Virtual sensors for local, three dimensional, broadband multiple-channel active noise control and the effects on the quiet zones

In this paper, two state of the art virtual sensor algorithms, i.e. the Remote Microphone Technique (RMT) and the Kalman filter based Virtual Sensing algorithm (KVS) are compared, in both state space (SS) and finite impulse response (FIR) implementations. The comparison focuses on the accuracy of the estimated sound pressure signals at the virtual locations and is based on actual measurements in a practical situation. The FIR implementation of the RMT algorithm was found to produce the most reliable results. It is implemented in a local, three dimensional, real-time, multiple-channel, broadband active noise control system. With this implementation, the benefits and limitations of the RMT-ANC system on the shape and size of the quiet zones are investigated

Impacts of trends and uncertainties in river flooding due to climate change

Projected climate changes will have an effect on frequencies and duration of\ud river flooding and therefore on design criteria for dikes or on risk assessment. In\ud addition to existing sources of uncertainty, extremes and variability of climatological\ud input will change. To deal with this problem the purpose of this project can be split into\ud two main parts. First, to identify possible effects of climate changes on extreme\ud discharges of rivers and particularly the uncertainty involved. Second, to determine the\ud appropriate level of modelling needed to predict such effects taking into account the\ud uncertainties. The major subsystems are climate, catchment and river. Important aspects\ud are the additional uncertainty introduced by each subsystem and the appropriate level of\ud modelling a subsystem. In this paper some preliminary excersises to address these\ud questions with respect to catchment and river are shown, based on very schematic\ud models not representing any particular catchment

Appropriate Models In Decision Support Systems For River Basin Management

In recent years, new ideas and techniques appear very quickly, like sustainability, adaptive management, Geographic Information System, Remote Sensing and participations of new stakeholders, which contribute a lot to the development of decision support systems in river basin management. However, the role of models still needs to be emphasized, especially for model-based decision support systems. This paper aims to find appropriate models for decision support systems. An appropriate system is defined as ‘the system can produce final outputs which enable the decision makers to distinguish different river engineering measures according to the current problem’. An appropriateness framework is proposed mainly based on uncertainty and sensitivity analysis. A flood risk model is used, as a part of the Dutch River Meuse DSS to investigate whether the appropriate framework works. The results showed that the proposed approach is applicable and helpful to find appropriate models

Identification of an appropriate low flow forecast model\ud for the Meuse River

This study investigates the selection of an appropriate low flow forecast model for the Meuse\ud River based on the comparison of output uncertainties of different models. For this purpose, three data\ud driven models have been developed for the Meuse River: a multivariate ARMAX model, a linear regression\ud model and an Artificial Neural Network (ANN) model. The uncertainty in these three models is assumed to\ud be represented by the difference between observed and simulated discharge. The results show that the ANN\ud low flow forecast model with one or two input variables(s) performed slightly better than the other statistical\ud models when forecasting low flows for a lead time of seven days. The approach for the selection of an\ud appropriate low flow forecast model adopted in this study can be used for other lead times and river basins\ud as well

Adding Value to Milk through the Production of Cheese and Other Dairy Products on the Farm in The Netherlands

Farm Management,

Identification of appropriate temporal scales of dominant low flow indicators in the Main River, Germany

Models incorporating the appropriate temporal scales of dominant indicators for low flows are assumed to perform better than models with arbitrary selected temporal scales. In this paper, we investigate appropriate temporal scales of dominant low flow indicators: precipitation (P), evapotranspiration (ET) and the standardized groundwater storage index (G). This analysis is done in the context of low flow forecasting with a lead time of 14 days in the Main River, a tributary of the Rhine River, located in Germany. Correlation coefficients (i.e. Pearson, Kendall and Spearman) are used to reveal the appropriate temporal scales of dominant low flow indicators at different time lags between low flows and indicators and different support scales of indicators. The results are presented for lag values and support scales, which result in correlation coefficients between low flows and dominant indicators falling into the maximum 10% percentile range. P has a maximum Spearman correlation coefficient (ρ) of 0.38 (p = 0.95) at a support scale of 336 days and a lag of zero days. ET has a maximum ρ of –0.60 (p = 0.95) at a support scale of 280 days and a lag of 56 days and G has a maximum ρ of 0.69 (p = 0.95) at a support scale of 7 days and a lag of 3 days. The identified appropriate support scales and lags can be used for low flow forecasting with a lead time of 14 days

Use of regional climate model simulations as input for hydrological models for the Hindukush-Karakorum-Himalaya region

The most important climatological inputs required for the calibration and validation of hydrological models are temperature and precipitation that can be derived from observational records or alternatively from regional climate models (RCMs). In this paper, meteorological station observations and results of the PRECIS (Providing REgional Climate for Impact Studies) RCM driven by the outputs of reanalysis ERA 40 data and HadAM3P general circulation model (GCM) results are used as input in the hydrological model. The objective is to investigate the effect of precipitation and temperature simulated with the PRECIS RCM nested in these two data sets on discharge simulated with the HBV model for three river basins in the Hindukush-Karakorum-Himalaya (HKH) region. Six HBV model experiments are designed: HBV-Met, HBV-ERA and HBV-Had, HBV-MetCRU-corrected, HBV-ERABenchmark and HBV-HadBenchmark where HBV is driven by meteorological stations data, data from PRECIS nested in ERA-40 and HadAM3P, meteorological stations CRU corrected data, ERA-40 reanalysis and HadAM3P GCM data, respectively. Present day PRECIS simulations possess strong capacity to simulate spatial patterns of present day climate characteristics. However, also some quantitative biases exist in the HKH region, where PRECIS RCM simulations underestimate temperature and overestimate precipitation with respect to CRU observations. The calibration and validation results of the HBV model experiments show that the performance of HBV-Met is better than the HBV models driven by other data sources. However, using input data series from sources different from the data used in the model calibration shows that HBV-Had is more efficient than other models and HBV-Met has the least absolute relative error with respect to all other models. The uncertainties are higher in least efficient models (i.e. HBV-MetCRU-corrected and HBV-ERABenchmark) where the model parameters are also unrealistic. In terms of both robustness and uncertainty ranges the HBV models calibrated with PRECIS output performed better than other calibrated models except for HBV-Met which has shown a higher robustness. This suggests that in data sparse regions such as the HKH region data from regional climate models may be used as input in hydrological models for climate scenarios studies