Climada-BR is a project of LABIC (Laboratory of Computational Intelligence from USP São Carlos), that seeks to improve hazard assesment in Brazil using LLM's (Large Language Models) for extracting event data from auternative sources. This repository is a fork from the original CLIMADA which stands for CLIMate ADAptation and is a probabilistic natural catastrophe impact model, that also calculates averted damage (benefit) thanks to adaptation measures of any kind (from grey to green infrastructure, behavioural, etc.). The installation Guide is at the end of the page.
Our proposal addresses the challenge of improving hazard assessment by extracting event data from alternative sources to enhance risk analysis and climate adaptation in Brazil. Hazards, dynamic events, or circumstances that can cause harm, play a significant role as crucial input parameters in modeling the impact of climatic events. For example, changes in weather patterns or the detection of invasive vector species exemplify hazards that significantly influence these models.
Hazards are fundamental for calibrating models, acting as key parameters to better understand the risks associated with climate change impacts. In this context, CLIMADA model, short for CLIMate ADAptation, serves as a probabilistic natural catastrophe impact model, encompassing the evaluation of averted damage (benefit) resulting from diverse adaptation measures, ranging from grey to green infrastructure and behavioral adaptations [1]. With these models, public managers and researchers can conduct risk analyses, taking into account the effects of climatic events based on local data. This enables the formulation of preventive plans and awareness strategies. Presently, CLIMADA offers historical and probabilistic event sets for various time horizons, spanning past, present, and future climates. However, CLIMADA faces limitations when directly applied to the Brazilian context. These limitations stem from a scarcity of local data and the intricate regional complexities present in Brazil.
The overarching idea of the CLIMADA-BR proposal is to develop an open-source software tailored for Brazilian decision-makers. This software aims to act as a facilitating tool for community participation and crowd-sourcing data collection. By enabling the extraction of hazards from diverse local sources, such as news, bulletins, and reports from public managers, CLIMADA-BR strives to facilitate comprehensive climate risk modeling. This, in turn, enhances decision-making processes and fosters community involvement in tracking climate change impacts at the local level.
Currently, CLIMADA is the first global platform for probabilistic multi-hazard risk modeling, incorporating uncertainty and sensitivity analyses. This model enables the assessment of natural hazard risks and the evaluation of adaptation options by comparing averted impacts to implementation costs. Hazard, in CLIMADA, is modeled as a probabilistic set of events, each representing intensity at geographical locations with an associated probability of occurrence. The risk of a single event is defined as its impact multiplied by its probability of occurrence. CLIMADA allows for globally consistent risk assessment from city to continental scale, considering historical data, future projections, and various adaptation options.
Our proposal is straightforward and has a direct impact on public managers, researchers, and stakeholders interested in monitoring and understanding the impact of climate change in impoverished regions. We propose extending the model to CLIMADA-BR, integrating Large Language Models (LLMs) for real-time hazard extraction from news, bulletins, and reports. This adaptation will significantly enhance the model's sensitivity to localized climate events, resulting in improved risk assessments for various societal sectors. The idea is to leverage significant advancements in LLMs to enrich the model's hazard database.
Our hypothesis is that the integration of Large Language Models (LLMs) into the CLIMADA-BR framework enhances the capacity to collect, classify, and extract hazards. This integration aims to provide a more accurate representation of climate impacts on a local scale, considering diverse regions in Brazil.
The LLMs employed will extract hazard events in the 5W1H format (what, where, when, who, why, and how), an area where the project coordinator already has expertise, particularly in climate change events [2,3]. To assess the software's performance, we will conduct experiments on events modeling variables related to the detection of invasive vector species and disease transmission dynamics, providing insights for public officials to make informed decisions regarding public health impacts.
This project's development involves two Ph.D. students under the coordinator's supervision, who are currently engaged in the creation of artificial intelligence models for event extraction and sensing. These students bring valuable expertise to the initiative, contributing to the advancement of the project's objectives.
First, we will conduct rigorous testing and validation of the CLIMADA-BR model, refining its functionalities based on feedback and real-world data. Simultaneously, we will collaborate with key stakeholders, including government agencies, public health institutions, and environmental organizations, to ensure the model's alignment with their needs. We will leverage existing collaborations with stakeholders such as the Center for Artificial Intelligence in Brazil (C4AI), IBM, and FAPESP to ensure widespread implementation and impactful utilization of CLIMADA-BR in addressing climate-related hazards.
Once validated, the CLIMADA-BR framework will be disseminated through workshops, training sessions, and online platforms, targeting decision-makers, researchers, and the public. An open-source release of the model will be pivotal to encourage broader adoption and continuous improvement. To maximize the project's impact, we will establish partnerships with local communities, leveraging their knowledge and contributing to the model's enrichment.
