoring System (ITS) were the most common approaches among AI categories. Furthermore, high school-related applications were more frequent and STEM courses were substantially targeted by AI. In conclusion, the remarkable impact of AI on education was concluded. The current study reveals information about the potentialities offered by AI in K-12 education which aids researchers in implementing AI-based education systems. As for future works, other databases such as ACM library and Google Scholar can be investigated as well. Furthermore, exploring the 95 papers that were excluded due to inaccessibility to their full texts can be taken into account. Finally, the papers can be also investigated in terms of pedagogical approaches or development tools.

Cutaneous leishmaniasis is a complex infection that is caused by different species of Leishmania and affects more than 2 million people in 88 countries. Identifying the environmental factors affecting the occurrence of cutaneous leishmaniasis and preparing a risk map is one of the basic tools to control and manage this disease. The aim of this study was a spatial prediction of cutaneous leishmaniasis in Isfahan province, Iran using three machine learning algorithms (decision tree (DT), support vector regression (SVR), and linear regression (LR)). The spatial database was created using data collected on the number of diseases in Isfahan province from 2011 to 2018, as well as ten environmental parameters (temperature, humidity, rainfall, altitude, slope, wind speed, normalized difference vegetation index (NDVI), number of sunny days, number of frosty days, and distance to stream) that affect the incidence of leishmaniasis. Furthermore, the fuzzy method was employed in this study to reduce uncertainty and evaluate the effect of environmental factors on disease prevalence. Using the holdout method and 70:30 ratios, the data were used to model and prepare a leishmaniasis prediction map and evaluate the results, respectively. The accuracy of the maps satisfied with the DT, SVR, and LR algorithms was 0.951, 0.934, and 0.914, respectively, according to the receiver operating characteristic (ROC) curve and area under the curve (AUC). Furthermore, the eastern and southern parts of the province have the lowest risk of leishmaniasis. The result of this issue is the identification of high-risk areas of the disease and increase life and peace for people in the community.

Recurring wildfires pose a critical global issue as they undermine social and economic stability and jeopardize human lives. To effectively manage disasters and bolster community resilience, the development of wildfire susceptibility maps (WFSMs) has emerged as a crucial undertaking in recent years. In this research endeavor, two deep learning algorithms were leveraged to generate WFSMs using two distinct remote sensing datasets. Specifically, the Moderate-Resolution Imaging Spectroradiometer (MODIS) and Landsat-8 images were utilized to monitor wildfires that transpired during the year 2021. To develop an effective WFSM, two datasets were created by incorporating 599 wildfire locations with Landsat-8 images and 232 sites with MODIS images, as well as twelve factors influencing wildfires. Deep learning algorithms, namely the long short-term memory (LSTM) and recurrent neural network (RNN), were utilized to model wildfire susceptibility using the two datasets. Subsequently, four WFSMs were generated using the LSTM (MODIS), LSTM (Landsat-8), RNN (MODIS), and RNN (Landsat-8) algorithms. The evaluation of the WFSMs was performed using the area under the receiver operating characteristic (ROC) curve (AUC) index. The results revealed that the RNN (MODIS) (AUC = 0.971), RNN (Landsat-8) (AUC = 0.966), LSTM (MODIS) (AUC = 0.964), and LSTM (Landsat-8) (AUC = 0.941) algorithms demonstrated the highest modeling accuracy, respectively. Moreover, the Gini index was employed to assess the impact of the twelve factors on wildfires in the study area. The results of the random forest (RF) algorithm indicated that temperature, wind speed, slope, and topographic wetness index (TWI) parameters had a significant effect on wildfires in the study region. These findings are instrumental in facilitating efficient wildfire management and enhancing community resilience against the detrimental effects of wildfires.

Sea surface temperature (SST) is crucial in ocean research and marine activities. It makes predicting SST of paramount importance. While SST is highly affected by different oceanic, atmospheric, and climatic parameters, few papers have investigated time-series SST prediction based on multiple features. This paper utilized multi features of air pressure, water temperature, wind direction, and wind speed for time-series hourly SST prediction using deep neural networks of convolutional neural network (CNN), long short-term memory (LSTM), and CNN–LSTM. Models were trained and validated by different epochs, and feature importance was evaluated by the leave-one-feature-out method. Air pressure and water temperature were significantly more important than wind direction and wind speed. Accordingly, feature selection is an essential step for time-series SST prediction. Findings also revealed that all models performed well with low prediction errors, and increasing the epochs did not necessarily improve the modeling. While all models were similarly practical, CNN was considered the most suitable as its training speed was several times faster than the other two models. With all this, the low variance of time-series data helped models make accurate predictions, and the proposed method may have higher errors while working with more variant features.

