Evaluating the risk perception due to land subsidence within onyeama mine, South East Nigeria
Notice bibliographique
Résumé
This paper conducts a systematic evaluation of risk perception due to land subsidence monitored within a major coalmine in Nigeria. There is a general assumption in most hazard research that risk perception should be a first determinant of whether adaptive action is taken or not. To gain a comprehensive insight using land subsidence, we propose a technique that integrates quantitative evaluation (vulnerability assessment) and subjective evaluation (perception analysis) of human responses towards this environmental hazard. Perception of risk, which is dependent on risk magnitude appraisal and risk acceptance, was amplified into the overall risk constellation. Vulnerability assessment was conducted based on the human development index (HDI) at fourteen locations investigated for the study, with particular focus on building networks as an index for loss estimation. We exploit data from Sentinel-1 Synthetic Aperture Radar (SAR) Satellites and Small-Baseline Subset Differential Interferometric Synthetic Aperture Radar (SBAS-DInSAR) technique to map Onyeama Coal Mine in South – East, Nigeria. From the HDI and according to the vulnerability assessment, the margin of error (E) is estimated as 0.06754, with samples proportion of ρ ˆ = 0.85. The 95 % confidence interval for proportion is in the range of 0.783 to 0.917. The results indicates that with an average of a finite population size of N = 1413 buildings at each study location, about 85 % of the samples ( n = 100 ) do not recognize that land subsidence is ongoing and constantly affecting their buildings. As a result of this ignorance and lack of awareness, constantly progressing subsidence becomes normalized in peoples’ perceptions, and their outlook toward danger is not integrated into day-to-day habits. Thus, risk perception is a lesser determinant of mitigation responses towards slowly progressing subsidence, and not actual exposure leading to action. • From the HDI and according to the vulnerability assessment, the margin of error (E) is estimated as 0.06754, with samples proportion of ρ ˆ = 0.85. • The 95 % confidence interval for proportion is in the range of 0.783 to 0.917. • The results indicates that with an average of a finite population size of N = 1413 buildings at each study location, about 85 % of the samples ( n = 100 ) do not recognize that land subsidence is ongoing and constantly affecting their buildings. • As a result of this ignorance and lack of awareness, constantly progressing subsidence becomes normalized in peoples’ perceptions, and their outlook toward danger is not integrated into day-to-day habits. • Thus, risk perception is a lesser determinant of mitigation responses towards slowly progressing subsidence, and not actual exposure leading to action.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,005 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».