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The purpose of the paper is to predict mapping of areas vulnerable to flooding in the Ourika watershed in the High Atlas of Morocco with the aim of providing a useful tool capable of helping in the mitigation and management of floods in the associated region, as well as Morocco as a whole.

Four machine learning (ML) algorithms including k-nearest neighbors (KNN), artificial neural network, random forest (RF) and x-gradient boost (XGB) are adopted for modeling. Additionally, 16 predictors divided into categorical and numerical variables are used as inputs for modeling.

The results showed that RF and XGB were the best performing algorithms, with AUC scores of 99.1 and 99.2%, respectively. Conversely, KNN had the lowest predictive power, scoring 94.4%. Overall, the algorithms predicted that over 60% of the watershed was in the very low flood risk class, while the high flood risk class accounted for less than 15% of the area.

There are limited, if not non-existent studies on modeling using AI tools including ML in the region in predictive modeling of flooding, making this study intriguing.

The present study aims to evaluate the effect of climate change on the flood hazard potential in the Kelantan River Basin using current and future scenarios.

The intensity-duration-frequency (IDF) was used to estimate the current 50- and 100-year return period 24-h design rainfall, and the climate change factor (CCF) was used to compute the future design rainfall. The CCF was calculated from the rainfall projections of two global climate models, CGCM1 and CCSM3, with different pre-processing steps applied to each. The IDF data were used in the rainfall-runoff-inundation model to simulate current and future flood inundation scenarios.

The estimated CCF values demonstrate a contrast, whereby each station had a CCF value greater than one for CGCM1, while some stations had a CCF value of less than one for CCSM3. Therefore, CGCM1 projected an aggravation and CCSM3 a reduction of flood hazard for future scenarios. The study reveals that topography plays an essential role in calculating the CCF.

To the best of the author’s knowledge, this is the first study to examine flood projections in the Kelantan River Basin. It is, therefore, hoped that these results could benefit local managers and authorities by enabling them to make informed decisions regarding flood risk mitigation in a climate change scenario.

In an era overshadowed by the alarming consequences of climate change and the escalating peril of recurring floods for communities worldwide, the significance of proficient disaster risk management has reached unprecedented levels. The successful implementation of disaster risk management necessitates the ability to make informed decisions. To this end, the utilization of three-dimensional (3D) visualization and Web-based rendering offers decision-makers the opportunity to engage with interactive data representations. This study aims to focus on Thiruvananthapuram, India, where the analysis of flooding caused by the Karamana River aims to furnish valuable insights for facilitating well-informed decision-making in the realm of disaster management.

This work introduces a systematic procedure for evaluating the influence of flooding on 3D building models through the utilization of Web-based visualization and rendering techniques. To ensure precision, aerial light detection and ranging (LiDAR) data is used to generate accurate 3D building models in CityGML format, adhering to the standards set by the Open Geospatial Consortium. By using one-meter digital elevation models derived from LiDAR data, flood simulations are conducted to analyze flow patterns at different discharge levels. The integration of 3D building maps with geographic information system (GIS)-based vector maps and a flood risk map enables the assessment of the extent of inundation. To facilitate visualization and querying tasks, a Web-based graphical user interface (GUI) is developed.

The efficiency of comprehensive 3D building maps in evaluating flood consequences in Thiruvananthapuram has been established by the research. By merging with GIS-based vector maps and a flood risk map, it becomes possible to scrutinize the extent of inundation and the affected structures. Furthermore, the Web-based GUI facilitates interactive data exploration, visualization and querying, thereby assisting in decision-making.

The study introduces an innovative approach that merges LiDAR data, 3D building mapping, flood simulation and Web-based visualization, which can be advantageous for decision-makers in disaster risk management and may have practical use in various regions and urban areas.

Inland shipping has been extensively recognised as a sustainable, efficient and good alternative to rail and road modes of transportation. In recent years, various authorities and academic researchers have advocated shifting from road to other sustainable modes like inland waterway transport (IWT) or rail transport. Academic work on modernisation and technological innovations to enhance the effectiveness and efficiency of waterborne transportation is becoming apparent as a growing body of literature caused by the need to achieve a sustainable transport system. Thus, it became apparent to explore the research trends on IWT.

A systematic and structured literature review study was employed in this paper to identify the challenges and concepts in modernising inland waterways for freight transportation. The review analysed 94 articles published in 54 journals from six well-known databases between 2010 and 2022.

The key findings of this review are that despite various challenges confronting the sector, there have been successful cases of technological advancement in the industry. The main interest among scholars is improving technical and economic performance, digitalisation, and safety and environmental issues. The review revealed that most of the literature is fragmented despite growing interest from practitioners and academic scholars. Academic research to address the strategic objectives, including strengthening competitiveness (shipbuilding, hydrodynamics, incorporating artificial intelligence into the decision-making process, adopting blockchain technology to ensure transparency and security in the transactions, new technologies for fleets adaptation to climate change, more effective handling, maintenance and rehabilitation technologies), matching growth and changing trade patterns (intermodal solutions and new logistics approaches) are major causes of concerns.

