Nausheen Saeed

Social media platforms have become central arenas for climate communication, shaping how climate-related issues are discussed, interpreted, and contested. While previous studies have examined online responses to predefined climate events, less is known about which types of events generate the strongest shifts in climate-related sentiment. This study investigates the drivers of climate sentiment on Twitter/X using more than 100,000 climate-related tweets posted between 2015 and 2026. The study applies a discourse-centred analytical framework combining sentiment analysis, event detection, external event validation, and topic modelling. Twitter-RoBERTa was used to construct a daily sentiment time series, from which 244 candidate sentiment peaks were identified. The ten highest-impact peaks were then compared with documented climate disasters and international climate policy events and analysed using LDA topic modelling. The results show that major sentiment shifts were only weakly associated with documented climate disasters and COP-related policy events. Instead, political actors, political debate, climate skepticism, media narratives, and public controversy were more prominent drivers of sentiment change. Topic analysis further showed that physical climate impacts were present in the wider discussion context but were rarely the dominant focus. The findings highlight the central role of political and media dynamics in shaping online climate sentiment.

Climate change poses escalating risks to environmental, economic, and social systems worldwide, making accurate temperature forecasting a critical component of climate impact assessment and mitigation planning. Advances in data-driven modelling have expanded the range of tools available for analysing climate time series, complementing traditional statistical approaches. The continued increase in global surface temperatures, driven primarily by anthropogenic greenhouse gas (GHG) emissions, underscores the need for forecasting models capable of capturing complex and non-linear climate dynamics. This study compares the predictive performance of a Long Short-Term Memory (LSTM) neural network with a Seasonal Autoregressive Integrated Moving Average (SARIMA) model using historical global temperature data. The results show that LSTM outperforms SARIMA at the global scale, achieving an R² of 0.9846, RMSE of 0.1528 °C, and MAE of 0.1198 °C, representing a 50.7% reduction in error relative to the SARIMA baseline (R² = 0.9364; RMSE = 0.3100 °C). However, regional analyses reveal heterogeneous performance, with LSTM overestimating seasonal variability in certain regions, while SARIMA exhibits greater local stability. Sectoral emission analysis identifies agriculture and energy production as the dominant global contributors, with substantial regional variation. These findings suggest that hybrid modelling approaches may offer improved robustness for regional climate assessment and policy applications.

Road maintenance agencies subjectively assess loose gravel as one of the parameters for determining gravel road conditions. This study aims to evaluate the performance of deep learning-based pre-trained networks in rating gravel road images according to classical methods as done by human experts. The dataset consists of images of gravel roads extracted from self-recorded videos and images extracted from Google Street View. The images were labelled manually, referring to the standard images as ground truth defined by the Road Maintenance Agency in Sweden (Trafikverket). The dataset was then partitioned in a ratio of 60:40 for training and testing. Various pre-trained models for computer vision tasks, namely Resnet18, Resnet50, Alexnet, DenseNet121, DenseNet201, and VGG-16, were used in the present study. The last few layers of these models were replaced to accommodate new image categories for our application. All the models performed well, with an accuracy of over 92%. The results reveal that the pre-trained VGG-16 with transfer learning exhibited the best performance in terms of accuracy and F1-score compared to other proposed models.

Road condition evaluation is a critical part of gravel road maintenance. One of the assessed parameters is the amount of loose gravel, as this determines the driving quality and safety. Loose gravel can cause tires to slip and the driver to lose control. An expert assesses the road conditions subjectively by looking at images and notes. This method is labor-intensive and subject to error in judgment; therefore, its reliability is questionable. Road management agencies look for automated and objective measurement systems. In this study, acoustic data on gravel hitting the bottom of a car was used. The connection between the acoustics and the condition of loose gravel on gravel roads was assessed. Traditional supervised learning algorithms and convolution neural network (CNN) were applied, and their performances are compared for the classification of loose gravel acoustics. The advantage of using a pre-trained CNN is that it selects relevant features for training. In addition, pre-trained networks offer the advantage of not requiring days of training or colossal training data. In supervised learning, the accuracy of the ensemble bagged tree algorithm for gravel and non-gravel sound classification was found to be 97.5%, whereas, in the case of deep learning, pre-trained network GoogLeNet accuracy was 97.91% for classifying spectrogram images of the gravel sounds.