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The United Nations Educational, Scientific and Cultural Organization (UNESCO) launched its Futures of Education policy initiative in early 2020. The process sought to open a sustained global policy debate on educational futures in a world typified by climatological instability, social inequity, and political unrest. Drawing on Deleuze and Guattari’s theory of assemblages, this paper explores how the Futures of Education initiative simultaneously produces, captures, and reroutes flows of desire by opening its policymaking process to global consultation. Following this line of thinking, the paper introduces a speculative method for critical policy analysis that utilises AI-powered tools to generate expressive images and imaginaries of education futures based on UNESCO’s three stages of policy development. The authors argue that this method enables a step beyond conventional interpretations of a policy’s discursive content toward a speculative analysis of its processual dynamics and expressive potentials. Ultimately, the paper underscores the generative possibilities of AI-assisted methods for tracking flows and expressions of desire within education policy assemblages under conditions of climatological and political upheaval. The authors encourage further experimentation with plugging emerging technologies into global policy assemblages to better understand how desire is invested into particular images and imaginaries of educational futures. [ABSTRACT FROM AUTHOR]

In the context of global climate change and rising sea levels, the adverse impacts of storm surges on the environment, economy, and society of affected areas are becoming increasingly significant. However, due to differences in geography, climate, and other conditions among the affected areas, a single method for assessing the risk of storm surge disasters cannot be fully applicable to all regions. To address this issue, an increasing number of new methods and models are being applied in the field of storm surge disaster risk assessment. This paper introduces representative traditional statistical methods, numerical simulation methods, and artificial intelligence-based techniques in this field. It compares these assessment methods in terms of accuracy, interpretability, and implementation difficulty. The paper emphasizes the importance of selecting appropriate assessment methods based on specific conditions and scientifically combining various methods in practice to improve the accuracy and reliability of storm surge disaster risk assessments. [ABSTRACT FROM AUTHOR]

AbstractThis review explores the transformative potential of Artificial Intelligence (AI) in the light of evolving threats to planetary health, particularly the dangers posed by the climate crisis and its emerging mental health impacts, in the context of a climate-vulnerable country such as the Philippines. This paper describes the country’s mental health system, outlines the chronic systemic challenges that it faces, and discusses the intensifying and widening impacts of climate change on mental health. Integrated mental healthcare must be part of the climate adaptation response, particularly for vulnerable populations. AI holds promise for mental healthcare in the Philippines, and be a tool that can potentially aid in addressing the shortage of mental health professionals, improve service accessibility, and provide direct services in climate-affected communities. However, the incorporation of AI into mental healthcare also presents significant challenges, such as potentially worsening the existing mental health inequities due to unequal access to resources and technologies, data privacy concerns, and potential AI algorithm biases. It is crucial to approach AI integration with ethical consideration and responsible implementation to harness its benefits, mitigate potential risks, and ensure inclusivity in mental healthcare delivery, especially in the era of a warming planet. [ABSTRACT FROM AUTHOR]

Nanotechnology, nanosensors in particular, has increasingly drawn researchers' attention in recent years since it has been shown to be a powerful tool for several fields like mining, robotics, medicine and agriculture amongst others. Challenges ahead, such as food availability, climate change and sustainability, have promoted such attention and pushed forward the use of nanosensors in agroindustry and environmental applications. However, issues with noise and confounding signals make the use of these tools a non-trivial technical challenge. Great advances in artificial intelligence, and more particularly machine learning, have provided new tools that have allowed researchers to improve the quality and functionality of nanosensor systems. This short review presents the latest work in the analysis of data from nanosensors using machine learning for agroenvironmental applications. It consists of an introduction to the topics of nanosensors and machine learning and the application of machine learning to the field of nanosensors. The rest of the paper consists of examples of the application of machine learning techniques to the utilisation of electrochemical, luminescent, SERS and colourimetric nanosensor classes. The final section consists of a short discussion and conclusion concerning the relevance of the material discussed in the review to the future of the agroenvironmental sector. [ABSTRACT FROM AUTHOR]

