Extreme weather events pose significant challenges, thereby demanding techniques for accurate analysis and precise forecasting to mitigate its impact. In recent years, deep learning techniques have emerged as a promising approach for weather forecasting and understanding the dynamics of extreme weather events. This review aims to provide a comprehensive overview of the state-of-the-art deep learning in the field. We explore the utilization of deep learning architectures, across various aspects of weather prediction such as thunderstorm, lightning, precipitation, drought, heatwave, cold waves and tropical cyclones. We highlight the potential of deep learning, such as its ability to capture complex patterns and non-linear relationships. Additionally, we discuss the limitations of current approaches and highlight future directions for advancements in the field of meteorology. The insights gained from this systematic review are crucial for the scientific community to make informed decisions and mitigate the impacts of extreme weather events.

Prediction of dynamic environmental variables in unmonitored sites remains a long-standing challenge for water resources science. The majority of the world's freshwater resources have inadequate monitoring of critical environmental variables needed for management. Yet, the need to have widespread predictions of hydrological variables such as river flow and water quality has become increasingly urgent due to climate and land use change over the past decades, and their associated impacts on water resources. Modern machine learning methods increasingly outperform their process-based and empirical model counterparts for hydrologic time series prediction with their ability to extract information from large, diverse data sets. We review relevant state-of-the art applications of machine learning for streamflow, water quality, and other water resources prediction and discuss opportunities to improve the use of machine learning with emerging methods for incorporating watershed characteristics into deep learning models, transfer learning, and incorporating process knowledge into machine learning models. The analysis here suggests most prior efforts have been focused on deep learning learning frameworks built on many sites for predictions at daily time scales in the United States, but that comparisons between different classes of machine learning methods are few and inadequate. We identify several open questions for time series predictions in unmonitored sites that include incorporating dynamic inputs and site characteristics, mechanistic understanding and spatial context, and explainable AI techniques in modern machine learning frameworks.

President Zelensky has described seaborne drones as Ukraine's "eyes and protection on the frontline", with claims of a series of successful strikes against Russian ships in the Black Sea and on a key bridge to Crimea. These remote-controlled devices are playing an increasingly prominent role, with both sides ramping up their use for attacks and reconnaissance. The BBC's Security Correspondent Frank Gardner and BBC Verify examine their influence on the conflict.

Here is the situation on Thursday, August 17, 2023. Ukraine said Russia carried out a series of drone attacks on grain silos and warehouses at a Danube River port near the border with Romania. Kyiv said its forces liberated the settlement of Urozhaine in the southeast, but top general Oleksandr Syrskyi warned the situation around Kupiansk on the northeastern front was deteriorating amid Russian counterattacks. Video obtained by Al Jazeera suggests a controversial unit of Chechen troops has been policing the town of Enerhodar near the Russian-occupied Zaporizhzhia Nuclear Power Plant. Russia's Ministry of Defence said it shot down three Ukrainian drones southwest of Moscow and one over Crimea.

Phytoplankton parasites are largely understudied microbial components with a potentially significant ecological impact on phytoplankton bloom dynamics. To better understand their impact, we need improved detection methods to integrate phytoplankton parasite interactions in monitoring aquatic ecosystems. Automated imaging devices usually produce high amount of phytoplankton image data, while the occurrence of anomalous phytoplankton data is rare. Thus, we propose an unsupervised anomaly detection system based on the similarity of the original and autoencoder-reconstructed samples. With this approach, we were able to reach an overall F1 score of 0.75 in nine phytoplankton species, which could be further improved by species-specific fine-tuning. The proposed unsupervised approach was further compared with the supervised Faster R-CNN based object detector. With this supervised approach and the model trained on plankton species and anomalies, we were able to reach the highest F1 score of 0.86. However, the unsupervised approach is expected to be more universal as it can detect also unknown anomalies and it does not require any annotated anomalous data that may not be always available in sufficient quantities. Although other studies have dealt with plankton anomaly detection in terms of non-plankton particles, or air bubble detection, our paper is according to our best knowledge the first one which focuses on automated anomaly detection considering putative phytoplankton parasites or infections.