By employing the approach of reviewing previously available literature on IWT review papers, this review complements the existing body of literature in the field of IWT by providing in a single paper a consolidation of recent state-of-the-art research on technological developments and challenges for inland waterways freight transport in the intermodal supply chain that can act as a single resource to keep researchers up to date with the most recent advancements in research in the domain of inland waterway freight transport. Additionally, this review identified gaps in the literature that may inspire new research themes in the field of IWT.

This paper aims to analyse the persistence of the S&P500 and DAX 30 stock indices as well as of the Fed’s Effective Federal Funds rate and of the European Central Bank’s Marginal Lending Facility rate, and the long-run linkages between stock prices and interest rates in the USA and Europe, respectively.

The methodology is based on the concepts of fractional integration and cointegration.

Using monthly data from January 1999 to December 2022, the results can be summarised as follows. All series examined are non-stationary: stock prices are found to be I(1) while interest rates display orders of integration substantially above 1, which implies a rejection of the hypothesis of mean reversion in all cases examined.

This paper uses an appropriate econometric framework to obtain new, reliable empirical evidence. All four series are highly persistent, and mean reversion does not occur in any single case. Moreover, the fractional cointegration analysis suggests that stock prices and interest rates are not linked in the long run.

This research is aimed at preparing landslide susceptibility using spatial analysis and soft computing machine learning techniques based on convolutional neural networks (CNNs), artificial neural networks (ANNs) and logistic regression (LR) models.

Using the Geographical Information System (GIS), a spatial database including topographic, hydrologic, geological and landuse data is created for the study area. The data are randomly divided between a training set (70%), a validation (10%) and a test set (20%).

The validation findings demonstrate that the CNN model (has an 89% success rate and an 84% prediction rate). The ANN model (with an 84% success rate and an 81% prediction rate) predicts landslides better than the LR model (with a success rate of 82% and a prediction rate of 79%). In comparison, the CNN proves to be more accurate than the logistic regression and is utilized for final susceptibility.

Land cover data and geological data are limited in largescale, making it challenging to develop accurate and comprehensive susceptibility maps.

It helps to identify areas with a higher likelihood of experiencing landslides. This information is crucial for assessing the risk posed to human lives, infrastructure and properties in these areas. It allows authorities and stakeholders to prioritize risk management efforts and allocate resources more effectively.

The social implications of a landslide susceptibility map are profound, as it provides vital information for disaster preparedness, risk mitigation and landuse planning. Communities can utilize these maps to identify vulnerable areas, implement zoning regulations and develop evacuation plans, ultimately safeguarding lives and property. Additionally, access to such information promotes public awareness and education about landslide risks, fostering a proactive approach to disaster management. However, reliance solely on these maps may also create a false sense of security, necessitating continuous updates and integration with other risk assessment measures to ensure effective disaster resilience strategies are in place.

Landslide susceptibility mapping provides a proactive approach to identifying areas at higher risk of landslides before any significant events occur. Researchers continually explore new data sources, modeling techniques and validation approaches, leading to a better understanding of landslide dynamics and susceptibility factors.

The study aims to identify the possible risk factors for electricity grids operational disruptions and to determine the most critical and influential risk indicators.

A multi-criteria decision-making best-worst method (BWM) is employed to quantitatively identify the most critical risk factors. The grey causal modeling (GCM) technique is employed to identify the causal and consequence factors and to effectively quantify them. The data used in this study consisted of two types – quantitative periodical data of critical factors taken from their respective government departments (e.g. Indian Meteorological Department, The Central Water Commission etc.) and the expert responses collected from professionals working in the Indian electric power sector.

The results of analysis for a case application in the Indian context shows that temperature dominates as the critical risk factor for electrical power grids, followed by humidity and crop production.

The study helps to understand the contribution of factors in electricity grids operational disruptions. Considering the cause consequences from the GCM causal analysis, rainfall, temperature and dam water levels are identified as the causal factors, while the crop production, stock prices, commodity prices are classified as the consequence factors. In practice, these causal factors can be controlled to reduce the overall effects.

From the results of the analysis, managers can use these outputs and compare the risk factors in electrical power grids for prioritization and subsequent considerations. It can assist the managers in efficient allocation of funds and manpower for building safeguards and creating risk management protocols based on the severity of the critical factor.