Research background: The climate crisis is one of the greatest challenges the world is facing in the 21st century. The global response to this problem must comprise transitions in land and ecosystems, energy, urban, infrastructure, and industrial systems and a transformation in enterprise and risk management. With the global pressure to fight climate change and achieve the Sustainable Development Goals, the innovative potential of Industry 4.0 technologies (blockchain, Big Data analytics, the Internet of Things, cloud computing, artificial intelligence, 3D additive manufacturing and technology platforms) has been subject to enormous interest among researchers and practitioners. Additionally, the COVID-19 pandemic has accelerated technology investment and digital transformation in many business areas, potentially including sustainability risk management. Purpose of the article: The purpose of this paper is to explore the scope of Industry 4.0 technology implementation in sustainability risk management and its financial impact. Methods: Based on the survey research, the authors verified if enterprises implementing Industry 4.0 technologies during the last five years are benefiting from their investment depending on the number of technologies. Due to the ordinal measurement scale of the analyzed variables, the verification of hypotheses was carried out using the Mann-Whitney U test. Findings & value added: In the opinion of the managers surveyed, the number of Industry 4.0 technologies implemented in the sustainability risk management process does not affect the enterprise's aggregate Financial Performance Indicator, individual financial indicators (ROA, ROE, OPM, NPM and FLR) and the enterprise's ability to manage financial risk. This study contributes to the literature on the technological transformation of the sustainability risk management process and its financial importance. The design and implementation of sustainable development strategies imply a long-term perspective, including the achievement of risk management effects in this area. The implementation of one technology, let alone two or more, is a difficult and time-consuming management challenge before synergy effects are achieved in an integrated technological eco-system. [ABSTRACT FROM AUTHOR]

Accurate prediction of storm surges is crucial for mitigating the impact of extreme weather events. This paper introduces the Bidirectional Attention-based Long Short-Term Memory (LSTM) Storm Surge Architecture, BALSSA, addressing limitations in traditional physical models. By leveraging machine learning techniques and extensive historical and real-time data, BALSSA significantly enhances prediction accuracy. Utilizing a bidirectional attention-based LSTM framework, it captures complex, non-linear relationships and long-term dependencies, improving the accuracy of storm surge predictions. The enhanced model, D-BALSSA, further amplifies predictive capability through a doubled bidirectional attention-based structure. Training and evaluation involve a comprehensive dataset from over 70 typhoon incidents in Macao between 2017 and 2022. The results showcase the outstanding performance of BALSSA, delivering highly accurate storm surge forecasts with a lead time of up to 72 h. Notably, the model exhibits a low Mean Absolute Error (MAE) of 0.0287 m and Root Mean Squared Error (RMSE) of 0.0357 m, crucial indicators measuring the accuracy of storm surge predictions in water level anomalies. These metrics comprehensively evaluate the model's accuracy within the specified timeframe, enabling timely evacuation and early warnings for effective disaster mitigation. An adaptive system, integrating real-time alerts, tropical cyclone (TC) chaser, and prospective visualizations of meteorological and tidal measurements, enhances BALSSA's capabilities for improved storm surge prediction. Positioned as a comprehensive tool for risk management, BALSSA supports decision makers, civil protection agencies, and governments involved in disaster preparedness and response. By leveraging advanced machine learning techniques and extensive data, BALSSA enables precise and timely predictions, empowering coastal communities to proactively prepare and respond to extreme weather events. This enhanced accuracy strengthens the resilience of coastal communities and protects lives and infrastructure from the escalating threats of climate change. [ABSTRACT FROM AUTHOR]

