Papers

63 papers indexed · Keshav three-pass summaries · ← randomstorms.net

FEATURED

Performance improvement of hydrological models using Unscented Kalman Filter-type data assimilation and data fusion

Parisa Almasi, Alireza Moghaddam Nia, Shahram Khalighi Sigaroodi, Ali Salajeghe et al. · 2026 · Applied Water Science

Applies the Unscented Kalman Filter (UKF) for state-only updating and a weighted-average data fusion scheme to three hydrological models (HBV, SWAT, WetSpa) differing in spatial complexity, on a single 216 km² semi-arid Iranian watershed. UKF yields up to a 29.8% NSE gain in validation; data fusion yields up to a 10.2% NSE gain, with UKF consistently outperforming fusion.

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real-time-control9 deep-learning8 reinforcement-learning6 urban-drainage6 water-quality5 bayesian-inference5 optimal-control4 combined-sewer-overflow4 probabilistic-machine-learning4 water-distribution-systems4 smart-water-systems4 open-source-software3

RECENTLY ADDED 8

Performance improvement of hydrological models using Unscented Kalman Filter-type data assimilation and data fusion

2026 · Parisa Almasi, Alireza Moghaddam Nia et al.

hydrological-modelingdata-assimilationkalman-filter

Learning to Reason in 13 Parameters

2026 · John X. Morris, Niloofar Mireshghallah et al.

parameter-efficient-fine-tuningreinforcement-learningreasoning

Zero-Shot Function Encoder-Based Differentiable Predictive Control

2026 · Hassan Iqbal, Xingjian Li et al.

zero-shot-controldifferentiable-predictive-controlneural-odes

RivRetrieve-Python: A Python package for facilitating and unifying access to global streamflow data

2026 · Simon Moulds, Thiago Nascimento et al.

streamflow-datahydrologyopen-source-software

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

2026 · Zhengqing Yuan, Hanchi Sun et al.

large-language-modelsmemory-efficient-trainingcpu-offloading

Transformers are Bayesian Networks

2026 · Greg Coppola

transformersbayesian-networksbelief-propagation

Inference Engineering

2026 · Philip Kiely

inference-engineeringllm-inferencegenerative-ai

CNN-Based Subnetworks for Improved Modelling of Extreme Flood and Drought Events in the Negro River Basin

2026 · Cíntia L. Eleutério, Carlos F. O. Mendes et al.

convolutional-neural-networkhydrological-modellingflood-drought-monitoring

ALL PAPERS · BY YEAR

2026

Performance improvement of hydrological models using Unscented Kalman Filter-type data assimilation and data fusion

Parisa Almasi, Alireza Moghaddam Nia, Shahram Khalighi Sigaroodi, Ali Salajeghe, Saeed Soltani Koopaei et al. · Applied Water Science

hydrological-modelingdata-assimilationkalman-filterdata-fusion

Learning to Reason in 13 Parameters

John X. Morris, Niloofar Mireshghallah, Mark Ibrahim, Saeed Mahloujifar

parameter-efficient-fine-tuningreinforcement-learningreasoninglow-rank-adaptation

Zero-Shot Function Encoder-Based Differentiable Predictive Control

Hassan Iqbal, Xingjian Li, Tyler Ingebrand, Adam Thorpe, Krishna Kumar et al. · Proceedings of Machine Learning Research

zero-shot-controldifferentiable-predictive-controlneural-odessystem-identification

RivRetrieve-Python: A Python package for facilitating and unifying access to global streamflow data

Simon Moulds, Thiago Nascimento, Ryan Riggs, George Allen, Frederik Kratzert · EGU General Assembly 2026

streamflow-datahydrologyopen-source-softwarelarge-sample-hydrology

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Zhengqing Yuan, Hanchi Sun, Lichao Sun, Yanfang Ye

large-language-modelsmemory-efficient-trainingcpu-offloadingsingle-gpu-training

Transformers are Bayesian Networks

transformersbayesian-networksbelief-propagationformal-verification

Inference Engineering

Philip Kiely · Baseten Books

inference-engineeringllm-inferencegenerative-aigpu-optimization githubgithubgithubgithubgithubgithubgithubgithub

CNN-Based Subnetworks for Improved Modelling of Extreme Flood and Drought Events in the Negro River Basin

Cíntia L. Eleutério, Carlos F. O. Mendes, Marcus W. Beims, Mircea Galiceanu, Naziano P. Filizola · Earth Systems and Environment

convolutional-neural-networkhydrological-modellingflood-drought-monitoringamazon-basin