At CLIMADA-BR/doc/tutorial/TUTORIAL_BASE_CLIMADA_BR.ipynb we have a minimalistic example of how CLIMADA works, which can also be run via our Google Colab page.
To run it in your machine instead of Colab you need to follow the installation guide below and also download the gpw_v4_population_count_rev11_2020_30_sec.tif file, which needs to be put in the SYSTEM_DIR of climada or you need to pass the especific directory location in a part of the code (there are comments showing how to do it).
At CLIMADA-BR/climada/ we have our API climadaBR which encapsulates the main functions used on our project, they are the following:
climadabr = ClimadaBR(); Creates the object
climadabr.DefineExposures(self, countries, res_arcsec, fin_mode, data_dir); Define the exposures, currently only by country name (countries) and socio-economic value (fin_mode)
climadabr.DefineHazards(self, ds, n_ev); Define the hazards, receive a xarray.Dataset with the information and the number of events (n_ev)
climadabr.DefineRandomHazards(self); Define a random hypothetical hazard
climadabr.HazardFromCSV(self, csv_file, data_dir); Define hazard from csv file data
climadabr.HazardFromExcel(self, excel_file, data_dir); Define hazard from excel file data
climadabr.AddImpactFunc(self, imp_fun); Add a impact function to the ImpactFuncSet
climadabr.DefineRandomImpactFuncSet(self); Define a ImpactFuncSet with a single random ImpactFunc
climadabr.ImpactFuncSetFromExcel(self, excel_file, data_dir); Define a ImpactFuncSet from excel file data
climadabr.ComputeImpact(self); Compute the impact
Conversor.convert_datasus_data(file_name, by_month_only, max_month, end_file_name); Model data from a file downloaded from Datasus
Conversor.convert_news_data(file_name, severity_threshold, by_month_only, max_month, end_file_name); Model data from a file made from news analysis
At CLIMADA-BR/doc/dengue_docs/ we have the documentation used for defining our dengue hazards and impact functions, the files used in our Tutorial_DENGUE.ipynb are also there. There is a README.md file explaining our sources here.
At CLIMADA-BR/doc/tutorial/Tutorial_DENGUE.ipynb we have a tutorial similar to our minimalistic example but equipped with our Conversor class, which take the dengue files (read the DENGUE documentation to know where to put your files) and convert them into a way that CLIMADA-BR can read and do the impact calculation for us, and other specific functions. Its goal is to be where we test how close can we take the events from news and aproximate them to the oficial data from trustable sources.
Anaconda, following the installation instructions for your OS.git installed
(Windows and macOS users are good to go once Anaconda is installed).
On Ubuntu and Debian, you may use APT: apt update
apt install gitBoth commands will probably require administrator rights, which can be enabled by prepending sudo.
Create a workspace directory. To make sure that your user can manipulate it without special privileges, use a subdirectory of your user/home directory. Do not use a directory that is synchronized by cloud storage systems like OneDrive, iCloud or Polybox!
Open the command line and navigate to the workspace directory you created using cd.
Replace <path/to/workspace> with the path of the directory that contains the workspace folder:
cd <path/to/workspace> git clone https://github.com/Labic-ICMC-USP/CLIMADA-BR.gitclimada_env for installing CLIMADA.
Use the default environment specs in env_climada.yml to create it.
Then activate the environment: conda env create -n climada_env -f requirements/env_climada.yml
conda activate climada_envpip: python -m pip install -e ./hint:: Using a path ./ (referring to the path you are currently located at) will instruct pip to install the local files instead of downloading the module from the internet.
The -e (for "editable") option further instructs pip to link to the source files instead of copying them during installation.
This means that any changes to the source files will have immediate effects in your environment, and re-installing the module is never required.
python -m unittest climada.engine.test.test_impactExecuting CLIMADA for the first time will take some time because it will generate a directory tree in your home/user directory. If this test passes, great! You are good to go.
[1] KROPF, Chahan M. et al. Uncertainty and sensitivity analysis for probabilistic weather and climate-risk modelling: an implementation in CLIMADA v. 3.1. 0. Geoscientific Model Development, v. 15, n. 18, p. 7177-7201, 2022.
[2] GÔLO, Marcos Paulo Silva; ROSSI, Rafael Geraldeli; MARCACINI, Ricardo Marcondes. Learning to sense from events via semantic variational autoencoder. Plos one, v. 16, n. 12, p. e0260701, 2021.
[3] MATTOS, Joao Pedro Rodrigues; MARCACINI, Ricardo M. Semi-supervised graph attention networks for event representation learning. In: 2021 IEEE International Conference on Data Mining (ICDM). IEEE, 2021. p. 1234-1239.