The exponential growth of population and industries has brought about an increase in energy consumption, causing severe climatic and environmental problems. Therefore, the move towards green ...renewable energy is being ever more intensified. This study aims at estimating the rooftop solar power production for Tehran, the capital city of Iran, using a Geospatial Information System (GIS) to assess the big data of city building parcels. Tehran is faced with severe air pollution due to its excessive fossil fuel usage, and its electricity demand is increasing. As a result, this paper attempts to provide the quantified solar power potential of city roof tops for policymakers and authorities in order to facilitate decision-making in relation to integrating renewable energies into the power production infrastructure. The results shows that approximately 3000 GWh (more than 14% of the total electric energy consumption) of solar power can be produced by the rooftop PV installations in Tehran. The potential nominal power of rooftop PV installations is estimated to be more than 2000 MW, which is four times the current installed PV capacity of the whole country. The findings of the study suggest that there is great potential hidden on the rooftops of the city, which can be utilized to assist the power systems of the city in the longer run for a more sustainable future.
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.
The ability of the Canadian agriculture sector to make better decisions and manage its operations more competitively in the long term is only as good as the information available to inform ...decision-making. At all levels of Government, a reliable flow of information between scientists, practitioners, policy-makers, and commodity groups is critical for developing and supporting agricultural policies and programs. Given the vastness and complexity of Canada’s agricultural regions, space-based remote sensing is one of the most reliable approaches to get detailed information describing the evolving state of the country’s environment. Agriculture and Agri-Food Canada (AAFC)—the Canadian federal department responsible for agriculture—produces the Annual Space-Based Crop Inventory (ACI) maps for Canada. These maps are valuable operational space-based remote sensing products which cover the agricultural land use and non-agricultural land cover found within Canada’s agricultural extent. Developing and implementing novel methods for improving these products are an ongoing priority of AAFC. Consequently, it is beneficial to implement advanced machine learning and big data processing methods along with open-access satellite imagery to effectively produce accurate ACI maps. In this study, for the first time, the Google Earth Engine (GEE) cloud computing platform was used along with an Artificial Neural Networks (ANN) algorithm and Sentinel-1, -2 images to produce an object-based ACI map for 2018. Furthermore, different limitations of the proposed method were discussed, and several suggestions were provided for future studies. The Overall Accuracy (OA) and Kappa Coefficient (KC) of the final 2018 ACI map using the proposed GEE cloud method were 77% and 0.74, respectively. Moreover, the average Producer Accuracy (PA) and User Accuracy (UA) for the 17 cropland classes were 79% and 77%, respectively. Although these levels of accuracies were slightly lower than those of the AAFC’s ACI map, this study demonstrated that the proposed cloud computing method should be investigated further because it was more efficient in terms of cost, time, computation, and automation.
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.
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.
A large portion of Canada is covered by wetlands; mapping and monitoring them is of great importance for various applications. In this regard, Remote Sensing (RS) technology has been widely employed ...for wetland studies in Canada over the past 45 years. This study evaluates meta-data to investigate the status and trends of wetland studies in Canada using RS technology by reviewing the scientific papers published between 1976 and the end of 2020 (300 papers in total). Initially, a meta-analysis was conducted to analyze the status of RS-based wetland studies in terms of the wetland classification systems, methods, classes, RS data usage, publication details (e.g., authors, keywords, citations, and publications time), geographic information, and level of classification accuracies. The deep systematic review of 128 peer-reviewed articles illustrated the rising trend in using multi-source RS datasets along with advanced machine learning algorithms for wetland mapping in Canada. It was also observed that most of the studies were implemented over the province of Ontario. Pixel-based supervised classifiers were the most popular wetland classification algorithms. This review summarizes different RS systems and methodologies for wetland mapping in Canada to outline how RS has been utilized for the generation of wetland inventories. The results of this review paper provide the current state-of-the-art methods and datasets for wetland studies in Canada and will provide direction for future wetland mapping research.
