Forecasting Extreme Weather Events: A Novel Approach Using Predictive Analytics and Attention-Based Hybrid LSTM-FCN Architecture

Abstract: This research paper explores the pivotal role of artificial intelligence (AI) in enhancing climate resilience through advanced predictive analytics and data-driven decision-making. As climate change intensifies the frequency and severity of extreme weather events, AI technologies—such as machine learning, deep learning, and neural networks—are being leveraged to model complex environmental systems, forecast climate-related risks, and inform mitigation and adaptation strategies. The paper examines the integration of satellite imagery, remote sensing data, and real-time monitoring with AI algorithms to predict disasters like floods, droughts, and wildfires with greater accuracy. It also investigates AI's contribution to sustainable development by optimizing resource allocation, improving early warning systems, and supporting climate-smart infrastructure. Through case studies and empirical analysis, this study highlights how AI-driven climate intelligence can reduce vulnerabilities, guide global policy, and accelerate the transition to a resilient and sustainable future. Keywords: Artificial intelligence, Climate resilience, Predictive analytics, Sustainable development, Climate risk modeling, Disaster forecasting, Machine learning, Deep learning, Environmental monitoring, Early warning systems, Climate adaptation, Risk reduction, Smart infrastructure, Climate change mitigation, Remote sensing

The increasing intensity and frequency of extreme weather events resulting from climate change have led to grid outages and other negative consequences. To ensure the resilience of buildings which serve as primary shelters for occupants, resilient strategies are being developed to improve their ability to withstand these extreme events (e.g., building upgrades and renewable energy generators and storage). However, a crucial step towards creating a resilient built environment is accurately estimating building performance during such conditions using historical extreme climate change-induced weather events. To conduct Building Performance Simulation (BPS) in extreme conditions, such as weather events induced by climate change, it is essential to utilize Actual Meteorological Year (AMY) weather files instead of Typical Meteorological Year (TMY) files. AMY files capture the precise climatic conditions during extreme weather events, enabling accurate simulation of such scenarios. These weather files provide valuable data that can be used to assess the vulnerabilities and resilience of buildings to extreme weather events. By analyzing past events and their impacts using BPS tools, we can gain insights into the specific weaknesses and areas that require improvement. This approach applies to both existing buildings needing climate change-resilient retrofits and new building designs that must be compatible with future climatic conditions. Moreover, the intensification and frequency increase of these extreme weather events makes developing adaptation and resilient-building measures imperative. This involves understanding the potential losses that households may experience due to the intensification of extreme events and developing farsighted coping strategies and climate-proof resilient-building initiatives. However, addressing the knowledge gap caused by the absence of an AMY weather file dataset of extreme events is essential. This will allow for accurate BPS during past extreme climate change-induced weather events. To fill this gap, this article introduces a comprehensive .epw format weather file dataset focusing on historical extreme weather events in Canada. This collection encompasses a diverse array of past extreme climate change occurrences in various locations, with potential for future expansion to include additional locations and countries. This dataset enables energy simulations for different types of buildings and considers a diverse range of historical weather conditions, allowing for better estimation of thermal performance.

South Africa's informal sector, including street vendors, faces significant occupational health and safety challenges, including exposure to air pollution, extreme weather, and limited sanitation. Current legislative frameworks, offer limited protections for these workers, leaving substantial gaps in regulatory coverage. Artificial Intelligence (AI) presents a promising solution by enabling real-time environmental monitoring, predictive analytics, and health alerts tailored to high-risk conditions. This chapter explores various AI applications for improving vendor safety, such as wearable devices for air quality and vital signs monitoring, and mobile apps delivering real-time health information. However, barriers to implementation exist, including financial constraints, digital literacy gaps, and data privacy concerns. To address these, the chapter recommends targeted interventions like public-private partnerships and subsidies, and digital literacy initiatives alongside robust policy frameworks that expand OHS protections to include AI-driven health solutions.

The electric power system is the largest artificial system so far. Many overhead transmission lines in the power grid are exposed to the natural environment for long periods, while the frequent occurrence of extreme weather events poses a severe threat to the power system. The data in this study came from multi-source dynamic data sets collected in real-time. These data are matched with previous overhead transmission lines disaster occurrences by coupling multiple to disaster factors through a decision support system based on predictive analysis. Then data mining techniques are used to establish the data models for transmission lines disaster to identify correlation models for factors such as transmission lines faults and meteorological data and to model transmission lines in HN Province, China, through analysis of disaster probability for prediction early warning. The results showed that the disaster model of overhead transmission lines matched the actual situation and had a high accuracy of early warning.

