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Omar Wani
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The potential of knowing more: A review of data-driven urban water management
S Eggimann, L Mutzner, O Wani, MY Schneider, D Spuhler, ...
Environmental science & technology 51 (5), 2538-2553, 2017
2202017
Smart urban water systems: what could possibly go wrong?
M Moy de Vitry, MY Schneider, OF Wani, L Manny, JP Leitão, S Eggimann
Environmental Research Letters 14 (8), 081001, 2019
392019
Parameter estimation of hydrologic models using a likelihood function for censored and binary observations
O Wani, A Scheidegger, JP Carbajal, J Rieckermann, F Blumensaat
Water Research, 2017
372017
Residual uncertainty estimation using instance-based learning with applications to hydrologic forecasting
O Wani, JVL Beckers, AH Weerts, DP Solomatine
Hydrology and Earth System Sciences 21 (8), 4021, 2017
332017
Comparing approaches to deal with non‐gaussianity of rainfall data in kriging‐based radar‐gauge rainfall merging
F Cecinati, O Wani, MA Rico‐Ramirez
Water Resources Research 53 (11), 8999-9018, 2017
322017
Using a simple post-processor to predict residual uncertainty for multiple hydrological model outputs
LB Ehlers, O Wani, J Koch, TO Sonnenborg, JC Refsgaard
Advances in Water Resources 129, 16-30, 2019
122019
Exploring a copula-based alternative to additive error models—for non-negative and autocorrelated time series in hydrology
O Wani, A Scheidegger, F Cecinati, G Espadas, J Rieckermann
Journal of Hydrology 575, 1031-1040, 2019
92019
Accounting for variation in rainfall intensity and surface slope in wash-off model calibration and prediction within the Bayesian framework
M Muthusamy, O Wani, A Schellart, S Tait
Water research 143, 561-569, 2018
62018
Impact of different sources of precipitation data on urban rainfall-runoff predictions: A comparison of rain gauges, commercial microwave links and radar
A Disch, A Scheidegger, OF Wani, J Rieckermann
Rainfall Monitoring, Modelling and Forecasting in Urban Environment …, 2019
32019
Parameter estimation of urban drainage models using binary observations from low-cost sensors
O Wani, F Blumensaat, A Scheidegger, T Doppler, J Rieckermann
URBAN DRAINAGE MODELLING, 5, 2015
22015
Evaluation and correction of uncertainty due to Gaussian approximation in radar-rain gauge merging using kriging with external drift
F Cecinati, O Wani, MA Rico-Ramirez
AGU Fall Meeting Abstracts 2016, H11G-06, 2016
12016
Implications of risk perception on flood management and warning: 2014 Kashmir floods as a case study
R Ghani
12015
Generating risk maps for river migration using probabilistic modeling
O Wani, B Noh, K Dunne, M Lamb
EGU23, 2023
2023
Probabilistic SAR-based water segmentation with adapted Bayesian convolutional neural network
V Hertel, C Chow, O Wani, M Wieland, S Martinis
Remote Sensing of Environment 285, 113388, 2023
2023
Predicting River Migration Hazards with Stochastic Modeling
B Noh, O Wani, K Dunne, MP Lamb
Fall Meeting 2022, 2022
2022
A physics-informed machine learning model for streamflow prediction
L Zhang, DG Bellugi, S Li, A Kamat, J Kadi, E Moges, G Gorski, O Wani, ...
Fall Meeting 2022, 2022
2022
SAR-based probabilistic water segmentation with adapted Bayesian convolutional neural networks
V Hertel, O Wani, A Schneibel, M Wieland, S Martinis, CWY Chow
2022
Does distributed monitoring improve the calibration of urban drainage models?
O Wani, M Maurer, J Rieckermann, F Blumensaat
International Urban Drainage Modeling Conference, 2022, Costa Mesa, USA, 2021
2021
Is flow control in a space-constrained drainage network effective? A performance assessment for combined sewer overflow reduction
W Wang, JP Leitão, O Wani
Environmental Research 202, 111688, 2021
2021
Climate emergency: It is time to prepare for severe floods that will hit South Asia
O Wani
Down to Earth, Aug 2021, URL: https://tinyurl.com/35hteaur, 2021
2021
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