Analytical and AI-based approaches to weather events in business: a survey
Rattachement africain : Comores, Égypte. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
Abstract One of the most noticeable aspects of climate change has been the increase in extreme weather events, mostly during recent years. Environmental changes and disasters resulting from these events, including hurricanes, floods, and heat waves, have caused significant ripples across industries. Understanding and minimizing the impact of these weather phenomena on logistics processes have become essential as their frequency and intensity increase. There have been more instances of delayed product deliveries because of extreme weather conditions. Unexpected events can disrupt parts of the transportation network, damage transport infrastructure, and cause congestion. This survey, based on previous studies, found that changing weather patterns over the past few years negatively affected several sectors, including agriculture, logistics, and manufacturing. Advanced machine learning models are needed to forecast shipment delivery delays and to leverage AI-driven optimization techniques and analytical models to mitigate the impact of extreme weather disruptions across multiple operations. The previous studies showed that several techniques are applied for improving the prediction of weather due to climate change and solving the effect of climate change on multiple sectors using 79 studies. The applied techniques, including LSTM and CNNs are used to tackle the difficulty of data structure to improve weather prediction; regression analysis, equation modeling, and DID analysis are applied for calculate the economic losses. Finally, optimization and simulation, Monte Carlo, and mixed linear programming are applied to optimize supply chain, and the weather events may also cause delivery delays which cause economic losses for several sectors.
Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.
Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Analytical and AI-based approaches to weather events in business: a survey
- Date Crossref
- 28/07/2026
- Éditeur
- Springer Science and Business Media LLC
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
-
Midocean University Comores (code pays fourni par la source)Université ou école supérieure
-
Fayoum University Fayoum University, Égypte (code pays fourni par la source)Université ou école supérieure
-
Cairo University Cairo University, Égypte (code pays fourni par la source)Université ou école supérieure
-
Cairo Higher Institute Cairo Higher Institute, Égypte (code pays fourni par la source)Université ou école supérieure
-
College of Informatics Moroni, Comores (pays nommé en fin d’affiliation)Université ou école supérieure
-
Faculty of Computers and Artificial Intelligence Computer Science Department Fayoum University, Égypte (pays nommé en fin d’affiliation)Université ou école supérieure
-
The Higher Institute for Language and Translation Accounting and Business Administration Cairo, Égypte (pays nommé en fin d’affiliation)Structure de recherche
-
Business Information System Department (BIS)- Faculty of Commerce and Business Administration Cairo, Égypte (pays nommé en fin d’affiliation)Université ou école supérieure
Midocean University (Comores), Fayoum University (Fayoum University, Égypte) et Cairo University (Cairo University, Égypte), avec 5 autres affiliations. Pays d’affiliation : Comores, Égypte.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.