This paper introduces the concept of infrastructure into discussions on climate change and education. We focus on the links between the increased use of digital data and the central role of data infrastructures in education, and the energy infrastructure needed to support their growing use in schools and school systems. We elaborate a need for a greater accounting of the climate and related social costs of these interwoven digital and energy infrastructures of schooling. We suggest this is part of the 'disposition' of the infrastructures of schooling that should be weighed into decisions on whether and how to continue with digital technologies in schools. By acknowledging the climate and environmental incommensurability of digital infrastructures, education leaders and young people can more fully understand their dispositions and their costs. We propose three implications for education governance that entail greater consideration of the limits of current school climate change infrastructures such as 'eco school' programs and EdTech 'AI for good' initiatives, pushes for 'computing within limits' without substantial changes, and current school governance practices which unnecessarily rely on digital infrastructures. Instead, what is needed may be a reversal of the extensive use of digital infrastructures by schools and education governance bodies. [ABSTRACT FROM AUTHOR]

Solar photovoltaic and wind power systems are very much dependent on climate variations. Wind and solar photovoltaic systems are unreliable without storage units like batteries and diesel generators as a backup. The addition of storage devices increases the reliability of the hybrid system consisting of solar photovoltaic and wind turbines. During cloudy and slow windy days, sufficient battery bank capacity is required to meet the load demand. This review paper gives new ways of hybrid energy generation. It discusses several optimization approaches and ideas for hybrid networks. Hybrid systems are gaining more popularity and fame in the current energy crisis scenario and environmental pollution. This research has provided a comprehensive assessment of existing optimization strategies, particularly those associated with the isolated microgrid in the literature. Artificial intelligence offers noteworthy optimization for microgrid operation without long-term weather data, as evidenced by the current optimization pattern for hybrid renewable sources. [ABSTRACT FROM AUTHOR]

Global drylands, covering about 41% of Earth's surface and inhabited by 38% of the world's population, are facing the stark challenges of water scarcity, low water productivity, and food insecurity. This paper highlights the major constraints to agricultural productivity, traditional irrigation scheduling methods, and associated challenges, efforts, and progress to enhance water use efficiency (WUE), conserve water, and guarantee food security by overviewing different smart irrigation approaches. Widely used traditional irrigation scheduling methods (based on weather, plant, and soil moisture conditions) usually lack important information needed for precise irrigation, which leads to over- or under-irrigation of fields. On the other hand, by using several factors, including soil and climate variation, soil properties, plant responses to water deficits, and changes in weather factors, smart irrigation can drive better irrigation decisions that can help save water and increase yields. Various smart irrigation approaches, such as artificial intelligence and deep learning (artificial neural network, fuzzy logic, expert system, hybrid intelligent system, and deep learning), model predictive irrigation systems, variable rate irrigation (VRI) technology, and unmanned aerial vehicles (UAVs) could ensure high water use efficiency in water-scarce regions. These smart irrigation technologies can improve water management and accelerate the progress in achieving multiple Sustainable Development Goals (SDGs), where no one gets left behind. [ABSTRACT FROM AUTHOR]

The aim of this paper is to highlight the role of computer based information systems in managing the resources of agricultural companies in order to forecast agricultural production and to study, from several points of view, the impact of climate change, respectively the changes brought by them. The Internet of Things involves the use of the Internet to connect different devices, services, and automated systems, thus forming a network of objects. Internet of Things devices will be integrated, connected to computer based information systems to increase the efficiency of agricultural production, by remotely monitoring sensors that can detect soil moisture and crop growth, ensuring (for example) remote management of smart combine harvesters and equipment of irrigation. The data provided by different sensors will be collected and the operational data obtained will be analyzed. The analysis will be performed using artificial intelligence methods that, through specific algorithms, allow the learning of new processing procedures, using data and previous knowledge collected, correlated with information obtained from meteorological services, to improve the making process. of decisions. Climate change is affecting agriculture by decreasing agricultural areas and reducing crops in the affected areas. These changes ultimately lead to a reduction in the amount of food available to humans and animals, to starvation, a phenomenon that still affects millions of people today. [ABSTRACT FROM AUTHOR]

