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 Book Chapter Book Chapter Faculty Publications- Book Chapter 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 A Multilayered Feed-Forward Neural Network Architecture for Rainfall Forecasting Description An account of the resource The amount of rain received in a particular demographic region in a given time interval is called the rainfall. Rainfall is a natural and complex process and has significance in different domains including agriculture, transport, disaster management, and natural calamities resilience [1]. Abnormal rainfall affects every facet of humans and all other living beings of the world and also has a great impact in wellbeing and financial disruptions of a country. Accurate rainfall predictions at regular time intervals are always important to issue warnings about likelihood of any disaster about to happen. This also provides people a time for strategic planning in their work and precautions at time of adversity [2]. It is worth noting that rainfall forecasting does not only have an impact in day-to-day life, but more importantly for tropical countries like India where the chief occupation being agriculture and also for various other industries. It largely helps in disaster management and recovery process as well. The rainfall being a variable over time, geography and atmospheric conditions makes the forecasting considerably difficult [3]. Rainfall forecasting keeps a person informed about the likelihood of rainfall the forthcoming day, week, or month which enable long-time planning and on the other way; hourly prediction helps for shortterm planning such as enforcing traffic measures. Literature has seen various studies in this domain using predictive machine learning (ML) algorithms such as neural networks (NNs), Genetic algorithms, and Fuzzy-based systems [4]. 2024 Sachi Nandan Mohanty, Preethi Nanjundan and Tejaswini Kar. Creator An entity primarily responsible for making the resource Rajagopal M.; Ramkumar S. Source A related resource from which the described resource is derived Artificial Intelligence in Forecasting: Tools and Techniques, pp. 31-48. Publisher An entity responsible for making the resource available CRC Press Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1201/9781003399292-2" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003399292-2</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196000481&amp;doi=10.1201%2F9781003399292-2&amp;partnerID=40&amp;md5=0c924b886062814d56a63c12796007bf" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196000481&amp;doi=10.1201%2f9781003399292-2&amp;partnerID=40&amp;md5=0c924b886062814d56a63c12796007bf</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-104005150-4; 978-103250615-9 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Book chapter Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Rajagopal M., School of Business and Management, CHRIST (Deemed to be University), Bangalore, India; Ramkumar S., Department of Computer Science, School of Sciences, CHRIST (Deemed to be University), Bangalore, India