| Titre : | Computational model for agricultural vocation in Spain using artificial neural networks, neuro-fuzzy systems, and support vector machines (2026) |
| Auteurs : | J.M. Sánchez ; F. Heras ; H.E. Espitia |
| Type de document : | Article |
| Dans : | Results in Engineering (vol. 29, March 2026) |
| Article en page(s) : | p. 109410 |
| Langues : | Anglais |
| Langues du résumé : | Anglais |
| Catégories : |
Catégories principales 13 - QUESTION SOCIALES ; 13.1 - TravailThésaurus IAMM EMPLOI AGRICOLE ; MAIN D'OEUVRE ; INTELLIGENCE ARTIFICIELLE ; RESEAU DE NEURONES ; MODELE ; ESPAGNE |
| Résumé : | Agricultural employment plays a critical role in food security and rural sustainability; however, it has experienced a sustained decline, largely driven by labor reallocation toward the service sector in Spain. Despite its relevance for public policy design, existing research has predominantly focused on agricultural production, with a notable lack of studies addressing agricultural employment dynamics using artificial intelligence techniques. This study addresses this gap by proposing a modular computational framework to model agricultural employment in Spain using artificial neural networks (ANN), neuro-fuzzy inference systems (ANFIS), and support vector machines (SVM). The framework integrates demographic evolution, sectoral participation, and labor disaggregation. In addition, model performance is evaluated using standard quantitative error metrics (MSE, MAE, and MAPE) and statistical comparison procedures. The results show that ANN and ANFIS achieve consistently lower prediction errors than SVM across most modules, with statistically significant differences in several outputs (p |
| Cote : | En ligne |
| URL / DOI : | https://doi.org/10.1016/j.rineng.2026.109410 |
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