The objective of this paper is to present a novel design of intelligent neuro-supervised networks (INSNs) in order to study the dynamics of a mathematical model for Parkinson's disease illness (PDI), governed with three differential classes to represent the rhythms of brain electrical activity measurements at different locations in the cerebral cortex. The proposed INSNs are constructed by exploiting the knacks of multilayer structure neural networks back-propagated with the Levenberg–Marquardt (LM) and Bayesian regularization (BR) optimization approaches. The reference data for the grids of input and the target samples of INSNs were formulated with a reliable numerical solver via the Adams method for sundry scenarios of PDI models by way of variation of sensor locations in order to measure the impact of the rhythms of brain electrical activity. The designed INSNs for both backpropagation procedures were implemented on created datasets segmented arbitrarily into training, testing, and validation samples by optimization of mean squared error based fitness function. Comparison of outcomes on the basis of exhaustive simulations of proposed INSNs via both LM and BR methodologies was conducted with reference solutions of PDI models by means of learning curves on MSE, adaptive control parameters of algorithms, absolute error, histogram error plots, and regression index. The outcomes endorse the efficacy of both INSNs solvers for different scenarios in PDI models, but the accuracy of the BR-based method is relatively superior, albeit at the cost of slightly more computations. [ABSTRACT FROM AUTHOR]

Wind patterns can change due to climate change, causing more storms, hurricanes, and quiet spells. These changes can dramatically affect wind power system performance and predictability. Researchers and practitioners are creating more advanced wind power forecasting algorithms that combine more parameters and data sources. Advanced numerical weather prediction models, machine learning techniques, and real-time meteorological sensor and satellite data are used. This paper proposes a Recurrent Neural Network (RNN) forecasting model incorporating a Dynamic Fitness Al-Biruni Earth Radius (DFBER) algorithm to predict wind power data patterns. The performance of this model is compared with several other popular models, including BER, Jaya Algorithm (JAYA), Fire Hawk Optimizer (FHO), Whale Optimization Algorithm (WOA), Grey Wolf Optimizer (GWO), and Particle Swarm Optimization (PSO)-based models. The evaluation is done using various metrics such as relative root mean squared error (RRMSE), Nash Sutcliffe Efficiency (NSE), mean absolute error (MAE), mean bias error (MBE), Pearson's correlation coefficient (r), coefficient of determination (R2), and determination agreement (WI). According to the evaluation metrics and analysis presented in the study, the proposed RNN-DFBER-based model outperforms the other models considered. This suggests that the RNN model, combined with the DFBER algorithm, predicts wind power data patterns more effectively than the alternative models. To support the findings, visualizations are provided to demonstrate the effectiveness of the RNN-DFBER model. Additionally, statistical analyses, such as the ANOVA test and the Wilcoxon Signed-Rank test, are conducted to assess the significance and reliability of the results. [ABSTRACT FROM AUTHOR]

The impact of global warming on the environment is a significant concern, and finding effective ways to address climate change is a priority. This paper investigates how Artificial Intelligence (AI) can be utilised to reduce greenhouse gas (GHG) emissions and support efforts to combat climate change, with a particular focus on the chemical industry. The study presents a theoretical framework and comparative analysis of both technological and non-technological solutions to achieve net-zero GHG emissions and establish a carbon-neutral status in the chemical industry. The aim is to assess the potential role of AI as a tool for reducing CO2 emissions from the chemical industry and contributing to the global goal of achieving net-zero CO2 emissions. The analysis will evaluate the efficacy of AI as a tool in reducing GHG emissions in the chemical industry and explore its potential for optimising processes, predicting and reducing emissions, and supporting the development of sustainable practices. By utilising AI, it may be possible to identify and implement effective solutions that may not have been possible through conventional methods. Ultimately, the study aims to contribute to the ongoing efforts to address climate change by highlighting the potential of AI as a tool for reducing GHG emissions in the chemical industry. [ABSTRACT FROM AUTHOR]