Fox News Greg Palkot reports from Kyiv on another deadly Russian missile strike and Moscows efforts to block Ukraine food exports. Russia continued to target Ukrainian grain infrastructure in attacks overnight Wednesday, a sign the country could be struggling to achieve a victory in its full-scale invasion of Ukraine. "By targeting Ukraine's grain depots, Putin seeks to starve Ukrainians and create a food crisis, in order to compel [Ukrainian President Volodymyr] Zelenskyy to capitulate and Western nations to withdraw support from Ukraine," Rebekah Koffler, a strategic military intelligence analyst, former senior official at the Defense Intelligence Agency, and author of "Putin's Playbook," told Fox News Digital. "Putin's goal at this stage is to turn Ukraine into a dysfunctional state, that is unable to govern itself and feed its people, thus raising the cost of rebuilding it for the U.S. and European countries." Koffler's comments come after another round of attacks against Ukraine's southern Odesa region, where overnight Russian drones hit storage facilities and ports that Ukraine has been using for grain transport, according to a report from The Associated Press.

Ukraine's air force said a wave of Russian military drones had entered the mouth of the Danube River and were headed towards the country's Izmail river port near the border with Romania. Social media groups monitoring the war reported hearing air defence systems firing in the area near Ukraine's Danube ports of Izmail and Reni early on Wednesday morning. The governor of southern Odesa region, Oleh Kiper, asked residents of Izmail district to take shelter at around 1:30 a.m. Ukraine's Danube River ports accounted for around a quarter of all grain exports from Ukraine before Russia recently pulled out of a deal allowing safe passage for the export of Ukrainian grain via the country's Black Sea ports. Danube River ports have now become the main export route, with grain shipments sent on barges from Ukraine across the Danube to Romania and its Black Sea port of Constanta for onward shipment.

In social science, formal and quantitative models, such as ones describing economic growth and collective action, are used to formulate mechanistic explanations, provide predictions, and uncover questions about observed phenomena. Here, we demonstrate the use of a machine learning system to aid the discovery of symbolic models that capture nonlinear and dynamical relationships in social science datasets. By extending neuro-symbolic methods to find compact functions and differential equations in noisy and longitudinal data, we show that our system can be used to discover interpretable models from real-world data in economics and sociology. Augmenting existing workflows with symbolic regression can help uncover novel relationships and explore counterfactual models during the scientific process. We propose that this AI-assisted framework can bridge parametric and non-parametric models commonly employed in social science research by systematically exploring the space of nonlinear models and enabling fine-grained control over expressivity and interpretability.

Our test sets were too small and too haphazard to support statistically valid conclusions, but they were suggestive of a number of conclusions. We summarize these here, and discuss them at greater length in section 7. Over the kinds of problems tested, GPT-4 with either plug-in is significantly stronger than GPT-4 by itself, or, almost certainly, than any AI that existed a year ago. However it is still far from reliable; it often outputs a wrong answer or fails to output any answer. In terms of overall score, we would judge that these systems performs on the level of a middling undergraduate student. However, their capacities and weaknesses do not align with a human student; the systems solve some problems that even capable students would find challenging, whereas they fail on some problems that even middling high school students would find easy.

Russian shelling has killed seven people, including a 23-day-old infant, and wounded 20 others in Ukraine's southern region of Kherson, prompting local officials to declare a day of mourning. Kyiv reclaimed part of Kherson from Russian occupation last November, but Kremlin troops have continued shelling the regional capital and areas around it from across the Dnipro River. A couple, their 23-day-old child and another man were killed in the village of Shyroka Balka, Interior Minister Ihor Klymenko said on Sunday. The couple's 12-year-old son was critically wounded and died in hospital. "The terrorists will never willingly stop killing civilians," Klymenko wrote in a Telegram post.