songshgeo / flood-collective-memory

estimate collective memory with survey data.

Flooding Memory

This project aims to improve socio-hydrological models that simulate social and flood interactions using the Universal Decay Model proposed by Candia et al. The paper is in preparation and the full project code and data will be made public after the official publication.

Related paper is under review in *Journal of Flood Risk Management.

Research framework

Assuming social memory of floods as an important partial, Di Baldassarre et al. (2013), Viglione et al. (2014), Di Baldassarre etal. (2015) raised models to capture the co-evolution of society and flood events in a socio-hydrological approach. On the base of their conceptualizations, we proposed a possible substitution of society module by the Universal Decay Model (UDM) of collective memory (Candia et al., 2019) as it provides a more explicit explanation to this kind of social process.

As all modules and their relations illustrated in Fig. 1, we choose the model by Di Baldassarre (2015) as a basic version to test the substitution, where collective memory plays a controlling role but at a lack of data as a support. First of all, we select a typical floodplain to do a survey regarding memory of major historical floods. Then, after processing the questionnaires data from survey, we use the dataset to fit memory decay rate under different alternatives of the society module. Finally, once this substitution works, we do simulations in socio-hydrological models to test if any essential difference can be demonstrated, otherwise there is no need to make the module more complicated.

Document contents：

generate_floods.py provides the algorithms for generating random flood sequences.

collective_memory.py is the algorithm used in the simulation of the socio-hydrological model

decay_rates_fit.py provides a process for fitting memory decay rates using survey data.

data_process.py includes some functions related to actual datasets.

model_test provides some functions for testing models' performance

simulating provides encapsulated functions for simulating by socio-hydrological models.

processing_questionnaires_data.ipynb used in processing data.

plotting_result1.ipynb, plotting_result2.ipynb, plotting_result3.ipynb, three notebooks were used in visualization and analysis.

data/Processed_data_in_English.csv is our survey data after processing.

Versions and Dependencies

We use python 3.7 and it works well.

Dependent libraries include: Pandas, Numpy

Major references：

Di Baldassarre, Giuliano, et al. "Debates—Perspectives on socio‐hydrology: Capturing feedbacks between physical and social processes." Water Resources Research 51.6 (2015): 4770-4781.

Candia, C., C. Jara-Figueroa, C. Rodriguez-Sickert, A.-L. Barabási, and C. A. Hidalgo. 2019. The universal decay of collective memory and attention. Nature Human Behaviour 3(1):82–91.

Ciullo, A., Viglione, A., Castellarin, A., Crisci, M., & Di Baldassarre, G. (2017). Socio-hydrological modelling of flood-risk dynamics: comparing the resilience of green and technological systems. Hydrological sciences journal, 62(6), 880-891.

Viglione, Alberto, et al. "Insights from socio-hydrology modelling on dealing with flood risk–roles of collective memory, risk-taking attitude and trust." Journal of Hydrology 518 (2014): 71-82.