Repo for flood prediction using LSTMs and HAND

Overview

Abstract

Every year, floods cause billions of dollars’ worth of damages to life, crops, and property. With a proper early flood warning system in place, decision-makers can take the necessary steps to prevent or at least mitigate the damage caused by floods. Although various flood prediction models exist, a majority of them fail to be fast, reliable, and detailed simultaneously. Our proposed system presents a novel hybrid flood prediction model using Long Short Term Memory(LSTM) for multivariate time series forecasting of water depth based on meteorological conditions and Height Above Nearest Drainage(HAND) to predict river stage in real-time and map the inundated areas for the corresponding water depth using enhanced HAND. Unlike traditional flood forecasting models, this hybrid approach is resource efficient and easy to implement making it highly practicable for real-time flood inundation mapping.

Methodology

The proposed system prioritizes quick development and real-time predictions without compromising on the accuracy. A range of factors affect the occurrences of riverine floods. However, climatological conditions are the major driving force behind them. Factors such as land use/land change and deforestation, although important, only affect flooding in the watershed over a long period of time. Hence, the proposed system used only meteorological conditions and DEM rasters for predicting floods over the next few days.

The relation between weather conditions and flood inundation is simplified by breaking the system into two modules. The first module being estimation of river stage height and the second one being flood inundation mapping. The system uses LSTMs, a data-driven empirical approach, to model the dependence of stage height on meteorological data and HAND, a simplified conceptual approach, to generate flood inundation maps based on the terrain of the watershed and the river stage height predicted by the first module.

Modules :

  1. Inundation Mapping - HAND algorithm to map inundated areas for a given stage height(as proposed in this paper).
  2. River Stage Estimation - Recurring neural networks (LSTMs) to predict the maximum stage height based on weather conditions of the last 3 days.
  3. Deforestation Analysis - Land use classification to identify the changing features of the area over time and identify the areas affected by deforestation.

Datasets

The proposed system uses different data for the three modules. Each of these are collected from different sources and processed separately. The module-wise requirements of data are as follows :

  1. Inundation Mapping:
    1. Digital Elevation Maps from United States Geological Survey
  2. River Stage Estimation:
    1. Meteorological data from National Climatic Data Center
    2. River stage height data from United States Army Corps of Engineers’ river gage data.
  3. Deforestation Analysis:
    1. Satellite images - Landsat 8, Landsat 5 from USGS Earth Explorer

Results

Stage Height Estimation

We tested our proposed system for Cedar Rapids, Iowa. Our experiments showed that features such as vegetation and soil type have little effect on short term flooding and can be disregarded for the prediction module. Testing multiple models showed that single output LSTM models perform better than single shot models. These models are stable upto lead times of 4 days with a Nash-Sutcliffe Efficiency greater than 0.5.

Flood Mapping

Each pixel of the inundation map raster is compared with a reference map created by ground-truthing to identify how many points were incorrectly classified as not flooded. The red areas in the image depict false negatives generated by the proposed system.

Code for SentiBERT: A Transferable Transformer-Based Architecture for Compositional Sentiment Semantics (ACL'2020).

SentiBERT Code for SentiBERT: A Transferable Transformer-Based Architecture for Compositional Sentiment Semantics (ACL'2020). https://arxiv.org/abs/20

Da Yin 66 Aug 13, 2022
Imaging, analysis, and simulation software for radio interferometry

ehtim (eht-imaging) Python modules for simulating and manipulating VLBI data and producing images with regularized maximum likelihood methods. This ve

Andrew Chael 5.2k Dec 28, 2022
Deep Ensembling with No Overhead for either Training or Testing: The All-Round Blessings of Dynamic Sparsity

[ICLR 2022] Deep Ensembling with No Overhead for either Training or Testing: The All-Round Blessings of Dynamic Sparsity by Shiwei Liu, Tianlong Chen, Zahra Atashgahi, Xiaohan Chen, Ghada Sokar, Elen

VITA 18 Dec 31, 2022
CyTran: Cycle-Consistent Transformers for Non-Contrast to Contrast CT Translation

CyTran: Cycle-Consistent Transformers for Non-Contrast to Contrast CT Translation We propose a novel approach to translate unpaired contrast computed

Nicolae Catalin Ristea 13 Jan 02, 2023
Pytorch reimplementation of PSM-Net: "Pyramid Stereo Matching Network"

This is a Pytorch Lightning version PSMNet which is based on JiaRenChang/PSMNet. use python main.py to start training. PSM-Net Pytorch reimplementatio

XIAOTIAN LIU 1 Nov 25, 2021
exponential adaptive pooling for PyTorch

AdaPool: Exponential Adaptive Pooling for Information-Retaining Downsampling Abstract Pooling layers are essential building blocks of Convolutional Ne

Alexandros Stergiou 55 Jan 04, 2023
A PyTorch implementation for our paper "Dual Contrastive Learning: Text Classification via Label-Aware Data Augmentation".

