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coastal_flooding

using social media to detect coastal flooding

coastal_flood_maps_arcpy

Python notebook used for flood maps calculation using ArcGIS' ArcPy package.

coastsat

Global shoreline mapping tool from satellite imagery

coessing2021

Python labs for COESSING (Coastal Ocean and Environment Summer School in Ghana) 2021

coessing2022-hub

This repo is for all Python/Julia content at COESSING 2022.

corr_meta

Supplementary script for meta-analysis primer

covid-19-data

A repository of data on coronavirus cases and deaths in the U.S.

crisis_mapping_flood

This code provides methodology for preparing flood hazard maps in a replicable way, particularly for the coastal cities where floods pose a recurrent danger because of climate change and its impact on sea level rise. Taking San Francisco, California as an example and using OpenStreetMap data from OSMnx Python Package, our study compiled a crisis map indicating hazard prone areas for evacuation management or emergency access.

cse-499-flood-forecasting

This is a repo for the course CSE 499 which contains all the files for the codebase of the project and the presentation files.

cw4floods

Main repository for challenge 31/2022: Flood forecasting: the power of citizen science

dabestr

Data Analysis with Bootstrap Estimation in R

datatools

A collection of data analysis tools for post-processing raw data and importing to python for further analysis.

data_coastal_flooding_lac_plosone_paper

Data of paper on Coastal Flooding in LAC (Effects of Climate Change on Exposure to Coastal Flooding in Latin America and the Caribbean), by B.G. Reguero, I.J. Losada, P. Díaz-Simal, F.J. Méndez and M .Beck.

ddbn

Data-driven Bayesian network developer

depression-analysis-toolbox

An ArcGIS toolbox for identifying nested depressions in digital elevation models (DEMs)

e-baynerd

Enhanced Bayesian Network for Raster Data

e17-co328-flood-forecasting-system

Floods are the most destructive form of natural hazards in both local and global context. This is true in terms of both loss of life and property damage. Early flood forecasting can be used to identify potential areas of flooding in order to develop mitigatory planning and evacuation programs to remove people from such areas during flooding and also to implement suitable preventive measures to avoid damage to properties. In this project, our main objective is to build a flood forecasting system for Mi Oya river basin(Sri Lanka). Mi Oya Basin is heavily affected by seasonal flooding and droughts. As per the available data, floods in the Mi Oya basin are unleashed due to river overflow and reservoir spilling. Out of the several reservoirs located in the basin, Tabbowa and Inginimitiya are crucial in worsening the flood impacts as these two reservoirs are frequently spilling under adverse weather conditions. As such, the prevalence of a real-time flood forecasting model with the incorporation of the reservoir operations for the entire basin is essential to alleviate the flood induced impacts while preserving the optimum volume of water in the major reservoirs in the basin.

earth-reverse-engineering

Reversing Google's 3D satellite mode

earthengine-py-notebooks

A collection of 300+ Jupyter Python notebook examples for using Google Earth Engine with interactive mapping

earthscope-gmtsar-shortcourse

This is a repository for course description and agenda.

earth_lab_project_flood_extent_analysis

Earth Lab @ UC Boulder, Colorado - Assessing the Extent of a Flood Event using Python: A Change Detection Approach with Landsat Imagery

ee-fastapi

Flood Detection with Google Earth Engine

ee_ipl_uv

Multitemporal Cloud Masking in the Google Earth Engine

estimating-coastal-city-flooding-risks

fast-prophet

faster implementation of Facebook's Prophet algorithm for time series forecasting

fast_coastal_adaptation_evaluation_santa_monica

This project lists the resources used to assess the efficacy of multiple coastal adaptation strategies in reducing flooding, economic damages, and impacts on the local population of Santa Monica Bay California.

flompy

Flood Mapping Python Toolbox: The FLOod Mapping PYthon toolbox is a free and open-source python toolbox for mapping of floodwater. It exploits the dense Sentinel-1 GRD intensity time series and is based on four processing steps. In the first step, a selection of Sentinel-1 images related to pre-flood (baseline) state and flood state is performed. In the second step, the preprocessing of the selected images is performed in order to create a co-registered stack with all the pre-flood and flood images. In the third step, a statistical temporal analysis is performed and a t-score map that represents the changes due to flood event is calculated. Finally, in the fourth step, a multi-scale iterative thresholding algorithm based on t-score map is performed to extract the final flood map. We believe that the end-user community can benefit by exploiting the FLOMPY's floodwater maps.

flood

Flood Areas Map (Israel) with Google earth engine, geemap library and streamlit

flood-detection

Flood detection with Matlab

flood-detection-algorithm-using-gee

A JavaScript code developed in Google Earth Engine (GEE) Platform to Detect Flooded Area along with Affected Population