Assessing groundwater potential for sustainable resource management is critically important. In addressing this concern, this study aims to advance the field by developing an innovative approach for ...Groundwater potential zone (GWPZ) mapping using advanced techniques, such as FuzzyAHP, FuzzyDEMATEL, and Logistic regression (LR) models. GWPZ was carried out by integrating various primary factors, such as hydrologic, soil permeability, morphometric, terrain distribution, and anthropogenic influences, incorporating twenty-seven individual criteria using multi-criteria decision models along with a hybrid approach for the Subarnarekha River basin, India, in Google earth engine (GEE). The predictive capability of the model was evaluated using a Multi-Collinearity test (VIF <10.0), followed by applying a random forest model, considering the weighted impact of the five primary factors. The hybrid model for GWPZ classification showed that 21.97 % (4256.3 km2) of the area exhibited very high potential, while 11.37 % (2202.1 km2) indicated very low potential for GW in this area. Validation of the groundwater level data from 72 observation wells, performed by the Area under receiver operating characteristic (AUROC) curve technique, yielded values ranging between 75 % and 78 % for different models, underscoring the robust predictability of GWPZ. The hybrid and LR-FuzzyAHP models demonstrated remarkable effectiveness in GWPZ mapping, indicating that the downstream and southern regions boast substantial groundwater potential attributed to alluvial soil and favorable recharge conditions. Conversely, the central part grapples with a scarcity of groundwater. It holds the potential to assist planners and managers in formulating strategies for managing groundwater levels and alleviating the impacts of future droughts.
Nowadays with the increase of high-rise buildings, emergency evacuation is an indispensable part of urban environment management. Due to various disaster incidents occurred in indoor environments, ...research has concentrated on ways to deal with the different difficulties of indoor emergency evacuation. Although global navigation satellite systems (GNSSs) such as global positioning system (GPS) come in handy in outdoor spaces, they are not of much use in enclosed places, where satellite signals cannot penetrate easily. Therefore, other approaches must be considered for pedestrian navigation to cope with the indoor positioning problem. Another problem in such environments is the information of the building indoor space. The majority of the studies has used prepared maps of the building, which limits their methodology to that specific study area. However, in this study we have proposed an end-to-end method that takes advantage of BIM model of the building, thereby applicable to every structure that has an equivalent building information model (BIM). Moreover, we have used a mixture of Wi-Fi fingerprinting and pedestrian dead reckoning (PDR) method with relatively higher accuracy compared to other similar methods for navigating the user to the exit point. For implementing PDR, we used the sensors in smartphones to calculate user steps and direction. In addition, the navigational information was superimposed on the smartphone screen using augmented reality (AR) technology, thus communicating the direction information in a user-friendly manner. Finally, the AR mobile emergency evacuation application developed was assessed with a sample audience. After an experience with the app, they filled out a questionnaire which was designed in the system usability scale test (SUS) format. The evaluation results showed that the app achieved an acceptable suitability for usage.
Oceans cover over 70% of the Earth’s surface and provide numerous services to humans and the environment. Therefore, it is crucial to monitor these valuable assets using advanced technologies. In ...this regard, Remote Sensing (RS) provides a great opportunity to study different oceanographic parameters using archived consistent multitemporal datasets in a cost-efficient approach. So far, various types of RS techniques have been developed and utilized for different oceanographic applications. In this study, 15 applications of RS in the ocean using different RS techniques and systems are comprehensively reviewed and discussed. This study is divided into two parts to supply more detailed information about each application. The first part briefly discusses 12 different RS systems that are often employed for ocean studies. Then, six applications of these systems in the ocean, including Ocean Surface Wind (OSW), Ocean Surface Current (OSC), Ocean Wave Height (OWH), Sea Level (SL), Ocean Tide (OT), and Ship Detection (SD), are provided. For each application, the applicable RS systems, their advantages and disadvantages, various RS and Machine Learning (ML) techniques, and several case studies are discussed. The other nine applications, including Iceberg, Sea Ice (SI), Sea Surface temperature (SST), Ocean Surface Salinity (OSS), Ocean Color (OC), Ocean Chlorophyll (OCh), Ocean Oil Spill (OOS), Underwater Ocean, and Fishery, are provided in Part II of this study.
As discussed in the first part of this review paper, Remote Sensing (RS) systems are great tools to study various oceanographic parameters. Part I of this study described different passive and active ...RS systems and six applications of RS in ocean studies, including Ocean Surface Wind (OSW), Ocean Surface Current (OSC), Ocean Wave Height (OWH), Sea Level (SL), Ocean Tide (OT), and Ship Detection (SD). In Part II, the remaining nine important applications of RS systems for ocean environments, including Iceberg, Sea Ice (SI), Sea Surface temperature (SST), Ocean Surface Salinity (OSS), Ocean Color (OC), Ocean Chlorophyll (OCh), Ocean Oil Spill (OOS), Underwater Ocean, and Fishery are comprehensively reviewed and discussed. For each application, the applicable RS systems, their advantages and disadvantages, various RS and Machine Learning (ML) techniques, and several case studies are discussed.
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