1. Collecting Meaningful Data
In order to make a prediction about the sea level rise worldwide, we
looked for different factors which are known causes for this trend and
collected chronological data on a large time span to make the prediction
The three factors used to predict sea level rise are the following:
- Global Temperature Trends Data are included from the GISS Surface Temperature (GISTEMP)
analysis and the global component of Climate at a Glance (GCAG). They
measure annual mean temperature anomalies in degrees Celsius from 1880
to the present.
- Ice Caps Melting Trends Average cumulative mass balance of “reference” Glaciers worldwide
from 1945-2014 sourced from US EPA and the World Glacier Monitoring
Service (WGMS). This is cumulative change in mass balance of a set of
“reference” glaciers worldwide beginning in 1945.
- CO2 Production Trends Global CO2 Emissions from fossil-fuels annually since 1751 till 2014.
2. Exploiting Data for Predictions
We performed a multi-classifier regression using the Numpy library on
the three datasets and we created a model able to perform predictions
on the sea level rise for future years.
Our prediction is that by 2100 the sea level is gonna increase by 75 centimetres, reaching a total increase of 1 meter since 1945.
It is interesting to note that the other factors we took in account
when performing our linear regression make our prediction make the
prediction diverge by the current progression of sea level raise.
The impact of temperature increase, ice melting and CO2 production
seems to play a major role in the increasing rate of sea level rise.
3. Comparing our Prediction with Real-World Scientific Models
We compared our predictions with those which were formulated by
scientific experts in the environmental and climatological research
This is especially relevant since our main purpose was to show how
some alarming trends could be spotted even by common people with basic
knowledge in the field and some data processing skills.
Researcher's forecasts varies widely, going from 20 to around 80 cm for 2100.
In this sense, our forecast is aligned with official predictions. The
NASA Empirical Projection in which gas emissions decline after a peak,
seems to be the most consistent with our findings, forecasting an
increase in sea level of approximately 0.75 meters by the end of the
4. Applying our Model to the Local Context
Since Trieste is a coastal city, we decided to consider the consequences of our predicted sea level rise on the city of Trieste.
After an evaluation of socioeconomical activities in the areas
touched by the sea level increase, we denoted x critical aspects which
will be heavily damaged:
- Economical activities, especially those in
relation with the Port of Trieste, which is notably the first in Italy
for the volume of its foreign trades.
- Artistic and cultural patrimony, since all the historical part of the city will be submerged, including Piazza dell'Unità d'Italia and the Città Vecchia.
- Demographic displacement, since many
residential areas could be affected by the increase of water level,
renting cost would probably increase and social activities will change .
Global and Forecasting Related
Local data and informations
Challenges in the development process
The main challenges we faced during the creation of our project are
undoubtedly the creation of a valid model which could take in account
many different factors and the search for specific databases that fitted