Believing in the weather guy to keep the golf course dry ? – Forecasting
rainfall could be tricky ..
Forecasting rainfall,
and at a broader level, forecasting the weather is a problem fraught with
challenges. Around the world, various initiatives, such as, Deep Thunder by IBM and Named Peril by Climate Corporation, have
been undertaken to address this challenge. In India, the Indian Meteorology
Department and the Centre for Mathematical Modeling and Computer Simulation
have developed several solutions to address this problem.
The
Government of India recently announced a country wide competition, among
software companies to forecast rainfall among other similar topics by sharing
the historical rainfall and temperature time series data across the different
sub-divisions of the Indian subcontinent.
The
opportunity to bring in Big Data and the related techniques to help forecast
rainfall is immense. Rainfall prediction helps in agriculture, irrigation,
disaster management during floods/droughts, and so on. Several attempts have
been made, primarily using statistical models, to predict rainfall.
Natural systems,
like atmosphere system, exhibit complex dynamical behavior and their data is
considered to exhibit nonlinear dynamical, even chaotic behavior. To add to
this rainfall data is more sensitive to temporal and spatial variations than
other climatic variables. The rainfall time series data, as was provided for
the said competition was used to train the neural networks capable of emulating
similar behaviors, including deterministic chaos.
So
what are the challenges in rainfall forecasting and why these are unique? Chaotic
dynamics is associated with extreme sensitivity to initial conditions,
exponential divergence of proximal trajectories and have very low
predictability horizons. Moreover, most of the variables in the atmospheric
state-space are not even measurable, giving rise to computationally intractable
behaviors. Hence it is difficult to model and predict atmospheric systems using
even higher-order multi-parameter statistical models.
Therefore,
machine learning algorithms, specifically recurrent neural networks, capable of
emulating the nonlinear dynamical, including chaotic, behaviors have become
increasingly popular in the domain of rainfall prediction.
Given
the advantages of recurrent neural networks in explaining the nonlinear
behavior between the inputs and output, the same were explored to forecast the
rainfall of 36 meteorological sub-divisions of India. The model used the past
years’ of rainfall data only, to forecast the monsoon rainfall of coming year. The
application presented would be providing the forecast for a rolling 18 months
period.
A
number of algorithms, Elman neural network, Jordan neural network, Radial Basis
Function neural network, were applied in a competitive way to obtain the best
possible prediction accuracy. The performance of the algorithms was tested across
the different regions, categorized by aridity, as the underlying dynamics were expected
to change, influenced by geographical location, ocean currents, and so on.
I
firmly believe that given the strong emergence of the usage of machine learning
algorithms, this challenge of forecasting rainfall with increased degree of
accuracy would be a reality in the coming years, and we could believe in the
weather guy to keep the golf course dry.
Somjit Amrit is the Chief Business Officer, Technosoft Corporation
He can be reached at somjit.amrit@technosoftcorp.com
