Statistical post-processing of hydrological forecasts using Bayesian model averaging

Accurate and reliable probabilistic forecasts of hydrological quantities like runoff or water level are beneficial to various areas of society. Probabilistic state-of-the-art hydrological ensemble prediction models are usually driven with meteorological ensemble forecasts. Hence, biases and dispersion errors of the meteorological forecasts cascade down to the hydrological predictions and add to the errors of the hydrological models. The systematic parts of these errors can be reduced by applying statistical post-processing. For a sound estimation of predictive uncertainty and an optimal correction of systematic errors, statistical post-processing methods should be tailored to the particular forecast variable at hand. Former studies have shown that it can make sense to treat hydrological quantities as bounded variables. In this paper, a doubly truncated Bayesian model averaging (BMA) method, which allows for flexible post-processing of (multi-model) ensemble forecasts of water level, is introduced. A case study based on water level for a gauge of river Rhine, reveals a good predictive skill of doubly truncated BMA compared both to the raw ensemble and the reference ensemble model output statistics approach.Comment: 19 pages, 6 figure

A Simple Flood Forecasting Scheme Using Wireless Sensor Networks

This paper presents a forecasting model designed using WSNs (Wireless Sensor Networks) to predict flood in rivers using simple and fast calculations to provide real-time results and save the lives of people who may be affected by the flood. Our prediction model uses multiple variable robust linear regression which is easy to understand and simple and cost effective in implementation, is speed efficient, but has low resource utilization and yet provides real time predictions with reliable accuracy, thus having features which are desirable in any real world algorithm. Our prediction model is independent of the number of parameters, i.e. any number of parameters may be added or removed based on the on-site requirements. When the water level rises, we represent it using a polynomial whose nature is used to determine if the water level may exceed the flood line in the near future. We compare our work with a contemporary algorithm to demonstrate our improvements over it. Then we present our simulation results for the predicted water level compared to the actual water level.Comment: 16 pages, 4 figures, published in International Journal Of Ad-Hoc, Sensor And Ubiquitous Computing, February 2012; V. seal et al, 'A Simple Flood Forecasting Scheme Using Wireless Sensor Networks', IJASUC, Feb.201

Forecasting time series by means of evolutionary algorithms

Proceeding of: 8th International Conference in Parallel Problem Solving from Nature - PPSN VIII , Birmingham, UK, September 18-22, 2004.The time series forecast is a very complex problem, consisting in predicting the behaviour of a data series with only the information of the previous sequence. There is many physical and artificial phenomenon that can be described by time series. The prediction of such phenomenon could be very complex. For instance, in the case of tide forecast, unusually high tides, or sea surges, result from a combination of chaotic climatic elements in conjunction with the more normal, periodic, tidal systems associated with a particular area. Too much variables influence the behaviour of the water level. Our problem is not only to find prediction rules, we also need to discard the noise and select the representative data. Our objective is to generate a set of prediction rules. There are many methods tying to achieve good predictions. In most of the cases this methods look for general rules that are able to predict the whole series. The problem is that usually the time series has local behaviours that dont allow a good level of prediction when using general rules. In this work we present a method for finding local rules able to predict only some zones of the series but achieving better level prediction. This method is based on the evolution of set of rules genetically codified, and following the Michigan approach. For evaluating the proposal, two different domains have been used: an artificial domain widely use in the bibliography (Mackey-Glass series) and a time series corresponding to a natural phenomenon, the water level in Venice Lagoon.Investigation supported by the Spanish Ministry of Science and Technology through the TRACER project under contract TIC2002-04498-C05-

Mountain winds (revisited)

The prediction of extremely high wind speeds, at ground level on the downstream side of a mountain range, is possible by solving the initial value problem for a two-layered nonlinear shallow water model of the atmosphere. Three different numerical methods are described to find the solutions which may involve shocks: (1) the vonNeumann-Richtmyer artificial viscosity method, (2) a filtering scheme, and (3) a hybrid method

Model Spasial Resiko Banjir Rob karena Pemanasan Global sebagai Masukan Perencanaan Pesisir (Studi Kasus: Pesisir Kota Semarang)

This research will examine how local zoning predictions of flood risk in 2050 rob of 1‐3 m (Oceane World Conference 2007). This can not be separated from the prediction that global warming is happening now has melted the polar ice caps that increasethe volume of sea water, besides that warming temperatures would increase the number rising tide to the mainland that caused flooding rob (Diposaptono, 2008 and Kodatie, 2003 ). The purpose of this research is to develop models rob floods in 2050 with a Geographic Information System to obtain prediction of disaster risk zoning in these predictions are used spatial model approach. The data acquired and processed by spatially derived variables vulnerability and vulnerability, the vulnerability variables caused by the higher average sea level rise and the decline in the face of the land, and variables such as vulnerability vulnerability of settlements, infrastructure vulnerability, institutional vulnerability and social vulnerability . Of this application can be concluded that the model is dynamic enough to be developed following the development of customized ariable conditions in the study area was kepecayaan level, but in essence the model of disaster risk zoning susceptibility and vulnerability factors must exist to determine the level ofrisk while the variables can be adjusted.