Abstract Nowadays, owing to population growth, increasing environmental pollution, and lifestyle changes, the number of asthmatics has significantly increased. Therefore, the purpose of our study was to determine the asthma-prone areas in Tehran, Iran considering environmental, spatial factors. Initially, we built a spatial database using 872 locations of children with asthma and 13 environmental factors affecting the disease—distance to parks and streets, rainfall, temperature, humidity, pressure, wind speed, particulate matter (PM 10 and PM 2.5), ozone (O3), sulfur dioxide (SO2), carbon monoxide (CO), and nitrogen dioxide (NO2). Subsequently, utilizing this spatial database, a random forest (RF) machine learning model, and a geographic information system, we prepared a map of asthma-prone areas. For modeling and validation, we deployed 70% and 30%, respectively, of the locations of children with asthma. The results of spatial autocorrelation and RF model showed that the criteria of distance to parks and streets as well as PM 2.5 and PM 10 had the greatest impact on asthma occurrence in the study area. Spatial autocorrelation analyses indicated that the distribution of asthma cases was not random. According to receiver operating characteristic results, the RF model had good accuracy (the area under the curve was 0.987 and 0.921, respectively, for training and testing data).

In this article, the gully erosion susceptibility map (GESM) was developed for the Abdanan region, Ilam province, Iran using frequency ratio (FR), logistic regression (LR), an ensemble of radial basis function (RBF) and imperialist competitive algorithm (ICA) models. At first, 74 gully erosion locations have been recognized and then 52 (70%) cases had been regarded for training, while 22 (30%) cases validate the models. To model the GESM, 12 affecting factors on gully erosion were identified and prepared in a GIS environment, including altitude, slope angle, slope aspect, plan curvature, topographical wetness index (TWI), normalized differential vegetation index (NDVI), soil type, rainfall, distance to river, distance to road, lithology and land use in the study area. According to the results of LR model, slope aspect factor (2.439) is the most important and NDVI factor (–1.462) has the least importance on the occurrence of gully erosion. Also, the results of FR model show that MuPlaj class (FR = 2.5) of lithology factor is the most important in gully erosion. The ROC–AUC has been applied to confirm the accuracy of the models. The ROC–AUC results show efficiencies of 89.9%, 88.3% and 85.1% for RBF–ICA, LR and FR models, respectively.

Industry 4.0 (I4.0) includes an important regeneration of production and management systems within manufacturing, where the majority of the procedures will be entirely or partially automated. However, there are insufficient research studies related to I4.0 development assessment based on the various requirement factors to enable countries to measure their own conditions and to make future national strategies and roadmaps for their activities in this field. Thus, this paper develops a comprehensive framework to assess the countries' readiness level in I4.0 development. The research is conducted in several steps. First, the main required clusters and their criteria of I4.0 development assessment such as technological, social, economic, political and environmental clusters are determined. Second, the importance of the clusters and their criteria are specified using the Fuzzy DEMATLE and Fuzzy ANP techniques. Third, the countries are ranked using the VIKOR technique. In this study, the conditions of I4.0 development for South Korea, the United States, China, Iran, Japan, and Germany are assessed based on the proposed framework. The ranking results show the United States, Germany, and Japan got ranked first to third, based on the obtained VIKOR index, 0.000, 0.528, and 0.559, respectively. The framework is verified using sensitivity analysis and validated based on the countries' existing plans. The evaluation results are rescannable. The result shows that the United States, Germany, Japan, and South Korea got high scores especially in the technological cluster and also some challenges in the criteria related to the social and cultural clusters. On the other hand, China and Iran got the highest score in the labor force criterion of the social cluster (0.023) and the intercultural understanding criterion of the cultural cluster (0.006), respectively, however, they need to strengthen the technological cluster “to be hopeful of having” an active role in future global marketing.

Traffic stream determining is an essential part of the intelligent transportation management system. Precise prediction of traffic flow provides a basis for other tasks, like forecasting travel time. While traditional methods have some merits for improving traffic prediction precision in some ways, high precision, considering different circumstances, is still difficult to achieve. This article presents a short-term traffic flow prediction model based on the Modified Elman Recurrent Neural Network model (GA-MENN) to deal with this practical problem. In GA-MENN, the algorithm of Elman Recurrent Neural Network is modified, optimized through the Genetic Algorithm (GA) and considered weather conditions, weekday, hour and day's classification to forecast the vehicle velocity in Tehran streets and highways. The traffic data were collected from the online Google Map API service for 139 routs in 7 districts in Tehran. The method improves prediction precision and also lowers the prediction error rate, according to experimental results. Exploratory outcomes verify the superior performance of the proposed traffic condition prediction model over Regression Multi-layer Perceptron, Linear Regression, Logistic Regression, Probabilistic Neural Network, Regression Generalized Feedforward, Time-lag Recurrent Network, Support Vector Machine model, Elman neural network, K- NN model, ARIMA, Kalman filter model, Convolutional Neural Networks (CNNs), SARIMA, and Long Short-Term Memory (LSTM) model. To the best of our knowledge, this is the first occasion when that traffic stream is gauged in urban roads and avenues in this specific way.

