arXiv subjects

Hassan Noura

Publications and source records attributed to Hassan Noura.

9 recordsLinked to original sources

arXiv2026

Risk Is Not the Target: A Monotonic Framework for Evaluating Wildfire Operational Risk Signals

Evaluating wildfire risk systems using standard machine-learning metrics such as F1-score or IoU is fundamentally flawed: these metrics assess event prediction accuracy, not the operational coherence of a continuous risk signal. This work proposes a novel monotonic evaluation framework that measures whether increases in a predicted risk score consistently correspond to increases in observed operational load, such as number of fires, intervention time, and deployed resources. Moreover, we compare three structurally different approaches on the French Alpes-Maritimes department: the expert-based DFE index, GRU- based predictive models, and FARS, a hybrid multi-agent system combining predictive AI with LLM-based reasoning. Experimental results reveal that the DFE, despite poor classification metrics, exhibits the most balanced monotonic behavior across the full risk scale. GRU models achieve strong local monotonicity but fail to produce well-distributed risk levels. FARS inherits and reveals the structural limitations of upstream signals rather than correcting them. The central finding is a paradigm shift: a good risk model does not predict fires accurately, but one whose ordinal scale meaningfully explains operational dynamics, as proved in this paper. Code of the monotonic framework is available on github.

cs.AI↗

arXiv2026

Extreme-value forest fire prediction A study of the Loss Function in an Ordinality Scheme

Wildfires are highly imbalanced natural hazards in both space and severity, making the prediction of extreme events particularly challenging. In this work, we introduce the first ordinal classification framework for forecasting wildfire severity levels directly aligned with operational decision-making in France. Our study investigates the influence of loss-function design on the ability of neural models to predict rare yet critical high-severity fire occurrences. We compare standard cross-entropy with several ordinal-aware objectives, including the proposed probabilistic TDeGPD loss derived from a truncated discrete exponentiated Generalized Pareto Distribution. Through extensive benchmarking over multiple architectures and real operational data, we show that ordinal supervision substantially improves model performance over conventional approaches. In particular, the Weighted Kappa Loss (WKLoss) achieves the best overall results, with more than +0.1 IoU (Intersection Over Union) gain on the most extreme severity classes while maintaining competitive calibration quality. However, performance remains limited for the rarest events due to their extremely low representation in the dataset. These findings highlight the importance of integrating both severity ordering, data imbalance considerations, and seasonality risk into wildfire forecasting systems. Future work will focus on incorporating seasonal dynamics and uncertainty information into training to further improve the reliability of extreme-event prediction.

cs.LG↗

arXiv2026

Data-Driven Fire-Zone Segmentation for Improved Short-Term Wildfire Prediction

Wildfire prediction models typically discretize study areas into uniform grids, ignoring the heterogeneous spatial distribution of ignitions. We challenge this paradigm by showing that how data is discretized matters more than which model is used. We propose an unsupervised fire-zone segmentation algorithm combining watershed detection with K-means clustering to define prediction units directly from historical fire patterns. Experiments across six French departments and six forecasting models show that fire-zone segmentation consistently outperforms grid-based approaches, with mean IoU improvements of +3--6% depending on spatial scale. The method is computationally lightweight (<10s per configuration) and fully parallelizable. Our results demonstrate that optimizing spatial discretization yields significant, reproducible performance gains for short-term wildfire forecasting.

cs.LG↗

arXiv2026

Proof of Concept: Multi-Target Wildfire Risk Prediction and Large Language Model Synthesis

Current state-of-the-art approaches to wildfire risk assessment often overlook operational needs, limiting their practical value for first responders and firefighting services. Effective wildfire management requires a multi-target analysis that captures the diverse dimensions of wildfire risk, including meteorological danger, ignition activity, intervention complexity, and resource mobilization, rather than relying on a single predictive indicator. In this proof of concept, we propose the development of a hybrid framework that combines predictive models for each risk dimension with large language models (LLMs) to synthesize heterogeneous outputs into structured, actionable reports.

cs.LG↗

arXiv2025

Localized Forest Fire Risk Prediction: A Department-Aware Approach for Operational Decision Support