The research comprehensively analyses the risk factors of electrical power grids in India. Moreover, the study apprehends the cause-consequence pair of factors, which are having the maximum effect. Previous studies have been focused on identification of risk factors and preliminary analysis of their criticality using autoregression. This research paper takes it forward by using decision-making methods and causal analysis of the risk factors with blend of quantitative and expert response based data analysis to focus on the determination of the criticality of the risk factors for the Indian electric power grid.

The purpose of this research is to provide a theoretical and practical theory and application that provides understanding and means to manage complex infrastructures.

In this research, complexity, nonlinear, noncontinuous effects and aleatoric and data unknowns are bypassed by directly addressing systems' responses. Graph theory, statistics and digital signal processing (DSP) tools are applied within a theoretical framework of the theory of faults (ToF). Motivational complex infrastructure systems (CISs) are difficult to model. Data are often missing or erroneous, changes are not well documented and processes are not well understood. On top of it, under complexity, stalwart analytical tools have limited predictive power. The aleatoric risk, such as rain and risk cascading from interconnected infrastructures, is unpredictable. Mitigation, response and recovery efforts are adversely affected.

The theory and application are presented and demonstrated by a step-by-step development of an application to a municipal drainage system. A database of faults is analyzed to produce system statistics, spatio-temporal morphology, behavior and traits. The gained understanding is compared to the physical system's design and to its modus operandi. Implications for design and maintenance are inferred; DSP tools to manage the system in real time are developed.

Sociological systems are interest driven. Some events are intentionally created and directed to the benefit and detriment of the opposing parties in a project. Those events may be explained and possibly predicted by understanding power plays, not power functions. For those events, sociological game theories provide better explanatory value than mathematical gain theories.

The theory provides a thematic network for modeling and resolving aleatoric uncertainty in engineering and sociological systems. The framework may be elaborated to fields such as energy, healthcare and critical infrastructure.

ToF provides a framework for the modeling and prediction of faults generated by inherent aleatoric uncertainties in social and technological systems. Therefore, the framework and theory lay the basis for automated monitoring and control of aleatoric uncertainties such as mechanical failures and human errors and the development of mitigation systems.

The contribution of this research is in the provision of an explicatory theory and a management paradigm for complex systems. This theory is applicable to a wide variety of fields from facilities and construction project management to maintenance and from academic studies to commercial use.

The choice of crops to produce at a location depends to a large degree on the climate. As the climate changes and food demand evolves, farmers may need to produce a different mix of crops. This study assesses how much cropland may be subject to such upheavals at the global scale, and then focuses on China as a case study to examine how spatial heterogeneity informs different contexts for adaptation within a country.

A global agricultural economic model is linked to a cropland allocation algorithm to generate maps of cropland distribution under historical and future conditions. The mix of crops at each location is examined to determine whether it is likely to experience a major shift.

Two-thirds of rainfed cropland and half of irrigated cropland are likely to experience substantial upheaval of some kind.

This analysis helps establish a global context for the local changes that producers might face under future climate and socioeconomic changes. The scale of the challenge means that the agricultural sector needs to prepare for these widespread and diverse upheavals.

In this paper, we aim to provide an extensive analysis to understand how various factors influence electricity prices in competitive markets, focusing on the day-ahead electricity market in Romania.

Our study period began in January 2019, before the COVID-19 pandemic, and continued for several months after the onset of the war in Ukraine. During this time, we also consider other challenges like reduced market competitiveness, droughts and water scarcity. Our initial dataset comprises diverse variables: prices of essential energy sources (like gas and oil), Danube River water levels (indicating hydrological conditions), economic indicators (such as inflation and interest rates), total energy consumption and production in Romania and a breakdown of energy generation by source (coal, gas, hydro, oil, nuclear and renewable energy sources) from various data sources. Additionally, we included carbon certificate prices and data on electricity import, export and other related variables. This dataset was collected via application programming interface (API) and web scraping, and then synchronized by date and hour.

We discover that the competitiveness significantly affected electricity prices in Romania. Furthermore, our study of electricity price trends and their determinants revealed indicators of economic health in 2019 and 2020. However, from 2021 onwards, signs of a potential economic crisis began to emerge, characterized by changes in the normal relationships between prices and quantities, among other factors. Thus, our analysis suggests that electricity prices could serve as a predictive index for economic crises. Overall, the Granger causality findings from 2019 to 2022 offer valuable insights into the factors driving energy market dynamics in Romania, highlighting the importance of economic policies, fuel costs and environmental regulations in shaping these dynamics.

We combine principal component analysis (PCA) to reduce the dataset’s dimensionality. Following this, we use continuous wavelet transform (CWT) to explore frequency-domain relationships between electricity price and quantity in the day-ahead market (DAM) and the components derived from PCA. Our research also delves into the competitiveness level in the DAM from January 2019 to August 2022, analyzing the Herfindahl-Hirschman index (HHI).