Spatial experiences in route finding, such as the ability of finding low‐traffic routes, exert a significant influence on travel time in big cities; therefore, the spatial experiences of seasoned individuals such as taxi drivers in route finding can be useful for improving route‐finding algorithms and preventing using routes having considerable traffic. In this regard, a spatial experience‐based route‐finding algorithm is introduced through ontology in this paper. To this end, different methods of modeling experiences are investigated. Then, a modeling method is chosen for modeling the experiences of drivers for route finding depending on the advantages of ontology, and an ontology based on the taxi drivers’ experiences is proposed. This ontology is employed to create an ontology‐based route‐finding algorithm. The results are compared with those of Google maps in terms of route length and travel time at peak traffic time. According to the results, although the route lengths of route‐finding method based on the ontology of drivers’ experiences in three cases (from nine cases) are greater than that based on Google maps, the travel times are shorter in most cases, and in some routes, the difference in travel time reaches only 10 minutes.

Purpose: The main purpose of this research is to locate the public libraries in Bojnourd based on the indicators of centrality, consistency and natural features of the earth. Identification of the major criteria in the site selection of the libraries, determination of the appropriate method for standardizing and weighting the criteria, and definition of appropriate scenarios for each of the selected area as based on their physical properties. Method: The present study's method is descriptive with a practical nature and a quantitative approach, and the survey method has been used to collect the data. In order to standardize the criteria maps, fuzzy functions were used; in to weigh the site selection criteria, the Fuzzy Analytical Hierarchy Process (FAHP) were utilized; and to combine the criteria, the weighted linear combination (WLC) were used. The Delphi method and paired comparison questionnaire were used to weight the criteria. The statistical population is composed of the experts related to the site selection of libraries, and 12 persons were selected as the sample using the targeted non-random sampling method. Findings: The findings of the research indicated that in general, population density, accessibility, distance from existing libraries, proximity to educational centers and proximity to cultural and religious centers, respectively, have been the most important criteria in terms of the importance of the final weight of criteria. By combining the criteria considered in this study, 6 areas had a high spatial fitness for library space. In addition, given the standard per capita, Bojnurd city needs 3 new libraries which can be selected according to the policies considered, from the 6 areas with high spatial fitness. Originality/value: Locating the appropriate areas for building a library can prevent additional costs before building a library. In addition, the correct site selection of libraries can have a significant impact on the efficiency of libraries. Considering the limited budget and the equipping policies of libraries, various strategies are adopted to lease a place for the library or to build a new library. Therefore, several plans and scenarios are presented in this research with an innovative approach within the selected areas according to the status of the existing uses of buildings and and the bulidings' quality. Furthermore, the most important issue that has received less attention thus far is the uncertainty in judgments. Therefore, the fuzzy multi-criteria decision-making method was used in order to solve this problem.

Paintings evoke certain emotions in the viewers. Colors, shape, texture, and many other factors affect the feeling conveyed by paintings, but colors seem to have a stronger effect due to a century-long study of color-emotion association in various fields of psychology, art, and color science. There are many color-emotion theories and most of them have been implemented, however, the Luscher Color Test is untouched amongst them. Based on several reasons, discussed in detail inside the paper, we believe this theory can cover problems in this domain of emotion extraction. The main motivation for choosing the Luscher test was that this method is designed for personality and mood analysis and it can better study abstract paintings. In this paper, a set of paintings from Iranian-Islamic cultural heritage is chosen as a dataset. We have proposed the L-EEP method based on Culture Technology (CT) concept to extract emotions from paintings with image processing techniques and psychology knowledge. This method extracts specific colors from paintings and by performing the Luscher test automatically, is able to determine eight emotions. For this matter, paintings are assessed in two moods: 1. The full extent of the painting 2. Cropped interest area of the painting that attracts more attention. Then, the color palette which is extracted colors ordered based on their coverage extent enters search engine. The search engine performs the searching process in the 3D knowledge base of Luscher color-emotion layers to extract relative values of emotions in both scenarios. For the evaluation of the results, three steps were taken. First, we compared the output results of ancient Persian painting with literature and text of their background stories. Then a viewer evaluation is done to compare the results with human viewpoint. Finally, a set of modern abstract paintings peer-rated in the IAPS standard system to further examine the proposed method. The results of the three forms of evaluation indicate the applicability of the L-EEP approach.