Global supply chains face increasing disruptions from cyber threats, geopolitical instability, extreme weather events, and a range of economic, social, and environmental sustainability challenges. As these disruptions intensify, enhancing Supply Chain Resilience (SCR) has become a strategic priority. This study investigates how Distributed Ledger Technology (DLT) can contribute to SCR by mitigating vulnerabilities and strengthening key capabilities within global supply chains. A qualitative research approach is employed, utilizing expert evaluations to examine DLT’s impact on supply chain vulnerabilities and capabilities. Five workshops were conducted with 25 industry professionals from logistics, IT, procurement, and risk management. Experts examined how DLT could address disruptions stemming from supplier instability, poor traceability, and regulatory and environmental pressures, while highlighting its potential to drive ethical sourcing and environmentally responsible practices. The structured discussions were guided by theoretical frameworks and expert evaluations were synthesized into two analytical matrices illustrating DLT’s influence on SCR. The findings reveal that the contribution of DLT to SCR and sustainability is highly context-dependent, with its effectiveness hinging on how it is embedded within governance structures and aligned with the interplay of complementary technologies. Building on these insights, the study presents the DLT-LFL (Distributed Ledger Technology–Learning Feedback Loop) framework, which integrates sensing, decision-making, adaptation, and predictive learning from distributed operational data, allowing supply chains to better anticipate disruptions, adjust processes dynamically, and continuously strengthen resilience and sustainable practices. The study also develops a practical checklist to assess how effective DLT applications and their integration with predictive and AI-driven analytics reduce vulnerabilities, strengthen capabilities, mitigate risks, and support adaptive decision-making.

This paper reports the development and results of a model exploring the resilience of the Dutch electricity transmission infrastructure to extreme weather events. Climate change is anticipated to result in an increase in the frequency and severity of extreme weather events over the coming decades. Situated in a low-lying coastal delta, the Netherlands may be particularly exposed to certain types of extreme weather(-induced) events. The degree to which the country’s electricity network may prove resilient in the face of these future events is an open question. The model focuses on two types of extreme events – floods and heat waves – and assesses two types of adaptation measures – substation flood protections and demand-side management. The model employs a network-based approach in assessing infrastructure resilience – explicitly representing the structure and properties of the Dutch transmission infrastructure – and extends previous work by accounting for key power system characteristics such as capacity constraints and cascading failures. From a practice perspective, the results offer a first indication of the vulnerability of the Dutch electricity transmission infrastructure in the context of climate change. These results suggest that the network displays some vulnerability to both floods and heat waves. Both types of adaptation measures tested are found to enhance resilience, though substation flood protection shows greater benefits. Whilst the model was specifically developed for the study of electricity networks, we anticipate that this method may also be applicable to other types of transport infrastructures.

This paper analyzes the change characteristics of extreme weather and climate events,such as heavy rain,drought,high temperature ≥35 ℃,low temperature ≤-20 ℃,frost,gale,sandstorm,thunderstorm and hail over the Hexi Corridor using the data of annual occurring days of extreme weather and climate events from 18 meteorological stations in the corridor during the period of 1958-2006 and the statistical methods,such as polynomial,linear trend,Mann-Kendall test,Moving T-test and Morlet wavelet analysis.The results show that the annual days of occurring some extreme weather events,such as gale,sandstorm,low temperature ≤-20 ℃,frost,thunderstorm and hail,over the Hexi Corridor in recent 49 years have been significantly decreased,but the annual days of occurring high temperature ≥35 ℃ have been obviously increased.Of which the abrupt change of occurrence of gale,sandstorm,high temperature ≥35 ℃ and frost occurred around 1996,1989,1996 and 1998 respectively.Generally,the change of the frequencies of heavy rain and drought is not so obvious.In the early 1990s,the annual days of occurring low temperature ≤-20 ℃ and frost were decreased,but those of high temperature ≥35 ℃,heavy rain and drought were increased,and these trends met the abrupt warming in the Hexi Corridor.There is a long-term periodical change in high temperature and drought but a staggered one in sandstorm and thunderstorm,and the periodical change of other extreme weather and climate events is not so obvious.

Extreme weather events are expected to increase. The paper provides a concise overview of climate change as well as issues pertaining to extreme weather events. Several indicators used to quantify climate variability, to assess countries’ vulnerability to climate change and extreme weather negative impacts are presented. In Asia rice assumes strategic relevance, thus accounting most of the world production and consumption. This paper aims at expanding the above statement and at investigating the potential effects of weather events on rice supply using a multiple regression analysis. Given its vulnerability to climate change and its limited ability to cope with it, Asia is expected to be severely affected by the adverse impacts of extreme weather conditions, where food security still represents a major concern.