The present stage of development of artificial intelligence (AI) is based on advanced technologies and modern methods and algorithms of machine learning (ML) including deep machine learning, intellectual data analysis and other fundamental scientific areas, which in turn play an important role in delivering applied solutions in almost all areas of digital economy. However, the corresponding expansion of the AI application range and complication of the class of problems, as well as of the spectrum and the volume of data used for creation of applied AI models and AI-based intelligent system call for a significant expansion of the theoretical and algorithmic basis of AI including development of ML methods that involve mathematical and physical models of objects and phenomena of domains, methods of fusion of multimodal data, methods for constructing geometric and topological components of deep neural networks, methods for modeling 3D objects, etc. In 2021, in order to cope well with these challenges, Skoltech Research Center for Applied Artificial Intelligence was created with support from the Federal Program "Artificial Intelligence." The aims of the Center are as follows: creation of a scientific and technological basis for solution of a wide range of relevant applied tasks for purposes of sustainable development of the economy of the Russian Federation including optimization problems of managerial decisions aimed at reduction of the carbon footprint and other urgent ESG tasks; environmental monitoring tasks for detection of anomalies and extreme situations forecast; assessment of economic and social risks and their dynamics caused by climatic changes; predictive analytic problems, etc. In the present paper, we describe new AI technologies, models, methods and algorithms developed in the Center, describe the main applied areas of research of the Center, and provide a briefly survey of the already developed scientific and applied results. [ABSTRACT FROM AUTHOR]

Despite Grewendorf's well-known German binding data with the double-object verb zeigen 'show', where one object reflexively binds the other and which suggests that the direct object (DO) is generated higher than the indirect object (IO), this paper argues for the canonical surface order of IO > DO as base order. We highlight the exceptional status of Grewendorf's examples, build on scope facts as well as a quantitative acceptability rating study, and exploit the fact that zeigen can also be used as inherently reflexive with idiomatic meaning. Appealing to the base configuration of the pieces of idiomatic expressions and considering different Spell-Out possibilities of coreferential objects in German, we show that the case, number, and gender underspecification of the anaphor sich poses a previously unnoticed problem for derivational approaches to binding. [ABSTRACT FROM AUTHOR]

• We proposed the conception of horticultural phenomics. • High-throughput aerospace-sky-ground phenotypic facilities in horticultural phenomics have also had the embryonic form and have been introduced, analyzed, distinguish systematically in this paper. • Horticultural phenomics towards multiscale, multidimension, multiperiod at present and is shown in this paper. • Application of plant phenomics in horticulture research have been summarized, such as omics research and horticultural breeding. With the development of advanced sensors, artificial intelligence, and computer vision, horticultural phenomics has made great progress in recent years. However, there is a lack of systematic reviews to discuss the horticultural phenomics and its applications at present. In this paper, we aim to fill this gap by providing a comprehensive review of the past developments, recent advances and existing challenges. Furthermore, we propose conception and give our prospects of high-throughput horticultural phenomics. We envision that horticultural phenomics will evolve towards multiscale, multidimension, multiperiod approach in the near future. This transformation will enable researchers to better realize the physiological processes, understand the genetic composition and develop new cultivars of horticultural plants that are better adapted to specific environments. Consequently, it will greatly promote the omics research in horticultural plants. In addition to advancing scientific research, high-throughput horticultural phenomics can also assist growers in efficient crop management, leading to improved yield and quality of horticultural products. However, it is important to acknowledge the challenges that need to be overcome. Once these challenges are addressed, high-throughput horticultural phenomics will become increasingly important in the fight against climate changes and maximizing profits. [ABSTRACT FROM AUTHOR]

• Agriculture is crucial for human development and depends heavily on irrigation. This paper presents an autonomous irrigation system using AI and 6G-IoT. • The system uses a prediction algorithm based on weather history data to anticipate rainfall patterns and climate changes. • It creates an intelligent system that irrigates the fields according to environmental conditions. • The developed system achieved an accuracy of 86.34%, sensitivity of 89.28%, and precision of 91%. Agriculture plays a vital role in the growth of humankind. The total world depends upon the agricultural contribution, and wherein the agriculture purely depends upon the necessity of irrigation. This paper develops an autonomous irrigation system using Artificial Intelligence (AI) and the 6G enabled Internet of Things (6G-IoT) transforming to create a hassle-free smart agriculture model. The developed autonomous irrigation system is purely based on prediction algorithm which is mount in a microprocessor, which uses weather history data using IoT to identify and predict rainfall patterns and any climatic changes; hence it creates an intelligent system which irrigates the field depending upon the outer environment. The developed system not only checks the environmental condition, but it also measures the moisture content of the soil to supply the required amount of water to the crops. The developed autonomous AI and 6G-IoT based smart agricultural irrigation system has been tested under control environment with an accuracy of 86.34%, sensitivity of 89.28% and precision of 91%. Hence, the proposed methodology could be the possible solution for autonomous irrigation of crops in future. [ABSTRACT FROM AUTHOR]