Tanglewood Catchment I&I Analysis

Inframark · Internal Technical Report

infiltration-inflowwastewater-collectioncatchment-analysisdata-driven-analytics

2025

DeepBase: a Deep Learning-based Daily Baseflow Dataset across the United States

Parnian Ghaneei, Hamid Moradkhani · Scientific Data

baseflow-estimationdeep-learning-hydrologyungauged-basinsstreamflow-separation

Zero-Shot Transferable Solution Method for Parametric Optimal Control Problems

Xinjian Li, Kelvin Kan, Deepanshu Verma, Krishna Kumar, Stanley Osher et al.

optimal-controlzero-shot-transferimitation-learningfunction-encoders

Implementation of a Real-Time decision support system to reduce pollutant load-discharges in Madrid combined sewer system based on off-line and real time modelling

A. Lastra, A. Carrasco, A. Villanueva, D. Sunyer, Jordi Meseguer et al. · 13th Urban Drainage Modelling Conference, Innsbruck, Austria

combined-sewer-overflowreal-time-controlurban-drainagewater-quality

Anomaly Detection in Sewer Systems Using Gaussian Process Regression

Mohsen Rezaee, Peter Melville-Shreeve, Hussein Rappel · CCWI 2025 - 21st Computing & Control for the Water Industry Conference

anomaly-detectiongaussian-process-regressionsewer-systemsprobabilistic-forecasting

Algorithms for Optimization

Mykel J. Kochenderfer, Tim A. Wheeler · The MIT Press

optimization-algorithmsengineering-designconvex-optimizationnumerical-methods

Probabilistic Machine Learning: Advanced Topics (Supplementary Material)

probabilistic-machine-learningbayesian-inferencegraphical-modelsvariational-inference

2024

A data-driven method for estimating sewer inflow and infiltration based on temperature and conductivity monitoring

Jingyu Ge, Jiuling Li, Ruihong Qiu, Tao Shi, Chenming Zhang et al. · Water Research

sewer-monitoringinflow-infiltrationtime-series-reconstructiondata-driven

A data-driven method for estimating sewer inflow and infiltration based on temperature and conductivity monitoring

Jingyu Ge, Jiuling Li, Ruihong Qiu, Tao Shi, Chenming Zhang et al. · Water Research

sewer-monitoringinflow-infiltrationdata-drivenwater-quality-sensing

Learning Theory from First Principles

Francis Bach · MIT Press

statistical-learning-theorygeneralization-boundssupervised-learningoptimization

2023

Probabilistic Machine Learning: Advanced Topics

Kevin P. Murphy · MIT Press

probabilistic-machine-learningbayesian-inferencegenerative-modelsdeep-learning

Leveraging Transfer Learning in LSTM Neural Networks for Data-Efficient Burst Detection in Water Distribution Systems

Konstantinos Glynis, Zoran Kapelan, Martijn Bakker, Riccardo Taormina · Water Resources Management

burst-detectionwater-distribution-systemslstmtransfer-learning

Probabilistic Machine Learning: Advanced Topics

Kevin P. Murphy · MIT Press

probabilistic-machine-learningbayesian-inferencegenerative-modelsdeep-learning

Towards coordinated and robust real-time control: a decentralized approach for combined sewer overflow and urban flooding reduction based on multi-agent reinforcement learning

Zhiyu Zhang, Wenchong Tian, Zhenliang Liao · Water Research

urban-drainagereal-time-controlmulti-agent-reinforcement-learningcombined-sewer-overflow

Real-time control of combined sewer systems: Risks associated with uncertainties

J.A. van der Werf, Z. Kapelan, J. Langeveld · Journal of Hydrology

model-predictive-controlcombined-sewer-overflowurban-drainageuncertainty-analysis

An Introduction to Statistical Learning with Applications in Python

Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, Jonathan Taylor · Springer Texts in Statistics

statistical-learningmachine-learningpythonsupervised-learning

2022

Algorithms for Decision Making

Mykel J. Kochenderfer, Tim A. Wheeler, Kyle H. Wray · The MIT Press

decision-makingreinforcement-learningprobabilistic-reasoningmarkov-decision-processes

Flooding and Overflow Mitigation Using Deep Reinforcement Learning Based on Koopman Operator of Urban Drainage Systems

Wenchong Tian, Zhenliang Liao, Zhiyu Zhang, Hao Wu, Kunlun Xin · Water Resources Research

urban-drainagereinforcement-learningkoopman-operatorflood-mitigation

One script to solve it all – an open-source-based framework for a digital workflow based on WWTP data

