In the face of mounting global challenges stemming from population growth and climate fluctuations, the sustainable management of water resources emerges as a paramount concern. This scientific endeavor casts its gaze upon the Upper Euphrates basin, homing in on the Tunceli Munzur water sub-basin and the Sakarya Basin’s Kütahya Porsuk Stream Beşdeğirmen rivers. The investigation unfolds through the intricate analysis of daily average flow data, total daily precipitation, and daily average air temperature values, with the objective of unraveling the complexities of future water potential estimation. Central to our exploration are a series of well-established techniques including linear regression (LR), support vector regression (SVR), decision tree (DT), random forest (RF), and extra trees regression (ETR). We employ these methodologies diligently to decipher patterns woven within the dataset, fostering an informed understanding of water dynamics. To ascend the pinnacle of estimation accuracy, we introduce a groundbreaking hybrid approach, wherein the enigmatic wavelet transform (WT) technique assumes a pivotal role. Through systematic stratification of our dataset into training, validation, and test sets, comprising roughly 65%, 15%, and 20% of the data, respectively, a comprehensive experiment takes shape. Our results unveil the formidable performance of the ETR method, achieving a striking 88% estimation accuracy for the Porsuk Stream Beşdeğirmen, while the RF method garners a commendable 85.2% success rate for the Munzur water Melekbahçe. The apex of innovation unfolds within our hybrid model, a harmonious fusion of methodologies that transcends their individual capacities. This composite entity elevates estimation success rates by a remarkable 20% for the Munzur water Melekbahçe and an appreciable 11% for the Porsuk Stream Beşdeğirmen. This amalgamation culminates in an extraordinary overall success rate of 97.7%. Our findings transcend mere insights, resonating as guiding beacons for navigating the intricate maze of water resource management in an era marked by uncertainties. This study underscores the indispensability of advanced mathematical paradigms and machine learning frontiers, fortifying the bedrock of sustainable water resource management for the generations to come. By harnessing the fusion of federated learning and a constellation of innovative techniques, we endeavor to illuminate the path towards deciphering the complex tapestry of water resource estimation and management, facilitating a resilient and enduring aquatic world.

Diagnosing and screening for diabetic retinopathy is a well-known issue in the biomedical field. A component of computer-aided diagnosis that has advanced significantly over the past few years as a result of the development and effectiveness of deep learning is the use of medical imagery from a patient’s eye to identify the damage caused to blood vessels. Issues with unbalanced datasets, incorrect annotations, a lack of sample images, and improper performance evaluation measures have negatively impacted the performance of deep learning models. Using three benchmark datasets of diabetic retinopathy, we conducted a detailed comparison study comparing various state-of-the-art approaches to address the effect caused by class imbalance, with precision scores of 93%, 89%, 81%, 76%, and 96%, respectively, for normal, mild, moderate, severe, and DR phases. The analyses of the hybrid modeling, including CNN analysis and SHAP model derivation results, are compared at the end of the paper, and ideal hybrid modeling strategies for deep learning classification models for automated DR detection are identified.

With the rapid development in technology in recent years, the use of cameras and the production of video and image data have similarly increased. Therefore, there is a great need to develop and improve video surveillance techniques to their maximum extent, particularly in terms of their speed, performance, and resource utilization. It is challenging to accurately detect anomalies and increase the performance by minimizing false positives, especially in crowded and dynamic areas. Therefore, this study proposes a hybrid video anomaly detection model combining multiple machine learning algorithms with pixel-based video anomaly detection (PBVAD) and frame-based video anomaly detection (FBVAD) models. In the PBVAD model, the motion influence map (MIM) algorithm based on spatio–temporal (ST) factors is used, while in the FBVAD model, the k-nearest neighbors (kNN) and support vector machine (SVM) machine learning algorithms are used in a hybrid manner. An important result of our study is the high-performance anomaly detection achieved using the proposed hybrid algorithms on the UCF-Crime data set, which contains 128 h of original real-world video data and has not been extensively studied before. The AUC performance metrics obtained using our FBVAD-kNN algorithm in experiments were averaged to 98.0%. Meanwhile, the success rates obtained using our PBVAD-MIM algorithm in the experiments were averaged to 80.7%. Our study contributes significantly to the prevention of possible harm by detecting anomalies in video data in a near real-time manner.

Thunderstorms are weather phenomena that comprise thunder and lightning. They typically result in heavy precipitation, including rain, snow, and hail. Thunderstorms have adverse effects on flight at both the ground and the upper levels of the troposphere. The characteristics of the thunderstorm of Istanbul International Airport (International Civil Aviation Organization (ICAO) code: LTFM) have been investigated because it is currently one of the busiest airports in Europe and the seventh-busiest airport in the world. Geopotential height (m), temperature (°C), dewpoint temperature (°C), relative humidity (%), mixing ratio (g kg⁻¹), wind direction (°), and wind speed (knots) data for the ground level and upper levels of the İstanbul radiosonde station were obtained from the Turkish State Meteorological Service (TSMS) for 29 October 2018 and 1 January 2023. Surface data were regularly collected by the automatic weather stations near the runway and the upper-level data were collected by the radiosonde system located in the Kartal district of İstanbul. Thunderstorm statistics, stability indices, and meteorological variables at the upper levels were evaluated for this period. Thunderstorms were observed to be more frequent during the summer, with a total of 51 events. June had the highest number of thunderstorm events with a total of 32. This averages eight events per year. A total of 72.22% occurred during trough and cold front transitions. The K index and total totals index represented the thunderstorm events better than other stability indices. In total, 75% of the thunderstorm days were represented by these two stability indices. The results are similar to the covering of this area: the convective available potential energy (CAPE) values which are commonly used for atmospheric instability are low during thunderstorm events, and the K and total totals indices are better represented for thunderstorm events. This study investigates thunderstorm events at the LTFM, providing critical insights into aviation safety and operational efficiency. The research aims to improve flight planning, reduce weather-related disruptions, and increase safety and also serves as a reference for airports with similar climatic conditions.