Crop planting area and spatial distribution information have important practical significance for food security, global change, and sustainable agricultural development. How to efficiently and accurately identify crops in a timely manner by remote sensing in order to determine the crop planting area and its temporal–spatial dynamic change information is a core issue of monitoring crop growth and estimating regional crop yields. Based on hundreds of relevant documents from the past 25 years, in this paper, we summarize research progress in relation to farmland vegetation identification and classification by remote sensing. The classification and identification of farmland vegetation includes classification based on vegetation index, spectral bands, multi-source data fusion, artificial intelligence learning, and drone remote sensing. Representative studies of remote sensing methods are collated, the main content of each technology is summarized, and the advantages and disadvantages of each method are analyzed. Current problems related to crop remote sensing identification are then identified and future development directions are proposed. [ABSTRACT FROM AUTHOR]

With the confluence of rapidly continuing economic development and increasing greenhouse gas emissions, the frequency and severity of natural disasters caused by global warming have increased in recent years. The international Financial Stability Board established the Task Force on Climate-Related Financial Disclosures (TCFD) to provide recommendations for collecting and disclosing climate-related information that is helpful for decision-making such that investors, decision-makers and supervision units can understand and more accurately assess climate-related risks as well as opportunities. However, the TCFD recommendations only provide a general framework of risks and opportunities from which companies cannot derive specific disaster-related data for regional climate disclosures. For stakeholders in different continents and regions, there is a need to develop a disaster data-driven model that can precisely disclose regional risks. Our study develops a regional climate framework for TCFD based on natural language processing that can process and analyze data from historical local news. The paper aims to offer suitable climate recommendations for different regions and suggest possible preventive measures and responses to emergencies caused by various disasters. The contributions of this research contain: (1) using unsupervised learning to present climate change in a quantitative way; (2) helping enterprises adjust sustainable strategies according to conditions in different regions; (3) extending involvement from a small area to a large area, quantifying climate data so that investors can make decisions based on factual results; (4) combining artificial intelligence with climate change in the field of TCFD. [Display omitted] • We develop a data-driven regional climate framework for TCFD based on deep learning. • This framework can help enterprises minimize risks along with costs and can be applied to other industries. • This research supplements quantitative information in TCFD and provides a scientific climate risk management method. • The first study that applies artificial intelligence to climate change in the field of TCFD. [ABSTRACT FROM AUTHOR]

The energy and climate crises have accelerated the decarbonization of electric power systems. An important part of this decarbonization process, along with the incorporation of renewable and alternative energies, is the emergence of Carbon-neutral, intelligent systems technologies, coupled with digital transformation. These have brought new dynamics to the electric power systems transformation. Significant amounts of renewable energy, massive power electronics, changes in market planning, policy influence, demand response, and emerging technologies all become the essential components of new power systems, bringing remarkable changes to this new power systems' enabling technologies: IntelliSense. A novel IntelliSense framework driven by new power system requirements is proposed. The development status, classification characteristics, the application of intelligent sensors, intelligent sensor flexible charging, and multisource plus multidimensional big data processing are all discussed in detail. They are interspersed with discussions of critical features of new power systems, the uncertainties inherent in big data about power, wireless power transfer technologies, edge-fog-cloud collaborative computing, intelligent algorithms, and other core elements of IntelliSense. Finally, ten core IntelliSense technology challenges and five major future opportunities are summarized and are expected to serve as a reference and spark new ideas for low-carbon power system development. • This paper summarizes the state-of-the-art research on IntelliSense in NPS. • A novel technology-driven framework is proposed for the key role of IntelliSense in NPS. • New features of NPS are discussed, as well as their impact on IntelliSense. • The challenges and future directions for IntelliSense of NPS are discussed. [ABSTRACT FROM AUTHOR]