Forecasting high waters at Venice Lagoon using chaotic time series analisys and nonlinear neural netwoks

Time series analysis using nonlinear dynamics systems theory and multilayer neural networks models have been applied to the time sequence of water level data recorded every hour at 'Punta della Salute' from Venice Lagoon during the years 1980-1994. The first method is based on the reconstruction of the state space attractor using time delay embedding vectors and on the characterisation of invariant properties which define its dynamics. The results suggest the existence of a low dimensional chaotic attractor with a Lyapunov dimension, DL, of around 6.6 and a predictability between 8 and 13 hours ahead. Furthermore, once the attractor has been reconstructed it is possible to make predictions by mapping local-neighbourhood to local-neighbourhood in the reconstructed phase space. To compare the prediction results with another nonlinear method, two nonlinear autoregressive models (NAR) based on multilayer feedforward neural networks have been developed. From the study, it can be observed that nonlinear forecasting produces adequate results for the 'normal' dynamic behaviour of the water level of Venice Lagoon, outperforming linear algorithms, however, both methods fail to forecast the 'high water' phenomenon more than 2-3 hours ahead.Publicad

Rampant exchange of the structure and function of extramembrane domains between membrane and water soluble proteins.

Of the membrane proteins of known structure, we found that a remarkable 67% of the water soluble domains are structurally similar to water soluble proteins of known structure. Moreover, 41% of known water soluble protein structures share a domain with an already known membrane protein structure. We also found that functional residues are frequently conserved between extramembrane domains of membrane and soluble proteins that share structural similarity. These results suggest membrane and soluble proteins readily exchange domains and their attendant functionalities. The exchanges between membrane and soluble proteins are particularly frequent in eukaryotes, indicating that this is an important mechanism for increasing functional complexity. The high level of structural overlap between the two classes of proteins provides an opportunity to employ the extensive information on soluble proteins to illuminate membrane protein structure and function, for which much less is known. To this end, we employed structure guided sequence alignment to elucidate the functions of membrane proteins in the human genome. Our results bridge the gap of fold space between membrane and water soluble proteins and provide a resource for the prediction of membrane protein function. A database of predicted structural and functional relationships for proteins in the human genome is provided at sbi.postech.ac.kr/emdmp

Gas phase constant pressure heat capacities (C~p,gas~) for the C~1~ through C~10~ straight chain alkanes, isobutane, hydrogen atom, hydroxyl and methyl radicals, and water between 298.15 and 1500 K: A comparison of theoretical values against experimental data

Gas phase constant pressure heat capacities (C~p,gas~) for the C~1~ through C~10~ straight chain alkanes, isobutane, hydrogen atom, hydroxyl and methyl radicals, and water were calculated between 298.15 and 1500 K using various low (semiempirical PM6) through high level (CBS-Q//B3 and G4 composite) theoretical methods. All levels of theory provided good agreement with experimental C~p,gas~ data (<+/-10% deviation) regardless of molecular size. A modest but progressive loss of C~p,gas~ predictive accuracy occurs with increasing molecular size among the n-alkanes. For most compounds at all levels of theory, the highest C~p,gas~ estimation accuracy occurs at elevated temperatures, with decreasing accuracy as the temperature is lowered or raised about the method specific accuracy maximum. In general, C~p,gas~ prediction accuracy appears to depend less on the level of theory applied compared to the temperature under consideration

Recent results of the research for preseismic phenomena on the underground water and temperature in Pieria, northern Greece

International audienceThe recent results of the research for earthquake precursory phenomena on the underground water level and temperature at the area Pieria of northern Greece are presented. The analysis of our observations in relation to the local microseismicity indicate that underground water level variations may be considered as precursory phenomena connected to the local microseismic activity in the area of Pieria. Base on these results, it can be supported that monitoring the shallow underground water level and temperature for detecting earthquake precursory phenomena may be proved to be a useful method in the framework of an interdisciplinary research for earthquake prediction