Generalisation for neural networks through data sampling and training procedures, with applications to streamflow predictions

International audienceSince the 1990s, neural networks have been applied to many studies in hydrology and water resources. Extensive reviews on neural network modelling have identified the major issues affecting modelling performance; one of the most important is generalisation, which refers to building models that can infer the behaviour of the system under study for conditions represented not only in the data employed for training and testing but also for those conditions not present in the data sets but inherent to the system. This work compares five generalisation approaches: stop training, Bayesian regularisation, stacking, bagging and boosting. All have been tested with neural networks in various scientific domains; stop training and stacking having been applied regularly in hydrology and water resources for some years, while Bayesian regularisation, bagging and boosting have been less common. The comparison is applied to streamflow modelling with multi-layer perceptron neural networks and the Levenberg-Marquardt algorithm as training procedure. Six catchments, with diverse hydrological behaviours, are employed as test cases to draw general conclusions and guidelines on the use of the generalisation techniques for practitioners in hydrology and water resources. All generalisation approaches provide improved performance compared with standard neural networks without generalisation. Stacking, bagging and boosting, which affect the construction of training sets, provide the best improvement from standard models, compared with stop-training and Bayesian regularisation, which regulate the training algorithm. Stacking performs better than the others although the benefit in performance is slight compared with bagging and boosting; furthermore, it is not consistent from one catchment to another. For a good combination of improvement and stability in modelling performance, the joint use of stop training or Bayesian regularisation with either bagging or boosting is recommended. Keywords: neural networks, generalisation, stacking, bagging, boosting, stop-training, Bayesian regularisation, streamflow modellin

Simplifying a hydrological ensemble prediction system with a backward greedy selection of members – Part 2: Generalization in time and space

An uncertainty cascade model applied to stream flow forecasting seeks to evaluate the different sources of uncertainty of the complex rainfall-runoff process. The current trend focuses on the combination of Meteorological Ensemble Prediction Systems (MEPS) and hydrological model(s). However, the number of members of such a HEPS may rapidly increase to a level that may not be operationally sustainable. This paper evaluates the generalization ability of a simplification scheme of a 800-member HEPS formed by the combination of 16 lumped rainfall-runoff models with the 50 perturbed members from the European Centre for Medium-range Weather Forecasts (ECMWF) EPS. Tests are made at two levels. At the local level, the transferability of the 9th day hydrological member selection for the other 8 forecast horizons exhibits an 82% success rate. The other evaluation is made at the regional or cluster level, the transferability from one catchment to another from within a cluster of watersheds also leads to a good performance (85% success rate), especially for forecast time horizons above 3 days and when the basins that formed the cluster presented themselves a good performance on an individual basis. Diversity, defined as hydrological model complementarity addressing different aspects of a forecast, was identified as the critical factor for proper selection applications

Can a multi-model approach improve hydrological ensemble forecasting? A study on 29 French catchments using 16 hydrological model structures

An operational hydrological ensemble forecasting system based on a meteorological ensemble prediction system (M-EPS) coupled with a hydrological model searches to capture the uncertainties associated with the meteorological prediction to better predict river flows. However, the structure of the hydrological model is also an important source of uncertainty that has to be taken into account. This study aims at evaluating and comparing the performance and the reliability of different types of hydrological ensemble prediction systems (H-EPS), when ensemble weather forecasts are combined with a multi-model approach. The study is based on 29 catchments in France and 16 lumped hydrological model structures, driven by the weather forecasts from the European centre for medium-range weather forecasts (ECMWF). Results show that the ensemble predictions produced by a combination of several hydrological model structures and meteorological ensembles have higher skill and reliability than ensemble predictions given either by one single hydrological model fed by weather ensemble predictions or by several hydrological models and a deterministic meteorological forecast

Non-stationary temporal characterization of the temperature profile of a soil exposed to frost in south-eastern Canada

The objective of this work was to compare time and frequency fluctuations of air and soil temperatures (2-, 5-, 10-, 20- and 50-cm below the soil surface) using the continuous wavelet transform, with a particular emphasis on the daily cycle. The analysis of wavelet power spectra and cross power spectra provided detailed non-stationary accounts with respect to frequencies (or periods) and to time of the structure of the data and also of the relationships that exist between time series. For this particular application to the temperature profile of a soil exposed to frost, both the air temperature and the 2-cm depth soil temperature time series exhibited a dominant power peak at 1-d periodicity, prominent from spring to autumn. This feature was gradually damped as it propagated deeper into the soil and was weak for the 20-cm depth. Influence of the incoming solar radiation was also revealed in the wavelet power spectra analysis by a weaker intensity of the 1-d peak. The principal divergence between air and soil temperatures, besides damping, occurred in winter from the latent heat release associated to the freezing of the soil water and the insulation effect of snowpack that cease the dependence of the soil temperature to the air temperature. Attenuation and phase-shifting of the 1-d periodicity could be quantified through scale-averaged power spectra and time-lag estimations. Air temperature variance was only partly transferred to the 2-cm soil temperature time series and much less so to the 20-cm soil depth

Global sensitivity analysis in environmental water quality modelling: Where do we stand?