Global climate change has led to large fluctuations in lake levels in recent years as meteorological and hydrological parameters have changed and water use has been intense. Water scientists use various computer models to analyze the hydrological variables recorded in the past and make projections for all future scenarios. Based on the technological progress, six different types of algorithms were studied in this review to predict the water level in lakes. The prediction results show that Deep Learning (DL) has the highest accuracy in terms of the evaluation metrics. Since the Artificial Intelligence (AI) field is still emerging and continue to improve, this study highlights better comprehension of current applications and the problems that need to be investigated more for LWL forecasting techniques. It reveals that the studies mainly focused on lakes either in USA or China and there is room for improvement for other locations that are scarcely investigated. • This paper presents a systematic literature review for lake and reservoir water level studies for the first time. • Researchers favored input parameters as lake water level, precipitation and temperature among others. • The studies can be extended towards the direction of least studied areas such as deep learning based algorithms. • Increasing water level prediction areas recently indicates it became a hot topic because of visible climate change effects. [ABSTRACT FROM AUTHOR]

• This work focuses on the PV power output forecasting using time series algorithms. • The input datasets are selected PV data from north region of South Korea. • Feature selection for time series algorithms is based on different seasons and climate changes. • PV power generation forecasting by different AI models has carried out on an hourly basis to test efficacy. • LSTM model shows the lowest error rate for quick PV power generation forecasting. The present photovoltaic (PV) power generation systems are globally facing the irregularity problem in the distribution of PV generation. In particular, the exact PV power forecasting is critical for grid-connected photovoltaic (PV) systems under unwanted changes in environmental circumstances. The grid energy management, grid operation and scheduling are important factors to forecast the PV power output. Time series analysis is one of the most important aspects of PV output prediction, especially in places (in South Korea) where past solar radiation data or other weather parameters have not been recorded. In this paper, a variety of time-series methods including deep-learning algorithm and machine learning algorithms was used to predict the PV power generation output for quick respond to equipment and panel defects. For designing AI models, the input data were characterized by dividing seasons and choosing the multiple parameters from seasons. In this study, the photovoltaic power generation data was collected from Ansan city, South Korea during January 2017 to June 2021 and the weather data was collected from Suwon city, South Korea during January 2017 to June 2021. In this work, approx. 40,000 hours of operation data from 1.5 MW grid-connected PV system in South Korea was used. PV power generation forecasting was carried out on an hourly basis to test efficacy of various models. Among all models (Holt-Winters, Multivariate Linear Regression, ARIMA, SARIMA, ARIMAX, SARIMAX), LSTM model presented the lowest error rate as compared to other models for quick PV power generation forecasting. [ABSTRACT FROM AUTHOR]

• A novel method for assessing prediction of extremal hydrological events. • Bayesian causal modelling tool for predictive assessment. • Hybridization of Classical and new Hydrology based on Artificial Intelligence. • HydroPredicT_Extreme as a new tool for the prediction of potential flooding events. • Applicability to different time scales of extreme and sudden events. Disastrous losses related to high-flow events have increased dramatically over the past decades largely due to an increase in flood-prone regions settlements and shift in hydrological trends largely due to Climate Change. To mitigate the societal impact of hydrological and hydraulic extremes, knowledge of the processes leading to these extreme events is vital. Hydrological modelling is one of the main tools in this quest for knowledge but comes with uncertainties. For that it is necessary to deeply study the impact of hydrological models' structure on the magnitude and timing of extreme rainfall-runoff events. This paper is mainly aimed to show the development of a method called "HydroPredicT_Extreme" based on Bayesian Causal Modelling (BCM), a technique within Artificial Intelligence (AI). This method may enhance predictive capacity of extreme rainfall-runoff events. "HydroPredicT_Extreme" follows an iterative methodology that comprise 2 main stages. First one comprises a mixed graphical/analytical method from Hydrograph. This stage is conditioned by two initial constraints which are, a) pluviometry station is representative of hydrograph downstream flow behaviour; b) there must be independence of events. This first stage comprises sub-phases such as: 1.1. Calculation of Response Time (RT) through a mixed graphical/analytical approach, 1.2 Subtraction of RT from the flow series to remove the Rainfall-Flow delay; 1.3 Calculation base flow rate; 1.4 Subtraction base-flow from flow series to work on absolute inputs. Second man stage is called Bayesian Causal Modelling Translation (BCMT) that comprises the 2.1 Learning, 2.2 Training, 2.3 Simulation through BCM modelling, 2.4 Sensitivity Analysis-Validation. This whole methodology will become a digital application and software that could be extrapolated to several similar case studies. This may be coupled with posterior devices for the prevention of catastrophic flood consequences in the form of MultiHazard-Early Warning System (MH-EWS) or others. [ABSTRACT FROM AUTHOR]

