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aatwum's Projects

lidar2dems

Utilities using PDAL and PCL to create DTMs, DSMs, and CHMs from lidar data

local-coastal-flood-risk

Characterizing flood hazard estimates used by decision makers

LUP4LDN integrates Land Degradation Neutrality into participatory Land Use Planning via an interface that allows users to evaluate Land Use Management transition scenarios, providing visual and quantitative representations of Land Degradation gains and losses.

mangrove-rse

Understanding mangroves vegetation of Ghana based on Landsat time series in Google Earth Engine.

manyfews

The Many Flood Early Warning System

mapping_fbfs

Mapping Flood-based Farming Systems with Bayesian networks

maps2winbugs

a QGIS plugin to facilitate data processing for Bayesian spatial modeling

master-thesis-bayesiancnn

Master Thesis on Bayesian Convolutional Neural Network using Variational Inference

merged_global_pluvial_floods_database

Jupyter notebook that merges four raw flood databases into one single global pluvial flood database.

mitgcmtools.jl

Tools to interact with MITgcm (setup, run, output, plot, etc)

modelling_fbfs

Modelling Risk and Uncertainty in Flood-based farming systems

montecarlos_slc

Probabilistic shoreline change modeling

multivariate-facebook-prophet-time-series-forecasting-template

Template: Multivariate Facebook Prophet Time Series Forecasting

nasa-sees-2021-emergency-preparedness

NASA SEES Emergency Preparedness Team 2021: We used Google Earth Engine tools and satellite imagery to examine patterns with extreme heat, wildfires, floods, and tropical storms. This repository includes both our presentation and code for our research. Our presentation explains our project's code and methodology as well as our future interests.

natural-disaster-detection

Python script that detects natural disasters (Flood, Earthquake, Wildfire, Cyclone) from the input video and regenerates the video.

nextgisweb

Web GIS framework by NextGIS

oceananigans.jl

🌊 Julia software for fast, friendly, flexible, ocean-flavored fluid dynamics on CPUs and GPUs

oceancolordata.jl

ocean color data processing and analysis

oceanrobots.jl

Simulation, processing, and analysis of data generated by scientific robots in the Ocean.

oceanstateestimation.jl

Ocean State Estimation

onequantum-ghana

OneQuantum Ghana and PY4CA Joint Training on Introduction to Programming with Python

onequantum-mentorship-apr2022

This repository would include the coding tasks assigned by my mentor, Thomas Skripka.

parallel.gamit

Parallel.GAMIT is a Python software solution for parallel GPS processing of large regional or global networks. It also incorporates a metadata and RINEX data management tool that guarantees a consistent archive.

pgmpy

Python Library for learning (Structure and Parameter), inference (Probabilistic and Causal), and simulations in Bayesian Networks.

physics-informed-neural-network-for-flood-forecasting

The synthetic datasets generation process starts with generating hydrographs which required to run the FullSWOF-2D to obtain the final outputs. The SCS Hydrograph method is applied as a first step where it takes catchment characteristics and rainfall intensities as input and generates Synthetic Unit Hydrographs. The rainfall intensities considered here ranges from 1 to 6 minutes at an interval of 1 minute each. Therefore creating 6 different unit period hydrographs. These Synthetic Unit Hydrographs are transformed into Direct Runoff Hydrographs by multiplying it with rainfall depth as a next step.

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