Representing local dynamics within water resource systems through a data-driven emulation approach

Growing population and socio-economic activities along with looming effects of climate change have led to enormous pressures on water resource systems. To diagnose and quantify potential vulnerabilities, effective tools are required to represent the interactions between limited water availability and competing water demands across a range of spatial and temporal scales. Despite significant progresses in integrated modeling of water resource systems, the majority of existing models are still unable to fully describe the contemplating dynamics within and between elements of water resource systems across all relevant scales and/or variables. Here, a data-driven approach is suggested to represent local details of a water resource system through emulating an existing water resource system model, in which these details have been missed. This is through advising a set of interconnected functional mappings, i.e. integrated emulators, parameterized using the simulation results of the existing model at a common scale and/or variable but can support process representation with finer resolution and/or details. The proposed approach is applied to a complex water resource system in Southern Alberta, Canada, to provide a detailed understanding of the system’s dynamics at the Oldman Reservoir, which is the key to provision of effective water resource management in this semi-arid and already stressed cold region. By proposing a rigorous setup/falsification procedure, a set of alternative hypotheses for emulators describing the local dynamics of local irrigation demand and withdrawals along with reservoir release and evaporation is developed. Findings show that emulators formed using Artificial Neural Networks mainly outperform simpler emulators developed for the variables considered. The non-falsified emulators are then coupled to represent the local dynamics of the water resource system at the reservoir location, considering the underlying interplays with hydro-climatological conditions and human decision on the irrigation area. It is found that emulators with input variables identified through expert knowledge can outperform fully data-driven emulators in which proxies were selected based on an input variable selection method. The top non-falsified coupled models are able to capture the dynamic of lake evaporation, water withdrawal, irrigation demand, reservoir release and storage with coefficient of determination of 0.80 to 0.82, 0.45 to 0.55, 0.52 to 0.59, 0.98 to 0.99 and 0.72 to 0.88, respectively. The practical utility of the proposed approach is demonstrated through an impact assessment study by analysing four performance criteria, corresponding to reservoir’s storage, local irrigation demand, number of spill events and median reservoir release, in three stress-tests. These stress tests asses the local sensitivity of water resource system at the Oldman reservoir at three different levels, corresponding to (1) changing incoming streamflow to the basin in a bottom-up approach; (2) joint scenario of changing streamflow and warming climate, using a coupled bottom-up/top-down approach; and (3) specific changes in incoming streamflow, climate and irrigation area in a heuristic approach. For the first experimentation, weekly realizations for possible water availability are stochastically reconstructed and fed into the top non-falsified integrated emulator. By defining warm/dry, historical and cold/wet flow conditions, we found through alteration from dry to wet regime condition, the expected number of low storage duration is not changed, and expected annual water deficit is declined. Moreover, the expected number of spill events increases whereas median reservoir release increases. In the next impact assessment study, different scenarios of warming climate obtained from NASA-NEX downscaled global climate projections and the joint impact of changing streamflow and temperature on the system’s behaviour is evaluated. This assessment demonstrated that in warmer climate, the expected number of low storage duration in dry condition increases whereas in historical and wet conditions, the low storage duration does not change. In addition, the expected annual water deficit increases while the expected number of spill events decreases in the three flow regime conditions. Moreover, the expected median reservoir release increases in the dry, historical and wet regime conditions. In the final level of assessment, vulnerability of the system under changing streamflow, climate including temperature and precipitation and changing irrigation area is assessed. Results show that increasing irrigation area combined with declining inflow can considerably increase the duration of low reservoir storage in the Oldman Reservoir. Increasing temperature can lead to decline in both reservoir storage and outflow. In addition, when combined with declining inflow, increasing temperature can severely increase the annual water deficit for irrigation sector. Furthermore, it is noted that although the performance of unfalsified models are identical in representing the dynamics of the Oldman Reservoir under the historical data, but assessment can be slightly to moderately different depending on the defined scenarios of change. This is due to the choice of model configuration and can address the uncertainty regarding the system’s behaviour. Our study shows the promise of data-driven emulation approach as a tool for developing more enhanced water resource system models to face emerging management problems in the era of change

