| 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 | 242 | 2017 |
| 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 | 47 | 2019 |
| 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 | 41 | 2017 |
| 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 | 37 | 2017 |
| 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 | 36 | 2017 |
| 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 | 14 | 2019 |
| 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 | 10 | 2019 |
| 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 | 6 | 2023 |
| 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 | 6 | 2018 |
| 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 | 2 | 2019 |
| 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 | 2 | 2015 |
| Generating risk maps for river migration using probabilistic modeling O Wani, B Noh, K Dunne, M Lamb EGU23, 2023 | 1 | 2023 |
| 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 | 1 | 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 | 1 | 2021 |
| 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 | 1 | 2016 |
| Implications of risk perception on flood management and warning: 2014 Kashmir floods as a case study R Ghani | 1 | 2015 |
| A theory of stochastic fluvial landscape evolution GG Roberts, O Wani EarthArXiv, 2023 | | 2023 |
| Predicting River Migration Hazards with Stochastic Modeling B Noh, O Wani, K Dunne, MP Lamb AGU Fall Meeting Abstracts 2022, EP35A-06, 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, ... AGU Fall Meeting Abstracts 2022, H31E-01, 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 |
Jörg RieckermannEawagVerified email at eawag.ch