Markus Ahnert, Stefan Hurzlmeier · Water Practice & Technology

wastewater-treatmentdigital-workflowenvironmental-data-scienceopen-source

The role of deep learning in urban water management: A critical review

Guangtao Fu, Yiwen Jin, Siao Sun, Zhiguo Yuan, David Butler · Water Research

deep-learningurban-water-managementanomaly-detectionflood-forecasting

Representing Socio-Economic Uncertainty in Human System Models

Jennifer Morris, John Reilly, Sergey Paltsev, Andrei Sokolov, Kenneth Cox · Earth's Future

uncertainty-quantificationenergy-transitionmulti-sector-dynamicsscenario-discovery

Hydrograph separation: How physically based is recursive digital filtering?

Klaus Eckhardt · Hydrology and Earth System Sciences (preprint)

hydrograph-separationbaseflow-estimationdigital-filteringgroundwater-hydrology

Equity in Water Resources Planning: A Path Forward for Decision Support Modelers

Sarah Fletcher, Antonia Hadjimichael, Julianne Quinn, Khalid Osman, Matteo Giuliani et al. · Journal of Water Resources Planning and Management

water-equitydecision-support-modelingwater-resources-planningenvironmental-justice

Probabilistic Machine Learning: An Introduction

Kevin P. Murphy · MIT Press

probabilistic-machine-learningbayesian-inferencedeep-learningsupervised-learning githubgithubgithubgithubgithubgithubgithubgithubhuggingface

High-Dimensional Data Analysis with Low-Dimensional Models: Principles, Computation, and Applications

John Wright, Yi Ma · Cambridge University Press

sparse-recoverylow-rank-modelscompressed-sensingconvex-optimization github

Baseflow Separation Using the Digital Filter Method: Review and Sensitivity Analysis

Taeuk Kang, Sangho Lee, Namjoo Lee, Youngkyu Jin · Water

baseflow-separationdigital-filterstreamflowhydrology

2021

Distributed and vectorized method of characteristics for fast transient simulations in water distribution systems

Gerardo Riaño-Briceño, Lina Sela, Ben R. Hodges · Computer-Aided Civil and Infrastructure Engineering

water-hammermethod-of-characteristicsparallel-computingwater-distribution-systems

pystorms: A simulation sandbox for the development and evaluation of stormwater control algorithms

Sara P. Rimer, Abhiram Mullapudi, Sara C. Troutman, Gregory Ewing, Benjamin D. Bowes et al. · arXiv

stormwater-controlsimulation-sandboxsmart-water-systemsopen-source-software

Scientific Visualization: Python + Matplotlib

Nicolas P. Rougier

scientific-visualizationmatplotlibpythondata-visualization

Convex Optimization for Trajectory Generation: A Tutorial On Generating Dynamically Feasible Trajectories Reliably And Efficiently

Danylo Malyuta, Taylor P. Reynolds, Michael Szmuk, Thomas Lew, Riccardo Bonalli et al.

trajectory-optimizationconvex-optimizationsequential-convex-programmingautonomous-vehicles

2020

A comprehensive review of deep learning applications in hydrology and water resources

Muhammed Sit, Bekir Z. Demiray, Zhongrun Xiang, Gregory J. Ewing, Yusuf Sermet et al. · Water Science & Technology

deep-learninghydrologywater-resourcessystematic-review

Rethinking the Framework of Smart Water System: A Review

Jiada Li, Xiafei Yang, Robert Sitzenfrei · Water

smart-water-systemswater-distribution-networksiot-watercyber-physical-systems

Drinking Water Distribution System Network Clustering Using Self-Organizing Map for Real-Time Demand Estimation

S. M. Masud Rana, Dominic L. Boccelli, Angela Marchi, Graeme C. Dandy · Journal of Water Resources Planning and Management

water-distribution-systemsdemand-estimationnetwork-clusteringself-organizing-maps

Industry engagement with control research: Perspective and messages

Tariq Samad, Margret Bauer, Scott Bortoff, Stefano Di Cairano, Lorenzo Fagiano et al. · Annual Reviews in Control

theory-practice-gapindustrial-controltechnology-transferadvanced-control

Statistical Learning Approaches for the Control of Stormwater Systems

Abhiram Mullapudi · University of Michigan (doctoral dissertation)

stormwater-controlreinforcement-learningbayesian-optimizationsmart-water-systems

acados – a modular open-source framework for fast embedded optimal control

Robin Verschueren, Gianluca Frison, Dimitris Kouzoupis, Jonathan Frey, Niels van Duijkeren et al.

