Please use this identifier to cite or link to this item: http://localhost:8080/xmlui/handle/123456789/9678
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dc.contributor.authorWaleed A Hammood, Omar Abdulmaged Hammood-
dc.contributor.authorSalah A. Aliesawi, Ejiro U. Osiobe-
dc.contributor.authorRaed Abdulkareem Hasan, Safia Malallah-
dc.date.accessioned2025-02-17T08:43:45Z-
dc.date.available2025-02-17T08:43:45Z-
dc.date.issued2024-06-20-
dc.identifier.issn3006–5429-
dc.identifier.urihttp://localhost:8080/xmlui/handle/123456789/9678-
dc.description.abstractNaturally occurring floods are an essential part of life in many parts of the world. Floods, of all the natural dangers, have the greatest effect on society because they cover large geographic regions, happen often, and have a lasting negative socioeconomic impact. Thus, it becomes necessary to design a comprehensive and successful strategy for preventing floods, which will require technical advancements to improve the operational efficacy of government organizations. The Flood Early Warning and Response System (FEWRS), which gives pertinent stakeholders fast information and practical reaction guidelines, emerges as a critical instrument in reducing the loss of lives and property. Unfortunately, current FEWRS frequently fall short of providing enough information on flood disasters, which reduces their ability to mitigate local-level effects and impedes attempts to save lives. Assessing the effectiveness of information systems (IS) within this particular setting is a noteworthy obstacle for scholars, professionals, and administrators. The objective of this research is to tackle this difficulty by exploring the factors that lead to the success of FEWRS. This involves incorporating risk knowledge and response capabilities into the standard IS success model. The present study employs the DeLone and McLean (D&M) models due to their efficaciousness in meeting the designated requirements that are essential for mitigating the impact of flooding disasters.en_US
dc.language.isoenen_US
dc.publisherBabylonian Journal of Machine Learningen_US
dc.subjectInformation systems Success modelen_US
dc.subjectData Analysisen_US
dc.subjectService qualityen_US
dc.subjectFlood mitigationen_US
dc.subjectFlood managementen_US
dc.titleData Analysis of An Exploring the Information Systems Success Factors for Early Warning Systems Adoptionen_US
dc.typeArticleen_US
Appears in Collections:قسم نظم المعلومات



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