Hydrological extremes are common throughout the world and can be considered a globally significant phenomenon with severe environmental and social consequences. In recent decades, especially in the second half of the 20th century, Extreme Hydrological Events (EHEs) have attracted extensive attention. Physiological and anthropogenic factors have increased the frequency and severity of hydrological extremes worldwide in the last few decades. Recently, it has become a significant environmental issue in Sri Lanka. Both floods and droughts are widespread throughout the country, and the influence of floods is becoming more common every year. Currently, the frequency and severity of EHEs in the Kelani River Basin (KRB), Sri Lanka, are very common and have increased due to climate variations. Therefore, this study focused mainly on evaluating the EHEs and the impact of long-term El Niño Southern Oscillation (ENSO), Sea Surface Temperature (SST), and Indian Ocean Dipole (IOD) dynamics on extreme events. Rainfall Anomaly Index (RAI) and Extreme Precipitation Indices (EPIs) were calculated to examine the EHEs and their spatial variability. In addition, the relationships between EHEs and ENSO were investigated using several climate indices based on SST anomalies. Both observed and satellite-derived daily precipitation from 1951 to 2019 were used to assess the EHEs in the KRB. The trend of EHEs and the change points were evaluated using the Pettitt test, and teleconnection with global indices was examined using the correlation coefficient in the R application. The result of the study revealed that the pattern of EHEs varied spatially from 1951 to 2019. The strong La Niña years showed a high degree of teleconnection with EHEs in April (r = 0.622 at 0.05 significance level) and August (r = −0.732 at 0.05 significance level). NINO3.4 and the Southern Oscillation Index (SOI) have shown a significant positive impact on EHEs in the Northeast Monsoon (NEM) period. This research on KRB will be a popular scientific measure that can provide scientific results and solutions for the comprehensive decision-making process in the future. Investigating the global physical changes that influence EHEs is critical to taking the necessary steps to reduce the severity of hydrological extremes in Sri Lanka.

Based on User Generated Content (UGC) data, namely Unmanned Aerial Vehicle (UAV) photos and corresponding social media captions, this paper uses the methods of AI cognitive analysis and GIS spatial analysis to profile UAV photography tourists. The findings of this study reveal that UAV photography tourists primarily take pictures of natural landscapes, most of which do not contain humans, concentrated in the coastal regions. Additionally, a sentiment analysis indicates that tourists’ opinions of photography are typically positive.

Recent terrorist attacks have emerged as a formidable menace to global peace and security, giving rise to an acute humanitarian and economic crisis characterized by the loss of numerous lives and the incurring of substantial financial damages. In response to this pressing concern, the scholarly community has introduced a range of AI-driven predictive analytics methodologies as prospective instruments in the fight against terrorism. Machine Learning (ML) techniques, commonly employed in counter-terrorism efforts, face the formidable challenge of accurately forecasting terrorism activities due to the escalating complexity and voluminous nature of the underlying data. In this context, we present a solution in the form of an optimal weighted voting ensemble classifier, specially tailored for the classification of weapon types in terrorist attacks. Leveraging the computational capabilities of the Particle Swarm Optimization (PSO) algorithm, we determine the optimal weight assignments for the base learners, namely Random Forest and Xtreme Gradient Boosted Machines. Additionally, we developed several machine learning model crafted to predict the likelihood of casualties in the aftermath of a terrorist incident. Rigorous validation of these models is carried out utilizing the publicly available Global Terrorism Database (GTD), necessitating meticulous data preprocessing to rectify anomalies and address class imbalances, a task effectively accomplished through the application of the Synthetic Minority Over-sampling Technique (SMOTE). To underscore the effectiveness of our proposed model, we conduct an extensive comparative analysis, bench-marking it against state-of-the-art machine learning models. The comprehensive experimental results unequivocally demonstrate the superiority of our models, as they consistently achieve the lowest error rates, thereby highlighting their enhanced generalizability and performance. The findings derived from our study furnish invaluable insights to empower counter-terrorism agencies, facilitating data-driven decision-making and the proactive implementation of measures to mitigate and counteract the scourge of terrorist acts.

Collection and delivery points are an alternative to home delivery and represent an important opportunity to reduce delivery failures in urban areas. As online shopping has become increasingly popular, different accessibility modes such as walking, cycling, and driving are considered for the collection of parcels at collection and delivery points (CDPs). The primary objective of the present study was to assess the spatial variability and accessibility of CDPs in Nanjing City, China. The point of interest (POI) data of 1224 CDPs (including 424 China Post Stations and 800 Cainiao Stations), and population and gross domestic product data were employed for the spatial analysis. The results showed that China Post Stations and Cainiao Stations were distributed in Nanjing as clusters at α = 0.01. Both types (51.1% China Post Stations and 63.2% Cainiao Stations) of CDPs were aggregated in the high population density areas. Moreover, 28.0% of China Post Stations and 50.9% of Cainiao Stations were located in high GDP density areas. The overall spatial distribution of China Post Stations in population and GDP density areas was medium, while that of the Cainiao Stations was high. There was a significant correlation between the spatial distribution of the CDPs, population, and GDP. There were significant spatial accessibility differences to CDPs among different accessibility modes like walking, cycling, and driving. Walking and cycling mode accessibility to China Post Stations and Cainiao Stations were 13.8 and 25.3% and 9.2 and 28.9%, respectively while 71.8% of China Post Stations and 71.1% of Cainiao Stations were accessed by driving. The findings of this study would be beneficial for policymakers and practitioners to develop related policies, to assist companies in building up more sustainable urban logistics and a booming CDPs’ network in the future.