For thousands of years forest fires played the role of a regulator in the ecosystem. Forest fires contributed to the ecological balance by destroying old and diseased plant material; but in the modern era fires are a major problem that tests the endurance not only of government agencies around the world, but also have an effect on climate change. Forest fires have become more intense, more destructive, and more deadly; these are known as megafires. They can cause major economic and ecological problems, especially in the summer months (dry season). However, humanity has developed a tool that can predict fire events, to detect them in time, but also to predict their duration. This tool is artificial intelligence, specifically, machine learning, which is one part of AI. Consequently, this paper briefly mentions several methods of machine learning as used in predicting forest fires and in early detection, submitting an overall review of current models. Our main overall objective is to venture into a new field: predicting the duration of ongoing forest fires. Our contribution offers a new way to manage forest fires, using accessible open data, available from the Hellenic Fire Service. In particular, we imported over 72,000 data from a 10-year period (2014–2023) using machine learning techniques. The experimental and validation results are more than encouraging, with Random Forest achieving the lowest value for the error range (8–13%), meaning it was 87–92% accurate on the prediction of forest fire duration. Finally, some future directions in which to extend this research are presented.

Fire, whether wild or urban, depends on the triad of oxygen, fuel, and heat. Urban fires, although smaller in scale, have devastating impacts, as evidenced by the 2018 wildfire in Mati, Attica (Greece), which claimed 104 lives. The elderly and children are the most vulnerable due to mobility and cognitive limitations. This study applies Grammatical Evolution (GE), a machine learning method that generates interpretable classification rules to predict the consequences of urban fires. Using historical data (casualties, containment time, and meteorological/demographic parameters), GE produces classification rules in human-readable form. The rules achieve over 85% accuracy, revealing critical correlations. For example, high temperatures (>35 °C) combined with irregular building layouts exponentially increase fatality risks, while firefighter response time proves more critical than fire intensity itself. Applications include dynamic evacuation strategies (real-time adaptation), preventive urban planning (fire-resistant materials and green buffer zones), and targeted awareness campaigns for at-risk groups. Unlike “black-box” machine learning techniques, GE offers transparent human-readable rules, enabling firefighters and authorities to make rapid informed decisions. Future advancements could integrate real-time data (IoT sensors and satellites) and extend the methodology to other natural disasters. Protecting urban centers from fires is not only a technological challenge but also a moral imperative to safeguard human lives and societal cohesion.

Throughout history, human societies have sought to explain natural phenomena through the lens of mythology. Earthquakes, as sudden and often devastating events, have inspired a range of symbolic and mythological interpretations across different civilizations. It was not until the 18th and 19th centuries that a more positivist and scientific approach began to emerge regarding the explanation of earthquakes, recognizing their origin as stemming from processes occurring beneath the Earth’s surface. A pivotal moment in the emergence of modern seismology was the Lisbon earthquake of 1755, which marked a significant shift towards scientific inquiry. This means that the question of how earthquakes occur has been resolved; thanks to advancements in scientific, geological, and geophysical research, it is now well understood that seismic events result from the collision and movement of lithospheric or tectonic plates. The contemporary challenge that emerges, however, lies in whether such seismic phenomena can be accurately predicted. In this paper, a systematic attempt is made to use techniques based on Grammatical Evolution to determine the magnitude of earthquakes. These techniques use freely available data in which the history of large earthquakes is introduced before the application of the proposed techniques. From the execution of the experiments, it has become clear that the use of these techniques can allow for more effective estimation of the magnitude of earthquakes compared to other machine learning techniques from the relevant literature.

Earthquakes are the result of the dynamic processes occurring beneath the Earth’s crust; specifically, the movement and interaction of tectonic/lithospheric plates. When one plate shifts relative to another, stress accumulates and is eventually released as seismic energy. This process is continuous and unstoppable. This phenomenon is well recognized in the Mediterranean region, where significant seismic activity arises from the northward convergence (4–10 mm per year) of the African plate relative to the Eurasian plate along a complex plate boundary. Consequently, our research will focus on the Mediterranean region, specifically examining seismic activity from 1990 to 2015 within the latitude range of 33–44° and longitude range of 17–44°. These geographical coordinates encompass 28 seismic zones, with the most active areas being Turkey and Greece. In this paper, we applied Grammatical Evolution for artificial feature construction in earthquake prediction, evaluated against machine learning approaches including MLP(GEN), MLP(PSO), SVM, and NNC. Experiments showed that feature construction (FC) achieved the best performance, with a mean error of 9.05% and overall accuracy of 91%, outperforming SVM. Further analysis revealed that a single constructed feature $\left(N_f=1\right)$ yielded the lowest average error (8.21%), while varying the number of generations indicated that $N_g=200$ provided an effective balance between computational cost and predictive accuracy. These findings confirm the efficiency of FC in enhancing earthquake prediction models through artificial feature construction. Our results, as will be discussed in greater detail within the research, yield an average error of approximately 9%, corresponding to an overall accuracy of 91%.