Dual-Contrastive-Learning A PyTorch implementation for our paper "Dual Contrastive Learning: Text Classification via Label-Aware Data Augmentation". Y

hoshi-hiyouga 85 Dec 26, 2022
A PyTorch Implementation of "SINE: Scalable Incomplete Network Embedding" (ICDM 2018).

Scalable Incomplete Network Embedding ⠀⠀ A PyTorch implementation of Scalable Incomplete Network Embedding (ICDM 2018). Abstract Attributed network em

Benedek Rozemberczki 69 Sep 22, 2022
Official Pytorch implementation of 'GOCor: Bringing Globally Optimized Correspondence Volumes into Your Neural Network' (NeurIPS 2020)

Official implementation of GOCor This is the official implementation of our paper : GOCor: Bringing Globally Optimized Correspondence Volumes into You

Prune Truong 71 Nov 18, 2022
Neural Network Libraries

Neural Network Libraries Neural Network Libraries is a deep learning framework that is intended to be used for research, development and production. W

Sony 2.6k Dec 30, 2022
Cerberus Transformer: Joint Semantic, Affordance and Attribute Parsing

Cerberus Transformer: Joint Semantic, Affordance and Attribute Parsing Paper Introduction Multi-task indoor scene understanding is widely considered a

62 Dec 05, 2022
Bayesian-Torch is a library of neural network layers and utilities extending the core of PyTorch to enable the user to perform stochastic variational inference in Bayesian deep neural networks

Bayesian-Torch is a library of neural network layers and utilities extending the core of PyTorch to enable the user to perform stochastic variational inference in Bayesian deep neural networks. Bayes

Intel Labs 210 Jan 04, 2023
Code for How To Create A Fully Automated AI Based Trading System With Python

AI Based Trading System This code works as a boilerplate for an AI based trading system with yfinance as data source and RobinHood or Alpaca as broker

Rubén 196 Jan 05, 2023
Weakly Supervised Learning of Instance Segmentation with Inter-pixel Relations, CVPR 2019 (Oral)

Weakly Supervised Learning of Instance Segmentation with Inter-pixel Relations The code of: Weakly Supervised Learning of Instance Segmentation with I

Jiwoon Ahn 472 Dec 29, 2022
📚 Papermill is a tool for parameterizing, executing, and analyzing Jupyter Notebooks.

papermill is a tool for parameterizing, executing, and analyzing Jupyter Notebooks. Papermill lets you: parameterize notebooks execute notebooks This

nteract 5.1k Jan 03, 2023
This repository focus on Image Captioning & Video Captioning & Seq-to-Seq Learning & NLP

Awesome-Visual-Captioning Table of Contents ACL-2021 CVPR-2021 AAAI-2021 ACMMM-2020 NeurIPS-2020 ECCV-2020 CVPR-2020 ACL-2020 AAAI-2020 ACL-2019 NeurI

Ziqi Zhang 362 Jan 03, 2023
Pyramid Pooling Transformer for Scene Understanding

Pyramid Pooling Transformer for Scene Understanding Requirements: torch 1.6+ torchvision 0.7.0 timm==0.3.2 Validated on torch 1.6.0, torchvision 0.7.0

Yu-Huan Wu 119 Dec 29, 2022
DISTIL: Deep dIverSified inTeractIve Learning.

DISTIL: Deep dIverSified inTeractIve Learning. An active/inter-active learning library built on py-torch for reducing labeling costs.

decile-team 110 Dec 06, 2022
Using Python to Play Cyberpunk 2077

CyberPython 2077 Using Python to Play Cyberpunk 2077 This repo will contain code from the Cyberpython 2077 video series on Youtube (youtube.

Harrison 118 Oct 18, 2022
This repo provides a demo for the CVPR 2021 paper "A Fourier-based Framework for Domain Generalization" on the PACS dataset.

FACT This repo provides a demo for the CVPR 2021 paper "A Fourier-based Framework for Domain Generalization" on the PACS dataset. To cite, please use:

105 Dec 17, 2022