Forecasting surface temperatures for optimal deployment of

Special Sustainability ProgramsGroup B7

61710175 Aneesh Chandran61710726 Vaibhav Mathur61710314 Pradeep Kumar Grandhi61710251 Nishikant Mishra61710309 Divya Dewan61710269 Mahesh Panse

Goal:• Introducing special sustainability program for countries at most risk due to rise in

surface temperatures

Stakeholders: • Client: United Nations Environment Programme (UNEP)

• UNEP is an agency of United Nations which coordinates its environmental activities, assisting developing countries in implementing environmentally sound policies and practices.

• Selected member countries of UNEP

Challenges: • Strong evidence required to convince countries about climate change• Slow process; Only slight variations noticed after decades of data collection

Opportunities: Accurate temperature forecasts will help mitigate the risk from environmental disasters such as droughts, floods etc

Business Problem

Goal:• Forecasting yearly average temperature for next 5 yrs (2013-2017) to sensitize the

countries about the risk of climate change• Forecasting monthly average temperatures for next 24 months (Jan’13-Dec’14) as

benchmarks to study the effect of special sustainability program (using test and control groups)

Why? • Environmental imbalance, population affected severely• Noticeable impact on energy, agriculture and infrastructure sectors

Success criteria:Actual temperatures recorded in the future should be as close to the forecasts. Any positive deviation would signify the country is enforcing the sustainability measures properly.

Forecasting Problem

Data source: www.kaggle.com

Country selection: 10 countries out of the top 20 countries with highest CO2 emission

Dataset 1: Monthly average temperatures from 1750-2012 but relevant only from 1970-2012Output Variable: Monthly average temperatures for next 24 monthsInput Variables: Time, Monthly average temperature

Dataset 2: Yearly average temperatures from 1750-2012 but relevant only from 1913-2012Output Variable: Yearly average temperatures for next 5 yearsInput Variables: Time, Yearly average temperature

Data Description

Exploratory Data Analysis

Methods used:• Naïve forecasting• Smoothing: Holt-Winter’s• Multiple Linear Regression

Output variable:• Monthly average temperature (numerical)

Performance Measures: MAPE - To evaluate the performance of each model and select the best modelRMSE - To check each model for overfitting

Forecasting Methods (monthly)

• Despite being designed to capture seasonality, Holt-Winter’s method did not actually capture the seasonality for monthly data

Performance Evaluation

• MLR was used to forecast the data for next 24 months.

Forecast Results

Methods used:• Naïve forecasting• Smoothing: Double exponential• Multiple Linear Regression

Output variable:• Yearly average temperature (numerical)

Performance Measures: MAPE - To evaluate the performance of each model and select the best modelRMSE - To check each model for overfitting

Forecasting Methods (yearly)

Performance Evaluation• Looking at the MAPE, Double exponential method was chosen as the best

method for forecasting yearly temperatures.

Forecast Results

• To test the validity of the Special Sustainability Programs, UNEP should create test groups which will follow the Sustainability program and check their actual temperatures against forecasted temperatures for any deviations.

• Since UNEP would want to focus on reducing the risk of rising temperatures, UNEP should make other UN member countries aware about the special sustainability program, so that International treaties for climate control could be signed.

• Given the CO2 emissions have a strong correlation with the increasing temperatures, UNEP should introduce Carbon tax worldwide.

• A carbon tax is a fee for making users of fossil fuels pay for climate damage their fuel use imposes by releasing carbon dioxide into the atmosphere, and for motivating switches to clean energy.

• UNEP could also collaborate with agencies such as Energy Coordinating Agency (ECA) which specialize in providing infrastructural support to curb temperature increases.

Recommendation