Oliva, L.; Gómez-Sebastià, I.; Verdaguer, M.; Sànchez-Marrè, M.; Poch, M.; Cortes, U. Environmental modelling & software Vol. 89, p. 106-119 DOI: 10.1016/j.envsoft.2016.11.009 Data de publicació: 2017-03-01 Article en revista
This paper characterizes part of an interdisciplinary research effort on Artificial Intelligence (AI) techniques and tools applied to Environmental Decision-Support Systems (EDSS). WaWO+ the ontology we present here, provides a set of concepts that are queried, advertised and used to support reasoning about and the management of urban water resources in complex scenarios as a River Basin. The goal of this research is to increase efficiency in Data and Knowledge interoperability and data integration among heterogeneous environmental data sources (e.g., software agents) using an explicit, machine understandable ontology to facilitate urban water resources management within a River Basin.
This manuscript describes the MatSWMM toolbox, an open-source Matlab, Python, and LabVIEW-based software package for the analysis and design of real-time control (RTC) strategies in urban drainage systems (UDS). MatSWMM includes control-oriented models of UDS, and the storm water management model (SWMM) of the US Environmental Protection Agency (EPA), as well as systematic-system edition functionalities. Furthermore, MatSWMM is also provided with a population-dynamics-based controller for UDS with three of the fundamental dynamics, i.e., the Smith, projection, and replicator dynamics. The simulation algorithm, and a detailed description of the features of MatSWMM are presented in this manuscript in order to illustrate the capabilities that the tool has for educational and research purposes.
Soil organic matter dynamics are essential for terrestrial ecosystem functions as they affect biogeochemical cycles and, thus, the provision of plant nutrients or the release of greenhouse gases to the atmosphere. Most of the involved processes are driven by microorganisms. To investigate and understand these processes, individual-based models allow analyzing complex microbial systems' behavior based on rules and conditions for individual entities within these systems, taking into account local interactions and individual variations.
Meseguer, J.; Mirats, J.; Cembrano, M.; Puig, V.; Quevedo, J.; Perez, R.; Sanz, G.; Ibarra, D. Environmental modelling & software Vol. 60, p. 331-345 DOI: 10.1016/j.envsoft.2014.06.025 Data de publicació: 2014-10-01 Article en revista
This paper describes a model-driven decision-support system (software tool) implementing a model-based methodology for on-line leakage detection and localization which is useful for a large class of water distribution networks. Since these methods present a certain degree of complexity which limits their use to experts, the proposed software tool focuses on the integration of a method emphasizing its use by water network managers as a decision support system. The proposed software tool integrates a model-based leakage localization methodology based on the use of on-line telemetry information, as well as a water network calibrated hydraulic model. The application of the resulting decision support software tool in a district metered area (DMA) of the Barcelona distribution network is provided and discussed. The obtained results show that the leakage detection and localization may be performed efficiently reducing the required time.
Irrigation canals are open-flow water hydraulic systems, whose objective is mainly to convey water from its source down to its final users. They are large distributed systems characterized by non-linearity and
dynamic behavior that depends on the operating point. Moreover, in canals with multiple reaches dynamic behavior is highly affected by the coupling among them. The physical model for those systems leads to a distributed-parameter model whose description usually requires partial differential equations (PDEs). However, the solution and parameter estimation of those PDE equations can only be obtained numerically and imply quite time-consuming computations that make them not suitable for real-time control purposes. Alternatively, in this paper, it will be shown that open-flow canal systems can be suitably represented for control purposes by using linear parameter-varying (LPV) models. The advantage of this approach compared to the use of PDE equation is that allows simpler models which are suitable for control design and whose parameters can be easily identified from inputeoutput data by means of classical identification techniques. In this paper, the well-known control-oriented, model named integral delay zero (IDZ), that is able to represent the canal dynamics around a given operating point by means of a linear time-invariant (LTI) model is extended to multiple operating points by means of an LPV model. The derivation of this LPV model for single-reach open-flow canal systems as well as its extension to multiple-reach open-flow canals is proposed. In particular, the proposed methodology allows deriving the model structure and estimating model parameters using data by means of identification techniques. Thus, a gray-box control model is obtained whose validation is carried out using single-pool and two-pool test canals obtaining satisfactory results.
