A Data Science Central Community

**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 [email protected]

© 2019 AnalyticBridge.com is a subsidiary and dedicated channel of Data Science Central LLC Powered by

Badges | Report an Issue | Privacy Policy | Terms of Service

**Most Popular Content on DSC**

To not miss this type of content in the future, subscribe to our newsletter.

**Technical**

- Free Books and Resources for DSC Members
- Learn Machine Learning Coding Basics in a weekend
- New Machine Learning Cheat Sheet | Old one
- Advanced Machine Learning with Basic Excel
- 12 Algorithms Every Data Scientist Should Know
- Hitchhiker's Guide to Data Science, Machine Learning, R, Python
- Visualizations: Comparing Tableau, SPSS, R, Excel, Matlab, JS, Pyth...
- How to Automatically Determine the Number of Clusters in your Data
- New Perspectives on Statistical Distributions and Deep Learning
- Fascinating New Results in the Theory of Randomness
- Long-range Correlations in Time Series: Modeling, Testing, Case Study
- Fast Combinatorial Feature Selection with New Definition of Predict...
- 10 types of regressions. Which one to use?
- 40 Techniques Used by Data Scientists
- 15 Deep Learning Tutorials
- R: a survival guide to data science with R

**Non Technical**

- Advanced Analytic Platforms - Incumbents Fall - Challengers Rise
- Difference between ML, Data Science, AI, Deep Learning, and Statistics
- How to Become a Data Scientist - On your own
- 16 analytic disciplines compared to data science
- Six categories of Data Scientists
- 21 data science systems used by Amazon to operate its business
- 24 Uses of Statistical Modeling
- 33 unusual problems that can be solved with data science
- 22 Differences Between Junior and Senior Data Scientists
- Why You Should be a Data Science Generalist - and How to Become One
- Becoming a Billionaire Data Scientist vs Struggling to Get a $100k Job
- Why do people with no experience want to become data scientists?

**Articles from top bloggers**

- Kirk Borne | Stephanie Glen | Vincent Granville
- Ajit Jaokar | Ronald van Loon | Bernard Marr
- Steve Miller | Bill Schmarzo | Bill Vorhies

**Other popular resources**

- Comprehensive Repository of Data Science and ML Resources
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- 100 Data Science Interview Questions and Answers
- Cheat Sheets | Curated Articles | Search | Jobs | Courses
- Post a Blog | Forum Questions | Books | Salaries | News

**Archives**: 2008-2014 | 2015-2016 | 2017-2019 | Book 1 | Book 2 | More

**Most popular articles**

- Free Book and Resources for DSC Members
- New Perspectives on Statistical Distributions and Deep Learning
- Time series, Growth Modeling and Data Science Wizardy
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- Comprehensive Repository of Data Science and ML Resources
- Advanced Machine Learning with Basic Excel
- Difference between ML, Data Science, AI, Deep Learning, and Statistics
- Selected Business Analytics, Data Science and ML articles
- How to Automatically Determine the Number of Clusters in your Data
- Fascinating New Results in the Theory of Randomness
- Hire a Data Scientist | Search DSC | Find a Job
- Post a Blog | Forum Questions

## You need to be a member of AnalyticBridge to add comments!

Join AnalyticBridge