Full-complexity Earth system models (ESMs) are computationally very expensive, limiting their use in exploring the climate outcomes of multiple emission pathways. More efficient emulators that approximate ESMs can directly map emissions onto climate outcomes, and benchmarks are being used to evaluate their accuracy on standardized tasks and datasets. We investigate a popular benchmark in data-driven climate emulation, ClimateBench, on which deep learning-based emulators are currently achieving the best performance. We implement a linear regression-based emulator, akin to pattern scaling, and find that it outperforms the incumbent 100M-parameter deep learning foundation model, ClimaX, on 3 out of 4 regionally-resolved surface-level climate variables. While emulating surface temperature is expected to be predominantly linear, this result is surprising for emulating precipitation. We identify that this outcome is a result of high levels of internal variability in the benchmark targets. To address internal variability, we update the benchmark targets with ensemble averages from the MPI-ESM1.2-LR model that contain 50 instead of 3 climate simulations per emission pathway. Using the new targets, we show that linear pattern scaling continues to be more accurate on temperature, but can be outperformed by a deep learning-based model for emulating precipitation. We publish our code, data, and an interactive tutorial at github.com/blutjens/climate-emulator.

Road traffic congestion prediction is a crucial component of intelligent transportation systems, since it enables proactive traffic management, enhances suburban experience, reduces environmental impact, and improves overall safety and efficiency. Although there are several public datasets, especially for metropolitan areas, these datasets may not be applicable to practical scenarios due to insufficiency in the scale of data (i.e. number of sensors and road links) and several external factors like different characteristics of the target area such as urban, highways and the data collection location. To address this, this paper introduces a novel IBB Traffic graph dataset as an alternative benchmark dataset to mitigate these limitations and enrich the literature with new geographical characteristics. IBB Traffic graph dataset covers the sensor data collected at 2451 distinct locations. Moreover, we propose a novel Road Traffic Prediction Model that strengthens temporal links through feature engineering, node embedding with GLEE to represent inter-related relationships within the traffic network, and traffic prediction with ExtraTrees. The results indicate that the proposed model consistently outperforms the baseline models, demonstrating an average accuracy improvement of 4%.

During the last week of July, Russia mounted its largest assaults in eight months in eastern Ukraine's Donetsk region, seizing a string of settlements in an apparent bid to cut off key supply routes and force a mass Ukrainian retreat. At the same time, Ukraine scored a high number of hits on Russian energy infrastructure and occupied Crimea, suggesting that its strategy of degrading Russian air defences is working. Russian assaults focused on central and southern Donetsk – from areas west of Bakhmut, which fell in May last year, to areas west of Avdiivka, which was lost in February, down to areas west of the city of Donetsk, which pro-Moscow separatists have controlled since 2014 – a line about 130km (80 miles) long. Russian forces have pressed their advantage in these areas to prevent Ukraine from digging entrenched defences, and they have inched forward for months, swallowing settlements at a staggering cost to their own troops. British military intelligence estimated that Russian casualties in May and June reached record daily highs of about 1,200 – about 70,000 soldiers for just those two months.

Elasmobranchs (sharks and rays) can be important components of marine ecosystems but are experiencing global population declines. Effective monitoring of these populations is essential to their protection. Baited Remote Underwater Video Stations (BRUVS) have been a key tool for monitoring, but require time-consuming manual analysis. To address these challenges, we developed SharkTrack, an AI-enhanced BRUVS analysis software. SharkTrack uses Convolutional Neural Networks and Multi-Object Tracking to detect and track elasmobranchs and provides an annotation pipeline to manually classify elasmobranch species and compute MaxN, the standard metric of relative abundance. We tested SharkTrack on BRUVS footage from locations unseen by the model during training. SharkTrack computed MaxN with 89% accuracy over 207 hours of footage. The semi-automatic SharkTrack pipeline required two minutes of manual classification per hour of video, a 97% reduction of manual BRUVS analysis time compared to traditional methods, estimated conservatively at one hour per hour of video. Furthermore, we demonstrate SharkTrack application across diverse marine ecosystems and elasmobranch species, an advancement compared to previous models, which were limited to specific species or locations. SharkTrack applications extend beyond BRUVS analysis, facilitating rapid annotation of unlabeled videos, aiding the development of further models to classify elasmobranch species. We provide public access to the software and an unprecedentedly diverse dataset, facilitating future research in an important area of marine conservation.

Significant wave height (SWH) is a vital metric in marine science, and accurate SWH estimation is crucial for various applications, e.g., marine energy development, fishery, early warning systems for potential risks, etc. Traditional SWH estimation methods that are based on numerical models and physical theories are hindered by computational inefficiencies. Recently, machine learning has emerged as an appealing alternative to improve accuracy and reduce computational time. However, due to limited observational technology and high costs, the scarcity of real-world data restricts the potential of machine learning models. To overcome these limitations, we propose an ocean SWH estimation framework, namely Orca. Specifically, Orca enhances the limited spatio-temporal reasoning abilities of classic LLMs with a novel spatiotemporal aware encoding module. By segmenting the limited buoy observational data temporally, encoding the buoys' locations spatially, and designing prompt templates, Orca capitalizes on the robust generalization ability of LLMs to estimate significant wave height effectively with limited data. Experimental results on the Gulf of Mexico demonstrate that Orca achieves state-of-the-art performance in SWH estimation.