The spread of contemporary artificial intelligence technologies, particularly machine learning, has significantly enhanced the capacity to predict asymmetrical natural disasters. Wildfires constitute a prominent example, as machine learning can be employed to forecast not only their spatial extent but also their environmental and socio-economic impacts, propagation dynamics, symmetrical or asymmetrical patterns, and even their duration. Such predictive capabilities are of critical importance for effective wildfire management, as they inform the strategic allocation of material resources, and the optimal deployment of human personnel in the field. Beyond that, examination of symmetrical or asymmetrical patterns in fires helps us to understand the causes and dynamics of their spread. The necessity of leveraging machine learning tools has become imperative in our era, as climate change has disrupted traditional wildfire management models due to prolonged droughts, rising temperatures, asymmetrical patterns, and the increasing frequency of extreme weather events. For this reason, our research seeks to fully exploit the potential of Principal Component Analysis (PCA), Minimum Redundancy Maximum Relevance (MRMR), and Grammatical Evolution, both for constructing Artificial Features and for generating Neural Network Architectures. For this purpose, we utilized the highly detailed and publicly available symmetrical datasets provided by the Hellenic Fire Service for the years 2014–2021, which we further enriched with meteorological data, corresponding to the prevailing conditions at both the onset and the suppression of each wildfire event. The research concluded that the Feature Construction technique, using Grammatical Evolution, combines both symmetrical and asymmetrical conditions, and that weather phenomena may provide and outperform other methods in terms of stability and accuracy. Therefore, the asymmetric phenomenon in our research is defined as the unpredictable outcome of climate change (meteorological data) which prolongs the duration of forest fires over time. Specifically, in the model accuracy of wildfire duration using Feature Construction, the mean error was 8.25%, indicating an overall accuracy of 91.75%.

Earthquake predictability remains a central challenge in seismology. Are earthquakes inherently unpredictable phenomena, or can they be forecasted through advances in technology? Contemporary seismological research continues to pursue this scientific milestone, often referred to as the ‘Holy Grail’ of earthquake prediction. In the direction of earthquake prediction based on historical data, the Grammatical Evolution technique of GenClass demonstrated high predictive accuracy for earthquake magnitude. Similarly, our research team follows this line of reasoning, operating under the belief that nature provides a pattern that, with the appropriate tools, can be decoded. What is certain is that, over the past 30 years, scientists and researchers have made significant strides in the field of seismology, largely aided by the development and application of artificial intelligence techniques. Artificial Neural Networks (ANNs) were first applied in the domain of seismology in 1994. The introduction of deep neural networks (DNNs), characterized by architectures incorporating two hidden layers, followed in 2002. Subsequently, recurrent neural networks (RNNs) were implemented within seismological studies as early as 2007. Most recently, grammatical evolution (GE) has been introduced in seismological studies (2025). Despite continuous progress in the field, achieving the so-called “triple prediction”—the precise estimation of the time, location, and magnitude of an earthquake—remains elusive. Nevertheless, machine learning and soft computing approaches have long played a significant role in seismological research. Concerning these approaches, significant advancements have been achieved, both in mapping seismic patterns and in predicting seismic characteristics on a smaller geographical scale. In this way, our research analyzes historical seismic events from 2004 to 2011 within the latitude range of 21°–79° longitude range of 33°–176°. The data is categorized and classified, with the aim of employing grammatical evolution techniques to achieve more accurate and timely predictions of earthquake magnitudes. This paper presents a systematic effort to enhance magnitude prediction accuracy using GE, contributing to the broader goal of reliable earthquake forecasting. Subsequently, this paper presents the superiority of GenClass, a key element of the grammatical evolution techniques, with an average error of 19%, indicating an overall accuracy of 81%.