A machine learning approach to evaluate the spatial variability of New York City's 311 street flooding complaints

Author:

Agonafir Candace,Lakhankar Tarendra,Khanbilvardi Reza,Krakauer Nir,Radell Dave,Devineni Naresh

Funder

NOAA Center for Earth System Sciences and Remote Sensing Technologies

Publisher

Elsevier BV

Subject

Urban Studies,General Environmental Science,Ecological Modeling,Geography, Planning and Development

Reference79 articles.

1. Understanding New York City street flooding through 311 complaints;Agonafir;Journal of Hydrology,2021

2. Flooding in the Nechako River basin of Canada: A random forest modeling approach to flood analysis in a regulated reservoir system;Albers;Canadian Water Resources Journal,2015

3. Random forests and decision trees;Ali;International Journal of Computer Science Issues,2012

4. Demystifying uncertainty in PM10 susceptibility mapping using variable drop-off in extreme-gradient boosting (XGB) and random forest (RF) algorithms;AlThuwaynee;Environmental Science and Pollution Research,2021

5. Impacts of changes in precipitation amount and distribution on water resources studied using a model rainwater harvesting system;Asadieh;Journal of the American Water Resources Association,2016

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