3,322 research outputs found
Forecast generation for real-time control of urban drainage systems using greybox modelling and radar rainfall
State-space adjustment of radar rainfall and stochastic flow forecasting for use in real-time control of urban drainage systems
Wildlife Communication
This report contains a progress report for the ph.d. project titled “Wildlife Communication”. The project focuses on investigating how signal processing and pattern recognition can be used to improve wildlife management in agriculture. Wildlife management systems used today experience habituation from wild animals which makes them ineffective. An intelligent wildlife management system could monitor its own effectiveness and alter its scaring strategy based on this
Probabilistic runoff volume forecasting in risk-based optimization for RTC of urban drainage systems
Towards a domain-based framework for use of rainfall forecasts in control of integrated urban wastewater systems
Evaluation of Maximum a Posteriori Estimation as Data Assimilation Method for Forecasting Infiltration-Inflow Affected Urban Runoff with Radar Rainfall Input
High quality on-line flow forecasts are useful for real-time operation of urban drainage systems and wastewater treatment plants. This requires computationally efficient models, which are continuously updated with observed data to provide good initial conditions for the forecasts. This paper presents a way of updating conceptual rainfall-runoff models using Maximum a Posteriori estimation to determine the most likely parameter constellation at the current point in time. This is done by combining information from prior parameter distributions and the model goodness of fit over a predefined period of time that precedes the forecast. The method is illustrated for an urban catchment, where flow forecasts of 0–4 h are generated by applying a lumped linear reservoir model with three cascading reservoirs. Radar rainfall observations are used as input to the model. The effects of different prior standard deviations and lengths of the auto-calibration period on the resulting flow forecast performance are evaluated. We were able to demonstrate that, if properly tuned, the method leads to a significant increase in forecasting performance compared to a model without continuous auto-calibration. Delayed responses and erratic behaviour in the parameter variations are, however, observed and the choice of prior distributions and length of auto-calibration period is not straightforward
Comparison of two stochastic techniques for reliable urban runoff prediction by modeling systematic errors
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