Increasing global demand for travel has drawn public attention to the tourism industry. This industry needs the design of intelligent systems based on new concepts to facilitate better service delivery. To this end, this study proposes a ubiquitous tourist system based on context-awareness, multicriteria decision making (MCDM), and augmented reality (AR) using a geospatial information system (GIS). This system provides two services to the user. First, it recommends a hotel in the vicinity of the user based on their preferences. Subsequently, it modifies the information property to augment the information concerning the visited object using AR technology. This system offers the advantage of adapting its models based on the user and their environment using context-awareness, thereby facilitating increased system automation during service delivery. Furthermore, this system enables personalization based on user needs. Our system was evaluated via a usability test using a Likert scale based on two system aspects, namely, system design, and user acceptance of the result. The output of this test yielded an average score of 4.112. The proximity of this score to the highest level of the Likert scale indicates the acceptance of the system by users.

A highly sensitive dual-polarized 'X' oriented quadruple D-shaped open channel photonic crystal fiber (PCF) based surface plasmon resonance (SPR) sensor for various analyte detection is proposed in this paper. Gold is taken as a plasmonic material for its stability and compatibility. Silicon nitride (Si3N4) and titanium oxide (TiO2) has been used separately as an adhesive layer of gold to elevate the sustainability of the evanescent field. This paper shows a comparative study and inspects the effect of sensing performance between Si3N4 and TiO2 as an adhesive layer of gold. Numerical investigations have been followed up using the finite element method (FEM). For practical feasibility, analyte and plasmonic materials have been placed at the outer surface of the sensor. After watchful investigation, the maximum wavelength sensitivities of 21,000 nm/RIU (Refractive Index Unit) and 18,000 nm/RIU have been found for the y-polarization when using TiO2 and Si3N4, respectively. The highest amplitude sensitivities are of 914RIU−1 and 625RIU−1 for TiO2 and Si3N4, respectively. Furthermore, minimum wavelength resolutions of 4.76 × 10−6 RIU and 5.55 × 10−6 RIU have been observed in y-polarization for TiO2 and Si3N4, respectively. The sensor evinces a maximum figure of merit (FOM) of 236RIU−1 for TiO2. This sensor has the analyte sensing range of 1.31–1.38RI (Refractive Index) for TiO2 and 1.32–1.39RI for Si3N4. The sensor also delivers low confinement loss for Si3N4 and TiO2, which certifies viability in fabricating the design. Recognizing this sensor’s wavelength sensitivity, amplitude sensitivity, and sensing RI range, it could be a promising candidate for detecting different liquid analytes with excellent accuracy.

This paper investigates the capabilities of the evolutionary fuzzy genetic (FG) approach and compares it with three neuro-fuzzy methods—neuro-fuzzy with grid partitioning (ANFIS-GP), neuro-fuzzy with subtractive clustering (ANFIS-SC), and neuro-fuzzy with fuzzy c-means clustering (ANFIS-FCM)—in terms of modeling long-term air temperatures for sustainability based on geographical information. In this regard, to estimate long-term air temperatures for a 40-year (1970–2011) period, the models were developed using data for the month of the year, latitude, longitude, and altitude obtained from 71 stations in Turkey. The models were evaluated with respect to mean absolute error (MAE), root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), and the determination coefficient (R2). All data were divided into three parts and every model was tested on each. The FG approach outperformed the other models, enhancing the MAE, RMSE, NSE, and R2 of the ANFIS-GP model, which yielded the highest accuracy among the neuro-fuzzy models by 20%, 30%, and 4%, respectively. A geographical information system was used to obtain temperature maps using estimates of the optimal models, and the results of the model were assessed using it.

