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Estimating rainfall using machine learning strategies based on weather radar data
Author(s) -
T. Aditya Sai Srinivas,
Somula Ramasubbareddy,
K. Govinda,
Saxena Akriti,
A. Pramod Reddy
Publication year - 2019
Publication title -
international journal of communication systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.344
H-Index - 49
eISSN - 1099-1131
pISSN - 1074-5351
DOI - 10.1002/dac.3999
Subject(s) - computer science , machine learning , random forest , inference , weather radar , artificial intelligence , radar , artificial neural network , recurrent neural network , python (programming language) , time series , weather forecasting , data set , data mining , meteorology , telecommunications , physics , operating system
Summary The precision of forecasting rainfall is vital owing to current world climate change. As deterministic weather forecasting models are usually time consuming, it becomes challenging to efficiently use this large volume of data in hand. Machine learning methods are already proven to be good replacement for traditional deterministic approaches in weather prediction. This paper presents an approach using recurrent neural networks (RNN) and long short term memory (LSTM) techniques to improve the rainfall forecast performance. This will be compared with the random forest classifier and XGBoost as well. The goal is to predict a set of hourly rainfall levels from sequences of weather radar measurements. Python libraries are utilized to forecast the time series data. The training set comprises of data from first 20 days of every month and the inference set data from the continuing days. This makes sure that both train and inference sets are more or less independent. The idea resides in implementing an end‐to‐end learning framework.