Our understanding of the time variability of intense cyclones in the Mediterranean region is still lacking despite its importance for the long-term forecast of climate anomalies. This study examines the month-to-month variability and predictability of cyclones, the intensity of which exceeded the 75th percentile (intense cyclones) and the 95th percentile (extreme cyclones), over the Western and Eastern Mediterranean. The locations of cyclones were obtained by applying the method of M. Yu. Bardin on the 6-hourly 1000 hPa geopotential height data from the NCEP/NCAR reanalysis for the period 1951–2017 (67 years). It was shown that annual frequencies of cyclones were higher in the Western Mediterranean due to the contribution of spring and autumn; monthly averages were higher in the Eastern Mediterranean in December/January–March for intense/extreme cyclones. In the context of global warming, no linear trends significant at the 90% confidence level were found in the variability of intense and extreme cyclones, except for a positive trend in autumn extreme cyclones over the Eastern Mediterranean. The time series of cyclones in both parts of the Mediterranean were characterized by a pronounced interannual variability with a noticeable decadal modulation. According to spectral analysis, these interannual periods were multiples of 2–3 years corresponding to the main global teleconnection patterns. Seasonally, the most energy was concentrated in winter spectra; spring and autumn spectra had lower comparable magnitudes. The correlation analysis between the frequency of cyclones and the indices of the main atmospheric patterns showed that the main synchronous patterns for intense and extreme Mediterranean cyclones in September–April were the Mediterranean Oscillation (with the opposite signs for the Western and Eastern Mediterranean), Scandinavia pattern (positive correlation), and East Atlantic Oscillation (negative correlation). Additional important synchronous teleconnection patterns for some months were the Arctic Oscillation and East Atlantic/West Russia pattern for the Western Mediterranean, and the Polar/Eurasia pattern and Tropical Northern Hemisphere pattern for the Eastern Mediterranean. The outcome of this paper was the use of an artificial neural network model with inputs of global teleconnection indices both in the atmosphere and ocean to describe the temporal variability of the frequency of intense cyclones in the Western and Eastern Mediterranean. The predictability of intense cyclones was shown with the possibility of forecasts with a lead time of 0, 2, 4, and 6 months for the Western Mediterranean in October, January, February, April, and May, and for the Eastern Mediterranean in January, February, March, April, and May. One of the applications of this model may be in forecasting the evolution of the monthly frequency of cyclones with a lead time of 2 to 6 months. [ABSTRACT FROM AUTHOR]

Current climate change threats and increasing CO2 emissions, especially from the building stock, represent a context where action is required. It is necessary to provide efficient manners to manage energy demand in buildings and contribute to a decarbonised future. By combining new technologies, such as artificial intelligence, Internet of things, blockchain, and the exploitation of big data towards solving real life problems, the way could be paved towards smart and energy-aware buildings. In this context, the aim of this paper is to present a critical review and an in-detail definition of the big data value chain for the built environment in Europe, covering multiple needs and perspectives: "policy", "technology" and "business", in order to explore the main challenges and opportunities in this area. [ABSTRACT FROM AUTHOR]