Financial news has been proven to be a crucial factor which causes fluctuations in stock prices. However, previous studies heavily relied on analyzing shallow features and ignored the structural relation among words in a sentence. Several sentiment analysis studies have tried to point out the relationship between investors' reaction and news events. However, the sentiment dataset was usually constructed from the lingual dataset which is unrelated to the financial sector and led to poor performance. This paper proposes a novel framework to predict the directions of stock prices by using both financial news and sentiment dictionary. The original contributions of this paper include the proposal of a novel two-stream gated recurrent unit network and Stock2Vec-a sentiment word embedding trained on financial news dataset and Harvard IV-4. Two main experiments are conducted: the first experiment predicts S&P 500 index stock price directions using the historical S&P 500 prices and the articles crawled from Reuters and Bloomberg, and the second experiment forecasts the price trends of VN-index using VietStock news and stock prices from cophieu68. Results show that: 1) two-stream GRU outperforms state-of-the-art models; 2) Stock2Vec is more efficient in dealing with financial datasets; and 3) applying the model, a simulation scenario proves that our model is effective for the stock sector.

The objective of this research is to develop an machine learning (ML) -based system that evaluates the performance of high school students during the semester and identify the most significant factors affecting student performance. It also specifies how the performance of models is affected when models run on data that only include the most important features. Classifiers employed for the system include random forest (RF), support vector machines (SVM), logistic regression (LR) and artificial neural network (ANN) techniques. Moreover, the Boruta algorithm was used to calculate the importance of features. The dataset includes behavioral information, individual information and the scores of students that were collected from teachers and a one-by-one survey through an online questionnaire. As a result, the effective features of the database were identified, and the least important features were eliminated from the dataset. The ANN accuracy, which was the best accuracy in the original dataset, was reduced in the decreased dataset. On the contrary, SVM performance was improved, which had the highest accuracy among other models, with 0.78. Moreover, the LR and RF models could provide the same performance in the decreased dataset. The results showed that ML models are influential for evaluating students, and stakeholders can use the identified effective factors to improve education.

IoT, as an emerging technology along with GIS, can result in advanced and user-friendly features in Smart Cities. In order to investigate the capabilities offered by these technologies, this paper provides an overview of GIS and IoT integration focusing on applications and architecture. Specifically, this paper starts with investigating the role of GIS and IoT separately and jointly in different domains. Then, a review of GIS and IoT integration studies is provided to examine how GIS could be used in IoT architecture. The results showed that the capabilities of GIS in dealing with geospatial data and attributes along with offering visualization and analyzing tools make it possible to develop an integrated system benefiting from real-time data collection and real-time monitoring provided by IoT. The presented details would assist researchers in future studies on utilizing GIS and IoT at the same time.

In this study, asthma-prone area modeling of Tehran, Iran was provided by employing three ensemble machine learning algorithms (Bootstrap aggregating (Bagging), Adaptive Boosting (AdaBoost), and Stacking). First, a spatial database was created with 872 locations of asthma patients and affecting factors (particulate matter (PM10 and PM2.5), ozone (O3), sulfur dioxide (SO2), carbon monoxide (CO), nitrogen dioxide (NO2), rainfall, wind speed, humidity, temperature, distance to street, traffic volume, and a normalized difference vegetation index (NDVI)). We created four factors using remote sensing (RS) imagery, including air pollution (O3, SO2, CO, and NO2), altitude, and NDVI. All criteria were prepared using a geographic information system (GIS). For modeling and validation, 70% and 30% of the data were used, respectively. The weight of evidence (WOE) model was used to assess the spatial relationship between the dependent and independent data. Finally, three ensemble algorithms were used to perform asthma-prone areas mapping. According to the Gini index, the most influential factors on asthma occurrence were distance to the street, NDVI, and traffic volume. The area under the curve (AUC) of receiver operating characteristic (ROC) values for the AdaBoost, Bagging, and Stacking algorithms was 0.849, 0.82, and 0.785, respectively. According to the findings, the AdaBoost algorithm outperforms the Bagging and Stacking algorithms in spatial modeling of asthma-prone areas.

GPS-equipped vehicles are an effective approach for acquiring urban population movement patterns. Attempts have been made in the present study in order to identify the population displacement pattern of the study region using taxis’ origin and destination data, and then model the parameters affecting the population displacement pattern and provide an ultimate model in order to predict pick-up and drop-off locations. In this way, the passenger pick-up and drop-off locations have been identified in order to obtain the population movement pattern. In this study, Moran’s I index was used to measure the spatial autocorrelation, and hot spot analysis was used to analyze spatial patterns of pick-up and drop-off locations. Effective parameters modeling was performed using the Poisson regression. The results of the spatiotemporal distribution map for pick-up and drop-off locations indicated a similarity in patterns and equal results for some locations. Results also indicated different features of spatial distribution during different hours of the day. Spatial autocorrelation analysis results indicated a low probability of randomness in the general spatial distribution of the locations. The result of modeling the parameters shows the positive effect of the parameters on the pattern of population movement, and according to the p-value of 0.000, Poisson regression is significant for the pick-up and drop-off locations. The modeling results also highlighted the importance of movement patterns in recognizing urban hot spots, which is valuable for policymakers and urban planners.