We present a knowledge-guided machine learning (KGML) framework for modeling multi-scale processes, and study its performance in the context of streamflow forecasting in hydrology. Specifically, we propose a novel hierarchical recurrent neural architecture that factorizes the system dynamics at multiple temporal scales and captures their interactions. This framework consists of an inverse and a forward model. The inverse model is used to empirically resolve the system's temporal modes from data (physical model simulations, observed data, or a combination of them from the past), and these states are then used in the forward model to predict streamflow. In a hydrological system, these modes can represent different processes, evolving at different temporal scales (e.g., slow: groundwater recharge and baseflow vs. fast: surface runoff due to extreme rainfall). A key advantage of our framework is that once trained, it can incorporate new observations into the model's context (internal state) without expensive optimization approaches (e.g., EnKF) that are traditionally used in physical sciences for data assimilation. Experiments with several river catchments from the NWS NCRFC region show the efficacy of this ML-based data assimilation framework compared to standard baselines, especially for basins that have a long history of observations. Even for basins that have a shorter observation history, we present two orthogonal strategies of training our FHNN framework: (a) using simulation data from imperfect simulations and (b) using observation data from multiple basins to build a global model. We show that both of these strategies (that can be used individually or together) are highly effective in mitigating the lack of training data. The improvement in forecast accuracy is particularly noteworthy for basins where local models perform poorly because of data sparsity.

Discerning between authentic content and that generated by advanced AI methods has become increasingly challenging. While previous research primarily addresses the detection of fake faces, the identification of generated natural images has only recently surfaced. This prompted the recent exploration of solutions that employ foundation vision-and-language models, like CLIP. However, the CLIP embedding space is optimized for global image-to-text alignment and is not inherently designed for deepfake detection, neglecting the potential benefits of tailored training and local image features. In this study, we propose CoDE (Contrastive Deepfake Embeddings), a novel embedding space specifically designed for deepfake detection. CoDE is trained via contrastive learning by additionally enforcing global-local similarities. To sustain the training of our model, we generate a comprehensive dataset that focuses on images generated by diffusion models and encompasses a collection of 9.2 million images produced by using four different generators. Experimental results demonstrate that CoDE achieves state-of-the-art accuracy on the newly collected dataset, while also showing excellent generalization capabilities to unseen image generators. Our source code, trained models, and collected dataset are publicly available at: https://github.com/aimagelab/CoDE.

Accurate forecasting of wind speed and direction is paramount across various domains, playing a pivotal role in weather prediction, renewable energy generation, agricultural management, and bushfire mitigation efforts. Accurate predictions enable meteorologists to deepen their understanding of atmospheric processes, leading to more precise weather forecasts and timely alerts for severe weather events [1]. In the realm of renewable energy, precise forecasts of wind conditions are indispensable to optimise the performance of wind farms and integrate wind energy efficiently into the power grid [2-4]. In agriculture, wind forecasts inform critical decisions such as crop spraying, sprinkler or central pivot irrigation timing, and pest control, ultimately improving crop yields and water management [5]. For bush-fire management, timely and accurate predictions of wind speed and direction are crucial for modelling fire behaviour, planning firefighter deployment, and planning evacuations, thereby reducing the impact of bushfires on communities and ecosystems [6, 7]. Given the multifaceted applications of wind forecasting, advancements in machine learning-based techniques for predicting wind speed and direction hold immense promise for bolstering societal resilience and fostering sustainable development. Traditionally, wind forecasting models fall into three categories: physical, statistical time series analysis and machine learning.

Tipping points occur in many real-world systems, at which the system shifts suddenly from one state to another. The ability to predict the occurrence of tipping points from time series data remains an outstanding challenge and a major interest in a broad range of research fields. Particularly, the widely used methods based on bifurcation theory are neither reliable in prediction accuracy nor applicable for irregularly-sampled time series which are commonly observed from real-world systems. Here we address this challenge by developing a deep learning algorithm for predicting the occurrence of tipping points in untrained systems, by exploiting information about normal forms. Our algorithm not only outperforms traditional methods for regularly-sampled model time series but also achieves accurate predictions for irregularly-sampled model time series and empirical time series. Our ability to predict tipping points for complex systems paves the way for mitigation risks, prevention of catastrophic failures, and restoration of degraded systems, with broad applications in social science, engineering, and biology.

Machine learning (ML) has increased greatly in both popularity and significance, driven by an increase in methods, computing power and data availability [33]. On July 5, 2024, a search on Web of Science for publications including the term "machine learning" yielded more than 350,000 results, corresponding to an average annual increase by more than 20% since 2006. ML models are often characterized by their high generalizability, making them particularly successful when used for supervised learning tasks like classification and risk prediction. In recent years, ML models based on deep artificial neural networks (ANNs) have led to groundbreaking results in the development of high-performing prediction models. The high prediction accuracy of modern ML models is usually achieved by optimizing complex "black-box" architectures with thousands of parameters. As a consequence, they often result in predictions that are difficult, if not impossible, to interpret. This interpretability problem has been hindering the use of ML in fields like medicine, ecology and insurance, where an understanding of the model and its inner workings is paramount to ensure user acceptance and fairness. In a recent environmental study, for example, we explored the use of ML to derive predictions of stream biological condition in the Chesapeake Bay watershed of the mid-Atlantic coast of North America [26]. Clearly, if these predictions are intended to inform future management policies (projecting, e.g., changes in land use, climate and watershed characteristics), they are required to be interpretable in terms of relevant features as well as the directions and strengths of the feature effects.