Forest fire prediction involves estimating the likelihood of fire ignition or related risk levels in a specific area over a defined time period. With climate change intensifying fire behavior and frequency, accurate prediction has become one of the most pressing challenges in Artificial Intelligence (AI). Traditionally, fire ignition is approached as a binary classification task in the literature. However, this formulation oversimplifies the problem, especially from the perspective of end-users such as firefighters. In general, as is the case in France, firefighting units are organized by department, each with its terrain, climate conditions, and historical experience with fire events. Consequently, fire risk should be modeled in a way that is sensitive to local conditions and does not assume uniform risk across all regions. This paper proposes a new approach that tailors fire risk assessment to departmental contexts, offering more actionable and region-specific predictions for operational use. With this, we present the first national-scale AI benchmark for metropolitan France using state-of-the-art AI models on a relatively unexplored dataset. Finally, we offer a summary of important future works that should be taken into account. Supplementary materials are available on GitHub.

cs.LG↗

arXiv2024

A Survey on Data-Driven Fault Diagnostic Techniques for Marine Diesel Engines

Fault diagnosis in marine diesel engines is vital for maritime safety and operational efficiency.These engines are integral to marine vessels, and their reliable performance is crucial for safenavigation. Swift identification and resolution of faults are essential to prevent breakdowns,enhance safety, and reduce the risk of catastrophic failures at sea. Proactive fault diagnosisfacilitates timely maintenance, minimizes downtime, and ensures the overall reliability andlongevity of marine diesel engines. This paper explores the importance of fault diagnosis,emphasizing subsystems, common faults, and recent advancements in data-driven approachesfor effective marine diesel engine maintenance

cs.LG↗

arXiv2017

An Efficient Keyless Fragmentation Algorithm for Data Protection

The family of Information Dispersal Algorithms is applied to distributed systems for secure and reliable storage and transmission. In comparison with perfect secret sharing it achieves a significantly smaller memory overhead and better performance, but provides only incremental confidentiality. Therefore, even if it is not possible to explicitly reconstruct data from less than the required amount of fragments, it is still possible to deduce some information about the nature of data by looking at preserved data patterns inside a fragment. The idea behind this paper is to provide a lightweight data fragmentation scheme, that would combine the space efficiency and simplicity that could be find in Information Dispersal Algorithms with a computational level of data confidentiality.

cs.CR↗

arXiv2017

A Revision of a New Chaos-Based Image Encryption System: Weaknesses and Limitations

Lately, multimedia encryption has been the focus of attention in many researches. Recently, a large number of encryption algorithms has been presented to protect image contents.The main objective of modern image encryption schemes is to reduce the computation complexity in order to respond to the real time multimedia and/or limited resources requirements without degrading the high level of security. In fact, most of the recent solutions are based on the chaotic theory. However, the majority of chaotic systems suffers from different limitations and their implementation is difficult at the hardware level because of the non integer operations that are employed requiring huge resources and latency. In this paper, we analyze the new chaos-based image encryption system presented in~\cite{el2016new}. It uses a static binary diffusion layer, followed by a key dependent bit-permutation layer that only iterates for one round. Based on their results in this paper, we claim that the uniformity and avalanche effect can be reached from the first round. However, we tried to verify the results but our conclusion was that these results were wrong because it was shown that at least 6 iterations are necessary to ensure the required cryptographic performance such as the plain-sensitivity property. Therefore, the required execution time must be multiplied by 6 and consequently this will increase the latency. In addition to all aforementioned problems, we find that ensuring the avalanche effect in the whole image introduces a high error propagation. In order to solve this problem, we recommend to ensure the avalanche effect in the level of blocks instead of the whole image.

cs.CR↗

arXiv2012

Analytic prognostic for petrochemical pipelines

Pipelines tubes are part of vital mechanical systems largely used in petrochemical industries. They serve to transport natural gases or liquids. They are cylindrical tubes and are submitted to the risks of corrosion due to high PH concentrations of the transported liquids in addition to fatigue cracks due to the alternation of pressure-depression of gas along the time, initiating therefore in the tubes body micro-cracks that can propagate abruptly to lead to failure. The development of the prognostic process for such systems increases largely their performance and their availability, as well decreases the global cost of their missions. Therefore, this paper deals with a new prognostic approach to improve the performance of these pipelines. Only the first mode of crack, that is, the opening mode, is considered.

physics.gen-ph↗