The use of knowledge-based systems has been shown to be a suitable approach to support decision making in environmental systems. Capturing and managing the huge quantity of data/information that has to be considered is an intrinsic factor that makes environmental systems a sophisticated domain.
Organizing this data in a naive way can impact the efficacy of any knowledge-based system. Another intrinsic factor is the variety of data sources, which can result in inconsistent, uncertain or incomplete knowledge bases when different data sources are considered. Accordingly, two central issues of a successful knowledge-based system are the organization of its knowledge base and the expressiveness of its specification language. In this paper, we introduce a stratified framework for structuring any environmental knowledge base.
We will argue that a declarative specification language, such as Answer Set Programming, is expressive enough to capture environmental knowledge bases that are inconsistent, uncertain and incomplete. We
also present an automata-based approach to manage actions in knowledge-based systems. By solving a use case, specifically the diagnosis of the safety of a particular industrial wastewater discharge in an
urban wastewater system, we illustrate how to represent relevant abstractions to model related complex processes. We show that by using them it is also possible to automate the diagnosis process (in the present case, for example, to diagnose problems at a wastewater treatment plant and afterward in the river) and hence support the decision-making task.
This work presents advances in the design of a hybrid methodology that combines artificial intelligence and statistical tools to induce a model of explicit knowledge in relation to the dynamics of a wastewater treatment plant. The methodology contributes to problem solving under the paradigm of knowledge discovery from data in which the pre-process, the automatic interpretation of results and the explicit production of knowledge play a role as important as the analysis itself. The data mining step is performed using clustering based on rules by states, which integrates the knowledge discovered separately at each step of the process into a single model of global operation of the phenomenon. This provides a more accurate model for the dynamics of the system than one obtained by analyzing the whole dataset with all the steps taken together.
Initial (IC) and boundary conditions (BC) are required in order to solve the set of stiff differential equations included in air quality models. In this work, the influences of ICeBC are analyzed in the northeastern Iberian Peninsula (NEIP) by applying MM5-EMICAT2000-CMAQ. A multiscale-nested configuration has been used to generate the ICeBC. The wider domain (D1) covers an area of 1392 X 1104 km2 centered in the Iberian Peninsula. Domain 2 (D2) covers an area of 272 X 272 km2 in the NEIP (D2) with high spatial and temporal resolution. The
information related to BC has been supplied to D2 through one-way nesting. Different scenarios were considered (base case, increments of +50% in ozone (O3) IC, +50% in O3 BC, +50% in O3 precursors IC, +50% in O3 precursors BC and clean BC). The impacts of the IC on a site decrease with simulation time. Focusing on the conditions within the PBL, a 48-h spin-up time is sufficient to reduce the impact factor of IC to 10% or less for O3 since the influence of pervasive local emissions. The influences of BC are more important for areas near domain
boundaries, especially in areas where the contribution of O3 precursors is due to a short-medium range transport.
Biosphere 2 is a closed ecosystem located near Tucson, Arizona, designed for studying the interactions between different biological species among each other and with their materially closed controlled environment, taking into account the limited resources that such an environment provides. Energy considerations play a central role in how these interactions play out. To this end, bond graph models were designed that enable the researcher to better understand the nature of these interactions, hopefully offering some insight into the much larger ecosystem of planet Earth.
In this work the GESCONDA software is presented. It is a tool for intelligent data analysis and implicit knowledge management of databases, with special focus on environmental databases. Differing from existing commercial systems, the more relevant aspects of this proposal are the incorporation of the statistical data filtering and pre-processing in the same software tool together with the intelligent data analysis techniques as well as the interaction of different data mining methods. Either statistical techniques or artificial intelligence techniques or even mixed techniques are combined and used to extract the knowledge contained within data.