Offshore wind farms represent a significant source of renewable energy, but their operation poses unique health and safety challenges due to the harsh marine environment and remote locations. This review explores the implementation of health and safety standards in offshore wind farms, highlighting key challenges and proposing solutions to mitigate risks. The offshore environment presents numerous hazards to workers, including adverse weather conditions, rough seas, and complex machinery. Ensuring the health and safety of personnel working in such environments requires comprehensive risk assessments, stringent safety protocols, and robust emergency response plans. However, the remote nature of offshore wind farms complicates rescue and evacuation procedures, necessitating specialized training and equipment for personnel. One of the primary challenges in implementing health and safety standards is the dynamic nature of offshore operations, which demand continuous monitoring and adaptation to changing conditions. Furthermore, the integration of multiple stakeholders, including project developers, contractors, and regulatory bodies, requires effective communication and collaboration to ensure compliance with safety regulations. To address these challenges, innovative technologies such as remote monitoring systems and predictive analytics can enhance safety performance by providing real-time data on environmental conditions and equipment status. Additionally, the development of standardized safety protocols and training programs tailored to the offshore wind industry can improve the competence and readiness of personnel in emergency situations. Implementing health and safety standards in offshore wind farms is crucial for safeguarding the well-being of workers and minimizing operational risks. By addressing the unique challenges of the offshore environment and adopting proactive safety measures, the industry can ensure sustainable growth while prioritizing the health and safety of its workforce.

Events over the past few decades have brought extreme weather and climate events to the fore of societal concerns. Ordinary citizens, individuals in the private sector, and people at the highest levels of government worry about the apparent increase in the frequency of weather and climate events causing extreme, and in some instances catastrophic, impacts. We differentiate between weather events – relatively short-term phenomena associated with, for instance, tropical cyclones (hurricanes and typhoons, for example), severe floods, and the like – and climate events – longer-lived and/or serial phenomena such as drought, season-long heat waves, record wildfire seasons, multiple occurrences of severe storms in a single season or year, etc. The differentiation is related to the distinction between weather, which can be forecast on short timescales of less than 1–2 weeks, and climate, which can be forecast on monthly, seasonal, and annual timescales. The adage "Climate is what you expect and weather is what you get" probably originates from the fact that climate is the statistical average of the weather over a specified time period. Regardless of whether an extreme event is weather- or climate-related, it could have significant and numerous implications for society.

Extreme weather events have been demonstrated to be increasing in frequency and intensity across the globe and are anticipated to increase further with projected changes in climate. Solar climate intervention strategies, specifically stratospheric aerosol injections (SAI), have the potential to minimise some of the impacts of a changing climate while more robust reductions in greenhouse gas emissions take effect. However, to date little attention has been paid to the possible responses of extreme weather and climate events under climate intervention scenarios. We present an analysis of 16 extreme surface temperature and precipitation indices, and associated vegetation responses, applied to the Geoengineering Large Ensemble (GLENS). GLENS is an ensemble of simulations performed with the Community Earth System Model (CESM1) where SAI is simulated to offset the warming produced by a high emission scenario throughout the 21st century, maintaining surface temperatures at 2020 levels. GLENS is generally successful at maintaining global mean temperature near 2020 levels, however it does not completely offset some of the projected warming in northern latitudes. Some regions are also projected to cool substantially in comparison to the present day, with the greatest decreases in daytime temperatures. The differential warming/cooling also translates to fewer very hot days but more very hot nights during the summer, and fewer very cold days or nights compared to the current day. Extreme precipitation patterns, for the most part, are projected to reduce in intensity in areas that are wet in the current climate and increase in intensity in dry areas. We also find that the distribution of daily precipitation becomes more consistent with more days with light rain, and fewer very intense events than occur currently. In many regions there is a reduction in the persistence of long dry and wet spells compared to present day. However, asymmetry in the night and day temperatures, together with changes in cloud cover and vegetative responses could exacerbate drying in regions that are already sensitive to drought. Overall, our results suggest that while SAI may ameliorate some of the extreme weather hazards produced by global warming, it would also present some significant differences in the distribution of climate extremes compared to the present day.