Global sensitivity analysis (GSA) is a valuable tool to support the use of mathematical models for environmental systems. During the last years the water quality modelling field has embraced the use of GSA. Environmental water quality modellers have tried to transfer the knowledge and experience acquired in other disciplines. The main objective of this paper is to provide an informed problem statement of the issues surrounding GSA applications in the environmental water quality modelling field. Specifically, this paper aims at identifying, for each GSA method, the potential use, the critical issues to be solved and the limits identified in a comprehensive literature review. The paper shows that the GSA methods are not mostly applied by using the numerical settings as suggested in the literature for other application fields. However, some authors have emphasized that the modeller must take care in employing such \u201cdefault\u201d numerical settings because,for complex water quality models, different GSA methods have been shown to provide different results depending on the settings. Quantitative convergence analysis has been identified as a key element for GSA quality control that merits further investigations for GSA application in the environmental water quality modelling field

Water quality and planktonic microbial assemblages of isolated wetlands in an agricultural landscape

Author Posting. © The Author(s), 2011. This is the author's version of the work. It is posted here by permission of Springer for personal use, not for redistribution. The definitive version was published in Wetlands 31 (2011): 885-894, doi:10.1007/s13157-011-0203-6.Wetlands provide ecosystem services including flood protection, water quality enhancement, food chain support, carbon sequestration, and support regional biodiversity. Wetlands occur in human-altered landscapes, and the ongoing ability of these wetlands to provide ecosystem services is lacking. Additionally, the apparent lack of connection of some wetlands, termed geographically isolated, to permanent waters has resulted in little regulatory recognition. We examined the influence of intensive agriculture on water quality and planktonic microbial assemblages of intermittently inundated wetlands. We sampled 10 reference and 10 agriculturally altered wetlands in the Gulf Coastal Plain of Georgia. Water quality measures included pH, alkalinity, dissolved organic carbon, nutrients (nitrate, ammonium, and phosphate), and filterable solids (dry mass and ash-free dry mass). We measured abundance and relative size distribution of the planktonic microbial assemblage (< 45 μm) using flow cytometry. Water quality in agricultural wetlands was characterized by elevated nutrients, pH, and suspended solids. Autotrophic microbial cells were largely absent from both wetland types. Heterotrophic microbial abundance was influenced by nutrients and suspended matter concentration. Agriculture caused changes in microbial assemblages forming the base of wetland food webs. Yet, these wetlands potentially support important ecological services in a highly altered landscape.Funding was provided by the Joseph W. Jones Ecological Research Center.2012-07-2

Status report on emerging photovoltaics

\ua9 2023 Society of Photo-Optical Instrumentation Engineers (SPIE).This report provides a snapshot of emerging photovoltaic (PV) technologies. It consists of concise contributions from experts in a wide range of fields including silicon, thin film, III-V, perovskite, organic, and dye-sensitized PVs. Strategies for exceeding the detailed balance limit and for light managing are presented, followed by a section detailing key applications and commercialization pathways. A section on sustainability then discusses the need for minimization of the environmental footprint in PV manufacturing and recycling. The report concludes with a perspective based on broad survey questions presented to the contributing authors regarding the needs and future evolution of PV

Multidimensional Scaling Reveals the Main Evolutionary Pathways of Class A G-Protein-Coupled Receptors

Class A G-protein-coupled receptors (GPCRs) constitute the largest family of transmembrane receptors in the human genome. Understanding the mechanisms which drove the evolution of such a large family would help understand the specificity of each GPCR sub-family with applications to drug design. To gain evolutionary information on class A GPCRs, we explored their sequence space by metric multidimensional scaling analysis (MDS). Three-dimensional mapping of human sequences shows a non-uniform distribution of GPCRs, organized in clusters that lay along four privileged directions. To interpret these directions, we projected supplementary sequences from different species onto the human space used as a reference. With this technique, we can easily monitor the evolutionary drift of several GPCR sub-families from cnidarians to humans. Results support a model of radiative evolution of class A GPCRs from a central node formed by peptide receptors. The privileged directions obtained from the MDS analysis are interpretable in terms of three main evolutionary pathways related to specific sequence determinants. The first pathway was initiated by a deletion in transmembrane helix 2 (TM2) and led to three sub-families by divergent evolution. The second pathway corresponds to the differentiation of the amine receptors. The third pathway corresponds to parallel evolution of several sub-families in relation with a covarion process involving proline residues in TM2 and TM5. As exemplified with GPCRs, the MDS projection technique is an important tool to compare orthologous sequence sets and to help decipher the mutational events that drove the evolution of protein families

Hormonal signaling in cnidarians : do we understand the pathways well enough to know whether they are being disrupted?

Author Posting. © The Author, 2006. This is the author's version of the work. It is posted here by permission of Springer for personal use, not for redistribution. The definitive version was published in Ecotoxicology 16 (2007): 5-13, doi:10.1007/s10646-006-0121-1.Cnidarians occupy a key evolutionary position as basal metazoans and are ecologically important as predators, prey and structure-builders. Bioregulatory molecules (e.g., amines, peptides and steroids) have been identified in cnidarians, but cnidarian signaling pathways remain poorly characterized. Cnidarians, especially hydras, are regularly used in toxicity testing, but few studies have used cnidarians in explicit testing for signal disruption. Sublethal endpoints developed in cnidarians include budding, regeneration, gametogenesis, mucus production and larval metamorphosis. Cnidarian genomic databases, microarrays and other molecular tools are increasingly facilitating mechanistic investigation of signaling pathways and signal disruption. Elucidation of cnidarian signaling processes in a comparative context can provide insight into the evolution and diversification of metazoan bioregulation. Characterizing signaling and signal disruption in cnidarians may also provide unique opportunities for evaluating risk to valuable marine resources, such as coral reefs