A Multilayered Feed-Forward Neural Network Architecture for Rainfall Forecasting
- Title
- A Multilayered Feed-Forward Neural Network Architecture for Rainfall Forecasting
- Creator
- Rajagopal M.; Ramkumar S.
- Description
- 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.
- Source
- Artificial Intelligence in Forecasting: Tools and Techniques, pp. 31-48.
- Date
- 2024-01-01
- Publisher
- CRC Press
- Coverage
- 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
- Rights
- Restricted Access
- Relation
- ISBN: 978-104005150-4; 978-103250615-9
- Format
- Online
- Language
- English
- Type
- Book chapter
Collection
Citation
Rajagopal M.; Ramkumar S., “A Multilayered Feed-Forward Neural Network Architecture for Rainfall Forecasting,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 23, 2025, https://archives.christuniversity.in/items/show/18055.