Natural hazards have displaced over 26.4 million people per year since 2008 (Norwegian Refugee Council, Global estimates 2015: people displaced by disasters. Retrieved from http://www.internal-displacement.org/assets/library/Media/201507-globalEstimates-2015/20150713-global-estimates-2015-en-v1.pdf, 2015). This number will only increase as climate change continues to exacerbate displacement due to monsoon-related flooding, coastal erosion, cyclones, and salinity intrusion (Barua P, Shahjahan M, Rahman M, Rahman S, Molla M. Forced Migr Rev 1(56), 88–91, 2017; Toufique K, Islam A, Int J Disaster Risk Reduction 10(Part A):236–249, 2014). Events such as Typhoon Haiyan, Hurricane Katrina, Hurricane Maria, Hurricane Irma and Michael represent examples of extreme weather events that had devastating effects on communities. Weick and Sutcliffe (Managing the unexpected: resilient performance in an age of uncertainty, 2nd edn. Wiley, San Francisco, 2007) argue that ‘Unexpected events often audit our resilience. Everything that was left unattended becomes a complex problem and every weakness comes rushing to the forefront.’ In the Chatham House report ‘Preparing for High Impact, Low Probability Events’, Lee et al. (Preparing for high-impact, low-probability events; lessons from Eyjafjallajokull. Chatham House Report, 2012:vii) ‘…found that governments and businesses remain unprepared for such events … the frequency of ‘high-impact, low-probability’ (HILP) events in the last decade signals the emergence of a new ‘normal’. Extreme weather and climate related events pose serious public health threats to vulnerable communities and has resulted in mass migrations and displacements; creating what some refer to as ‘climate refugees’. Threats and consequences stemming from climate related events are transboundary and transnational and thereby emerge as a global health security concern. Scenario planning provides a key ‘leverage point’ in managing (mitigation, preparedness, response and recovery) such disaster consequences by illuminating the space of possibilities regarding impact and vulnerabilities. To do so requires a framework to help characterize the challenges associated with the impact of climate related disasters.

<p>As a result of climate change, extreme weather events are becoming more common around the world. Coupled with the ever-present threat of sea level rise that coastal cities face there is a potential for far more severe weather events to occur. This thesis will seek to understand how an existing city can adapt to a more hostile environment, and how in the event of an extreme weather occurrence it maintains its function. There is an urgent need to understand how a city can respond when faced with these situations. Previous extreme weather events, Katrina, the Indian Ocean tsunami, and extreme flooding around the world, highlight the danger of a lack of preparedness and resilience found in most cities. The purpose of this thesis is to understand how the concept of a core shelter, as a way to address the threats of extreme weather events, can be applied to a well-established urban context, Wellington NZ. A core shelter is a structure that in the event of a large-scale disaster, protects its users, and post-disaster still reaches permanent housing standards without being deemed to be a permanent dwelling. It will also look at whether it is possible to create areas in an existing city that can be considered “safe havens” in the event of an extreme natural incident. This thesis outlines the need for these shelters by identifying the potential threats of climate change in a Wellington context, and by understanding the vulnerability of Wellington’s current building stock. It reaches a conclusion that through the implementation of core shelters in Wellington NZ, resilience will be improved, disaster response efforts will be aided, and destruction arising from extreme weather events will be reduced. In addition, it identifies the areas of Wellington that are deemed to be of higher risk in a disaster or extreme weather event, analyses an existing building’s potential to become a community resilience/core shelter, and proposes a custom building that could be built on Leeds St and Ghuznee St.</p>

<p>As a result of climate change, extreme weather events are becoming more common around the world. Coupled with the ever-present threat of sea level rise that coastal cities face there is a potential for far more severe weather events to occur. This thesis will seek to understand how an existing city can adapt to a more hostile environment, and how in the event of an extreme weather occurrence it maintains its function. There is an urgent need to understand how a city can respond when faced with these situations. Previous extreme weather events, Katrina, the Indian Ocean tsunami, and extreme flooding around the world, highlight the danger of a lack of preparedness and resilience found in most cities. The purpose of this thesis is to understand how the concept of a core shelter, as a way to address the threats of extreme weather events, can be applied to a well-established urban context, Wellington NZ. A core shelter is a structure that in the event of a large-scale disaster, protects its users, and post-disaster still reaches permanent housing standards without being deemed to be a permanent dwelling. It will also look at whether it is possible to create areas in an existing city that can be considered “safe havens” in the event of an extreme natural incident. This thesis outlines the need for these shelters by identifying the potential threats of climate change in a Wellington context, and by understanding the vulnerability of Wellington’s current building stock. It reaches a conclusion that through the implementation of core shelters in Wellington NZ, resilience will be improved, disaster response efforts will be aided, and destruction arising from extreme weather events will be reduced. In addition, it identifies the areas of Wellington that are deemed to be of higher risk in a disaster or extreme weather event, analyses an existing building’s potential to become a community resilience/core shelter, and proposes a custom building that could be built on Leeds St and Ghuznee St.</p>

Extreme weather and climate events are the result of interaction of multiple scale anomalous signals. Solar activity presents a remarkable 11-year cycle, which is an important decadal background affecting the occurrence of extreme weather and climate events. A lot of study works show that the periodic variation of solar activity has a modulating effect on the ocean-atmosphere system. The decadal variation of major atmospheric and oceanic modes, such as ENSO, has a phase-locked relationship with the periodic variation of solar activity. A significant solar footprint can be found in the tropical Pacific and the North Atlantic. The analysis shows that the solar activity also modulates the regional temperature, precipitation and typhoon activity as well.