Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Jyothish, V.R.; Abraham, Sajimon; Presannakumar, Krishna; Abraham, Leo Title A name given to the resource Improving Flood Prediction Using Artificial Neural Networks With Optimal Feature Selection on a Benchmark Dataset Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Journal of Forecasting;Volume;45;Issue;4;pp.1454-1472 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1002/for.70096" target="_blank" rel="noreferrer noopener">https://doi.org/10.1002/for.70096</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105027326393?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105027326393?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Jyothish V.R., School of Computer Sciences, Mahatma Gandhi University, Kerala, Kottayam, India; Abraham S., Faculty in School of Management and Business Studies, Mahatma Gandhi University, Kerala, Kottayam, India; Presannakumar K., CHRIST (deemed to be University), Bangalore, India; Abraham L., College of Engineering Trivandrum, Kerala, Thiruvananthapuram, India Description An account of the resource Disasters significantly impact people's lives; among them, flooding is the worst common, and it causes sudden and secure damage to both lives and property. Addressing such real-time crisis demands intricate and sophisticated flood prediction models with enhanced capabilities. The development of efficient flood prediction models is often hindered by the lack of available datasets and the need for optimal feature. To address the challenge of data availability, in the proposed research, we have manually prepared a novel dataset by collecting data from NASA's (National Aeronautics and Space Administration) Power Project. The proposed dataset is experimentally evaluated and verified and has been organized into a balanced benchmark dataset with 33 features using the SMOTE algorithm. To enhance the provenance of flood prediction model, we propose a novel feature selection method. This method integrates outcomes from three different feature selection techniques to identify the most prominent features. The proposed feature selection method improves the model's performance and efficiency by identifying optimal predictors. Experimental results demonstrate that the artificial neural network trained with the selected relevant features accurately predicts flood occurrences, showing enhanced accuracy compared to state-of-the-art methods. 2026 John Wiley &amp; Sons Ltd. Subject The topic of the resource artificial neural network; big data; feature selection; flood forecasting system; machine learning; optimal predictors Publisher An entity responsible for making the resource available John Wiley and Sons Ltd Relation A related resource ISSN: 2776693; CODEN: JOFOD Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online