embedded-optimal-controlmodel-predictive-controlnonlinear-mpcopen-source-software

Meshness of sewer networks and its implications for flooding occurrence

Julian David Reyes-Silva, Björn Helm, Peter Krebs · Water Science & Technology

urban-drainagegraph-theorysewer-networksflood-reliability

Centrality and shortest path length measures for the functional analysis of urban drainage networks

Julian D. Reyes-Silva, Jonatan Zischg, Christopher Klinkhamer, P. Suresh C. Rao, Robert Sitzenfrei et al. · Applied Network Science

urban-drainage-networkscomplex-networksgraph-theorysewer-systems

2019

The Linux Command Line, Fifth Internet Edition

William Shotts · LinuxCommand.org / No Starch Press

linuxcommand-lineshell-scriptingsystem-administration

2018

Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting

Bing Yu, Haoteng Yin, Zhanxing Zhu · arXiv

traffic-forecastinggraph-neural-networksspatio-temporal-learningtime-series-prediction

2017

Optimal Hydrograph Separation Using a Recursive Digital Filter Constrained by Chemical Mass Balance, with Application to Selected Chesapeake Bay Watersheds

Jeff P. Raffensperger, Anna C. Baker, Joel D. Blomquist, Jessica A. Hopple · U.S. Geological Survey Scientific Investigations Report 2017-5034

hydrograph-separationbase-flow-estimationgroundwater-streamflowrecursive-digital-filter

An automated toolchain for the data-driven and dynamical modeling of combined sewer systems

Sara Troutman, Nathaniel Schambach, Nancy Love, Branko Kerkez · Elsevier (Water Research)

combined-sewer-systemsdata-driven-modelingsmart-water-systemssystem-identification

2016

XGBoost: A Scalable Tree Boosting System

Tianqi Chen, Carlos Guestrin · KDD '16

gradient-boostingtree-ensemblescalable-machine-learningdistributed-computing githubgithub

Smarter Stormwater Systems

Branko Kerkez, Cyndee Gruden, Matthew Lewis, Luis Montestruque, Marcus Quigley et al. · Environmental Science & Technology

stormwater-managementreal-time-controlurban-water-qualityinternet-of-things

2014

A Tutorial on Principal Component Analysis

Jonathon Shlens

dimensionality-reductionprincipal-component-analysislinear-algebratutorial

2013

Bayesian Filtering and Smoothing

Simo Särkkä · Cambridge University Press (IMS Textbooks, volume 3)

bayesian-filteringkalman-filteringparticle-filtersstate-space-models

1985

Optimal Real-Time Control of Combined Sewer Networks

M. Papageorgiou, R. Mayr · IFAC Symposium on Systems Analysis Applied to Water and Related Land Resources, Lisbon, Portugal

optimal-controlreal-time-controlcombined-sewer-networkswater-resources

1975

Automatic Control of Large-Scale Combined Sewer Systems

John W. Labadie, Neil S. Grigg, Bruce H. Bradford · Journal of the Environmental Engineering Division, ASCE

combined-sewer-overflowoptimal-controlhierarchical-controlurban-stormwater

Undated

1-s2.0-S0043135424009023-mmc1

sewer-systemsinflow-infiltrationgroundwater-monitoringbayesian-inference

Wastewater Treatment Plant Operator Certification Training: Module 5 — Disinfection and Chlorination

wastewater-treatmentdisinfectionchlorinationoperator-training

On the slow death of scaling

scaling-lawslarge-language-modelscompute-efficiencyai-policy

The potential of knowing more – a review of data-driven urban water management

Sven Eggimann, Lena Mutzner, Omar Wani, Mariane Yvonne Schneider, Dorothee Spuhler et al. · Environmental Science & Technology

urban-water-managementdata-driven-infrastructuresmart-sensingwastewater-epidemiology

Base Flow Discharge to Streams and Rivers: Terminology, Concepts, and Base-flow Estimation using Optimal Hydrograph Separation

· U.S. Geological Survey / USGS National Water-Quality Assessment (NAWQA) Program

base-flowhydrograph-separationgroundwater-dischargestreamflow

Smart Stormwater Management Using Sensor-Driven Control Strategies

Christopher J. Marshall, Natalie B. Rivera, Anthony S. Whitman, Laura K. Delgado, Maryam Yusuff

stormwater-managementreal-time-controlsensor-networksurban-hydrology

Chapter Five: Linear Systems

linear-systemsstate-spacematrix-exponentialconvolution