A lack of required data resources is one of the challenges of accepting the Augmented Reality (AR) to provide the right services to the users, whereas the amount of spatial information produced by people is increasing daily. This research aims to design a personalized AR that is based on a tourist system that retrieves the big data according to the users’ demographic contexts in order to enrich the AR data source in tourism. This research is conducted in two main steps. First, the type of the tourist attraction where the users interest is predicted according to the user demographic contexts, which include age, gender, and education level, by using a machine learning method. Second, the correct data for the user are extracted from the big data by considering time, distance, popularity, and the neighborhood of the tourist places, by using the VIKOR and SWAR decision making methods. By about 6%, the results show better performance of the decision tree by predicting the type of tourist attraction, when compared to the SVM method. In addition, the results of the user study of the system show the overall satisfaction of the participants in terms of the ease-of-use, which is about 55%, and in terms of the systems usefulness, about 56%.

Every day, people are hired in different organizations and old and retiring employees are eliminated from enterprise systems. Eliminating these individuals from organizations leads to the loss of their spatial experiences. In addition, since new employees lack relevant experience, they need a long time to develop the correct skills for the company and may even cause damage to the organization during this learning process. Therefore, storing the spatial experience of individuals is a critical issue. Due to the intelligence of ubiquitous Geospatial Information System (GIS), any experience from any user can be received and stored. In the future, based on these experiences, an appropriate service to each user may be provided as needed. This paper aims to propose an ontology-based model to store spatial experiences in the field of ubiquitous GIS route finding. For this purpose, first ontology is designed for route finding, and then according to this ontology, an ontology-based route-finding algorithm is developed for ubiquitous GIS. Finally, this algorithm is implemented for Tehran, Iran, and its results are compared with the shortest path algorithm (Dijkstra’s algorithm) in terms of the route length and travel time for peak traffic time. The results show that while the route length obtained from the ontology-based algorithm is more than Dijkstra’s algorithm, the travel time is lower, and on some routes the difference in travel time saved reaches 35 minutes.

In this paper, land subsidence susceptibility was assessed for Shahryar County in Iran using the adaptive neuro-fuzzy inference system (ANFIS) machine learning algorithm. Another aim of the present paper was to assess if ensembles of ANFIS with two meta-heuristic algorithms (imperialist competitive algorithm (ICA) and gray wolf optimization (GWO)) would yield a better prediction performance. A remote sensing synthetic aperture radar (SAR) dataset from 2019 to 2020 and the persistent-scatterer SAR interferometry (PS-InSAR) technique were used to obtain a land subsidence inventory of the study area and use it for training and testing models. Resulting PS points were divided into two parts of 70% and 30% for training and testing the models, respectively. For susceptibility analysis, eleven conditioning factors were taken into account: the altitude, slope, aspect, plan curvature, profile curvature, topographic wetness index (TWI), distance to stream, distance to road, stream density, groundwater drawdown, and land use/land cover (LULC). A frequency ratio (FR) was applied to assess the correlation of factors to subsidence occurrence. The prediction power of the models and their generated land subsidence susceptibility maps (LSSMs) were validated using the root mean square error (RMSE) value and area under curve of receiver operating characteristic (AUC-ROC) analysis. The ROC results showed that ANFIS-ICA had the best accuracy (0.932) among the models (ANFIS-GWO (0.926), ANFIS (0.908)). The results of this work showed that optimizing ANFIS with meta-heuristics considerably improves LSSM accuracy although ANFIS alone had an acceptable result.

With the development of Internet of Things (IoT) applications, applying the potential and benefits of IoT technology in the health and environment services is increasing to improve the service quality using sensors and devices. This paper aims to apply GIS-based optimization algorithms for optimizing IoT-based network deployment through the use of wireless sensor networks (WSNs) and smart connected sensors for environmental and health applications. First, the WSN deployment research studies in health and environment applications are reviewed including fire monitoring, precise agriculture, telemonitoring, smart home, and hospital. Second, the WSN deployment process is modeled to optimize two conflict objectives, coverage and lifetime, by applying Minimum Spanning Tree (MST) routing protocol with minimum total network lengths. Third, the performance of the Bees Algorithm (BA) and Particle Swarm Optimization (PSO) algorithms are compared for the evaluation of GIS-based WSN deployment in health and environment applications. The algorithms were compared using convergence rate, constancy repeatability, and modeling complexity criteria. The results showed that the PSO algorithm converged to higher values of objective functions gradually while BA found better fitness values and was faster in the first iterations. The levels of stability and repeatability were high with 0.0150 of standard deviation for PSO and 0.0375 for BA. The PSO also had lower complexity than BA. Therefore, the PSO algorithm obtained better performance for IoT-based sensor network deployment.

