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Daily Precipitation Forecasting using Neural Network - A case study of Punjab,India

Lohani, Punit (2019) Daily Precipitation Forecasting using Neural Network - A case study of Punjab,India. Masters thesis, Dublin, National College of Ireland.

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Precipitation forecasting is one of the most important and crucial task that has gained the attention of the meteorologists around the world. A lot of changes have been observed in the climatic pattern over a period of time, so this field has become the area of research for the research communities. Accurately forecasting the amount of precipitation can prove to be very useful in detecting the occurrence of natural calamities like drought and flood in the near future. Due to the lack of proper irrigation facilities in some parts of India, farmers highly rely on precipitation. The traditional methods of forecasting are time consuming and due to non linear data, sometimes results in an inaccurate forecast. Machine learning approach has the ability to overcome the limitations and can present the forecast accurately. This study primarily focuses on the forecasting the daily amount of Precipitation using Seasonal ARIMA model and Long Short Term Memory networks. The objective of the study would be to check the ability of LSTM networks in accurately presenting the daily forecast for the amount of precipitation over Punjab in India.
Keywords: Precipitation forecasting, meteorologists, natural calamities, Seasonal ARIMA, Long Short Term Memory

Item Type: Thesis (Masters)
Subjects: G Geography. Anthropology. Recreation > GE Environmental Sciences
Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
Q Science > QA Mathematics > Computer software
T Technology > T Technology (General) > Information Technology > Computer software
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Dan English
Date Deposited: 07 Jul 2020 17:35
Last Modified: 07 Jul 2020 17:35

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