Ashland City, Tennessee, located within the Lower Cumberland Sycamore
watershed, is highly susceptible to flooding due to increased upstream water
levels. This study aimed to develop a robust flood prediction model for the
city, utilizing water level data at 30-minute intervals from ten USGS gauge
stations within the watershed. A Gated Recurrent Unit (GRU) network, known for
its ability to effectively process sequential time-series data, was used. The
model was trained, validated, and tested using a year-long dataset (January
2021-January 2022), and its performance was evaluated using statistical metrics
including Nash-Sutcliffe Efficiency (NSE), Root Mean Squared Error (RMSE),
Percent Bias (PBIAS), Mean Absolute Error (MAE), and Coefficient of
Determination (R^2). The results demonstrated a high level of accuracy, with
the model explaining 98.2% of the variance in the data. Despite minor
discrepancies between predicted and observed values, the GRU model proved to be
an effective tool for flood prediction in Ashland City, with potential
applications for enhancing disaster preparedness and response efforts in
Ashland City.

为阿什兰德城开发了一种可靠的洪水预测模型，使用游击递归单元（GRU）网络处理与时间序列相关的水位数据，在统计指标（如 Nash-Sutcliffe 效率、均方根误差、百分偏差、平均绝对误差和确定系数）的评估下，该模型表现出高水平的准确性，对数据方差解释率为 98.2%。尽管存在预测值与观测值之间的轻微差异，但 GRU 模型证明在阿什兰德城的洪水预测中是一种有效的工具，可用于增强该城市的灾害应对和准备工作。

田纳西州阿什兰市洪水预测的 GRU 神经网络实现

Implementing a GRU Neural Network for Flood Prediction in Ashland City,  Tennessee

Vulnerability and risk assessment of neighborhoods is essential for effective
disaster preparedness. Existing traditional systems, due to dependency on
time-consuming and cost-intensive field surveying, do not provide a scalable
way to decipher warnings and assess the precise extent of the risk at a
hyper-local level. In this work, machine learning was used to automate the
process of identifying dwellings and their type to build a potentially more
effective disaster vulnerability assessment system. First, satellite imageries
of low-income settlements and vulnerable areas in India were used to identify 7
different dwelling types. Specifically, we formulated the dwelling type
classification as a semantic segmentation task and trained a U-net based neural
network model, namely TernausNet, with the data we collected. Then a risk score
assessment model was employed, using the determined dwelling type along with an
inundation model of the regions. The entire pipeline was deployed to multiple
locations prior to natural hazards in India in 2020. Post hoc ground-truth data
from those regions was collected to validate the efficacy of this model which
showed promising performance. This work can aid disaster response organizations
and communities at risk by providing household-level risk information that can
inform preemptive actions.

使用机器学习对印度低收入居民区进行卫星图像分析，经过住宅类型识别和风险评估模型计算确定的住宅风险水平，可为灾难响应组织和有风险的社区提供基于家庭层面的风险信息，以便采取先发制人的行动。

利用卫星图像进行灾害风险评估的住宅类型分类

Dwelling Type Classification for Disaster Risk Assessment Using Satellite Imagery

Open Trip Planner was identified as the most promising open source
multi-modal trip planning software. Open Street Map, which provides mapping
data to Open Trip Planner, is one of the most well-known open source
international repository of geographic data. General Transit Feed
Specification, which provides transportation data to Open Trip Planner, has
been the standard for describing transit systems and platform for numerous
applications. Together, when used to implement an instance of Open Trip
Planner, these software has been helping in traffic decongestion all over the
world by assisting commuters to shift from using private transportation modes
to public ones. Their potential however goes beyond providing multi-modal
public transportation routes. This paper aims to first discuss the researchers'
experience in implementing a public transportation route planner for the
purpose of traffic decongestion.The researchers would examine the prospective
of using the system for disaster preparedness and recovery and concrete ways on
how to realize them.

Open Trip Planner 是一个有潜力的开源出行规划软件，借助于 Open Street Map 和 General Transit Feed Specification 等软件，它有望在交通拥堵以及灾害应对等领域发挥重要作用。