This study aimed to prepare forest fire susceptibility mapping (FFSM) using a ubiquitous GIS and an ensemble of adaptive neuro fuzzy interface system (ANFIS) with genetic (GA) and simulated annealing (SA) algorithms (ANFIS-GA-SA) and an ensemble of radial basis function (RBF) with an imperialist competitive algorithm (ICA) (RBF-ICA) model in Chaharmahal and Bakhtiari Province, Iran. The forest fire areas were determined using MODIS satellite imagery and a field survey. The modeling and validation of the models were performed with 70% (183 locations) and 30% (79 locations) of forest fire locations (262 locations), respectively. In order to prepare the FFSM, 10 criteria were then used, namely altitude, rainfall, slope angle, temperature, slope aspect, wind effect, distance to roads, land use, distance to settlements and soil type. After the FFSM was prepared, the maps were designed and implemented for web GIS and mobile application. A receiver operating characteristic (ROC)- area under the curve (AUC) index was used to validate the prepared maps. The ROC-AUC results showed an accuracy of 0.903 for the ANFIS-GA-SA model and an accuracy of 0.878 for the RBF-ICA model. The results of the spatial autocorrelation showed that the occurrence of fire in the study area has a cluster distribution and most of the spatial dependence is related to the distance to settlement, soil and rainfall variables.

Most existing augmented reality (AR) applications are suitable for cases in which only a small number of real world entities are involved, such as superimposing a character on a single surface. In this case, we only need to calculate pose of the camera relative to that surface. However, when an AR health or environmental application involves a one-to-one relationship between an entity in the real-world and the corresponding object in the computer model (geo-referenced object), we need to estimate the pose of the camera in reference to a common coordinate system for better geo-referenced object registration in the real-world. New innovations in developing cheap sensors, computer vision techniques, machine learning, and computing power have helped to develop applications with more precise matching between a real world and a virtual content. AR Tracking techniques can be divided into two subcategories: marker-based and marker-less approaches. This paper provides a comprehensive overview of marker-less registration and tracking techniques and reviews their most important categories in the context of ubiquitous Geospatial Information Systems (GIS) and AR focusing to health and environmental applications. Basic ideas, advantages, and disadvantages, as well as challenges, are discussed for each subcategory of tracking and registration techniques. We need precise enough virtual models of the environment for both calibrations of tracking and visualization. Ubiquitous GISs can play an important role in developing AR in terms of providing seamless and precise spatial data for outdoor (e.g., environmental applications) and indoor (e.g., health applications) environments.

Currently, new technologies have enabled the design of smart applications that are used as decision-making tools in the problems of daily life. The key issue in designing such an application is the increasing level of user interaction. Mixed reality (MR) is an emerging technology that deals with maximum user interaction in the real world compared to other similar technologies. Developing an MR application is complicated, and depends on the different components that have been addressed in previous literature. In addition to the extraction of such components, a comprehensive study that presents a generic framework comprising all components required to develop MR applications needs to be performed. This review studies intensive research to obtain a comprehensive framework for MR applications. The suggested framework comprises five layers: the first layer considers system components; the second and third layers focus on architectural issues for component integration; the fourth layer is the application layer that executes the architecture; and the fifth layer is the user interface layer that enables user interaction. The merits of this study are as follows: this review can act as a proper resource for MR basic concepts, and it introduces MR development steps and analytical models, a simulation toolkit, system types, and architecture types, in addition to practical issues for stakeholders such as considering MR different domains.

Preparing for intensifying threats of emergencies in unexpected, dangerous, and serious natural or man-made events, and consequent management of the situation, is highly demanding in terms of coordinating the personnel and resources to support human lives and the environment. This necessitates prompt action to manage the uncertainties and risks imposed by such extreme events, which requires collaborative operation among different stakeholders (i.e., the personnel from both the state and local communities). This research aims to find a way to enhance the coordination of multi-organizational response operations. To do so, this manuscript investigates the role of participants in the formed coordination response network and also the emergence and temporal dynamics of the network. By analyzing an inter-personal response coordination operation to an extreme bushfire event, the networks' and participants' structural change is evaluated during the evolution of the operation network over four time durations. The results reveal that the coordination response network becomes more decentralized over time due to the high volume of communication required to exchange information. New emerging communication structures often do not fit the developed plans, which stress the need for coordination by feedback in addition to by plan. In addition, we find that the participant's brokering role in the response operation network identifies a formal and informal coordination role. This is useful for comparison of network structures to examine whether what really happens during response operations complies with the initial policy.

The purpose of this paper is to model one of the urban network problems, the issue of water leakage. In order to manage water leakage, the specific area should be partially isolated from the rest of the network. As Geospatial Information System (GIS) is a powerful technology in spatial modeling, analysis and visualization of the water network management, a web GIS system for finding optimal valves to close in the event of an incident was developed. The system consists of a new GIS based algorithm for identifying the ideal valves to isolate the desired pipeline. The algorithm is able to identify optimum valves in a water distribution network in the shortest time by using the traceability in GIS web services. The system uses the functions of storing and managing the spatial data by expert users based on web 2.0 technology. The system was implemented and evaluated for Tehran’s district 5 water distribution network using Silverlight, C# and ArcGIS SDK (Software Development Kit). The evaluations demonstrated the accuracy of the algorithm and the operational viability of the system developed.

In the future, groundwater will be the major source of water for agriculture, drinking and food production as a result of global climate change. With increasing population growth, demand for groundwater has increased. Therefore, sustainable groundwater storage management has become a major challenge. This study introduces a new ensemble data mining approach with bivariate statistical models, using FR (frequency ratio), CF (certainty factor), EBF (evidential belief function), RF (random forest) and LMT (logistic model tree) to prepare a groundwater potential map (GPM) for the Booshehr plain. In the first step, 339 wells were chosen and randomly split into two groups with groundwater yields above 11 m3/h. A total of 238 wells (70%) were used for model training, and 101 wells (30%) were used for model validation. Then, 15 effective factors, including topographic and hydrologic factors, were selected for the modeling. The accuracy of the groundwater potential maps was determined using the ROC (receiver operating characteristic) curve and the AUC (area under the curve). The results show that the AUC obtained using the CF-RF, EBF-RF, FR-RF, CF-LMT, EBF-LMT and FR-LMT methods were 0.927, 0.924, 0.917, 0.906, 0.885 and 0.83, respectively. Therefore, it can be inferred that the ensemble of bivariate statistic and data mining models can improve the effectiveness of the methods in developing a groundwater potential map.

In recent years, the development of the GIS-T (Geographic Information System for Transportation) applications has gained much attention, providing the transportation planners and managers with in-depth knowledge to achieve better decisions. Needless to say, developing a successful GIS for transportation applications is highly dependent on the design of a well-structured data model. Dynamic segmentation (DS) data model is a popular one being used more and more for different GIS-T analyses, serving as a data model that splits linear features into new set of segments wherever its attributes change. In most cases, the sets of segments presenting a particular attribute change frequently. Transportation managers place great importance on having regular update and revision of segmented data to ensure correct and precise decisions are made. However, updating the segmented data manually is a difficult task and a time-consuming process to do, demanding an automatic approach. To alleviate this, the present study describes a rule-based method using topological concept to simply update road segments and replace the manual tasks that users are to carry out. The proposed approach was employed and implemented on real road network data of the City of Tehran provided by the Road Maintenance and Transportation Organization (RMTO) of Iran. The practical results demonstrated that the time, cost, human-type errors, and complexity involved in update tasks are all reduced. KEYWORDS: GIS-T, dynamic segmentation, segment, automatic update, change type, rule

Background: Cardiac arrest is a condition in which the heart is completely stopped and is not pumping any blood. Although most cardiac arrest cases are reported from homes or hospitals, about 20% occur in public areas. Therefore, these areas need to be investigated in terms of cardiac arrest incidence so that places of high incidence can be identi-fied and cardiac rehabilitation defibrillators installed there. Methods: In order to investigate a study area in Petersburg, Pennsylvania State, and to determine appropriate places for installing defibrillators with 5-year period data, swarm intelligence algorithms were used. Moreover, the location of the defibrillators was determined based on the following five evaluation criteria: land use, altitude of the area, econom-ic conditions, distance from hospitals and approximate areas of reported cases of cardiac arrest for public places that were created in geospatial information system (GIS). Results: The A-P HADEL algorithm results were more precise about 27.36%. The validation results indicated a wider coverage of real values and the verification results confirmed the faster and more exact optimization of the cost func-tion in the PSO method. Conclusion: The study findings emphasize the necessity of applying optimal optimization methods along with GIS and precise selection of criteria in the selection of optimal locations for installing medical facilities because the selected algorithm and criteria dramatically affect the final responses. Meanwhile, providing land suitability maps for installing facilities across hot and risky spots has the potential to save many lives.