A Distributed and Privacy-Aware Speed Advisory System for Optimising Conventional and Electric Vehicles Networks

One of the key ideas to make Intelligent Transportation Systems (ITS) work effectively is to deploy advanced communication and cooperative control technologies among the vehicles and road infrastructures. In this spirit, we propose a consensus-based distributed speed advisory system that optimally determines a recommended common speed for a given area in order that the group emissions, or group battery consumptions, are minimised. Our algorithms achieve this in a privacy-aware manner; namely, individual vehicles do not reveal in-vehicle information to other vehicles or to infrastructure. A mobility simulator is used to illustrate the efficacy of the algorithm, and hardware-in-the-loop tests involving a real vehicle are given to illustrate user acceptability and ease of the deployment.Comment: This is a journal paper based on the conference paper "Highway speed limits, optimised consensus, and intelligent speed advisory systems" presented at the 3rd International Conference on Connected Vehicles and Expo (ICCVE 2014) in November 2014. This is the revised version of the paper recently submitted to the IEEE Transactions on Intelligent Transportation Systems for publicatio

A Disaggregation‐Emulation Approach for Optimization of Large Urban Drainage Systems

none3Multi-objective optimization can help identify efficient and appealing designs of urban drainage systems. However, their application to large-scale problems is hindered by the computational cost of urban drainage simulation. We propose a novel disaggregation approach that allows simulating a portion of a drainage network while the remaining part is represented by a surrogate model that maps changes in the region of interest to hydraulic head time-series at synthetic nodes shared with the remaining part of the network. The proposed approach is demonstrated with an application to the many-objective optimization of sustainable urban drainage systems in two urban areas. The design problem's decision variables include the types of sustainable drainage systems, their combination within a subcatchment, their surface areas and spatial distribution, whereas the objectives include the minimization of capital cost, flood volume, flood duration, and total suspended solids or average peak runoff. The results show that the proposed disaggregation-emulation approach can provide an accurate representation of the system dynamics while significantly reducing the computational time compared to a model that simulates the whole network dynamics. Two alternative surrogate models are considered based on multilayer perceptron (MLP) and generalized regression neural networks (GRNN). MLP is found to be more accurate compared to GRNN at the cost of a larger computational time for the training process.noneSeyedashraf O.; Bottacin-Busolin Andrea; Harou J.J.Seyedashraf, O.; Bottacin-Busolin, Andrea; Harou, J. J

W-NINE: a two-stage emulation platform for mobile and wireless systems

More and more applications and protocols are now running on wireless networks. Testing the implementation of such applications and protocols is a real challenge as the position of the mobile terminals and environmental effects strongly affect the overall performance. Network emulation is often perceived as a good trade-off between experiments on operational wireless networks and discrete-event simulations on Opnet or ns-2. However, ensuring repeatability and realism in network emulation while taking into account mobility in a wireless environment is very difficult. This paper proposes a network emulation platform, called W-NINE, based on off-line computations preceding online pattern-based traffic shaping. The underlying concepts of repeatability, dynamicity, accuracy and realism are defined in the emulation context. Two different simple case studies illustrate the validity of our approach with respect to these concepts

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

Copyright © 2014 Elsevier. NOTICE: this is the author’s version of a work that was accepted for publication in Environmental Modelling and Software. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Environmental Modelling and Software Vol. 62 (2014), DOI: 10.1016/j.envsoft.2014.09.013The development and application of evolutionary algorithms (EAs) and other metaheuristics for the optimisation of water resources systems has been an active research field for over two decades. Research to date has emphasized algorithmic improvements and individual applications in specific areas (e.g. model calibration, water distribution systems, groundwater management, river-basin planning and management, etc.). However, there has been limited synthesis between shared problem traits, common EA challenges, and needed advances across major applications. This paper clarifies the current status and future research directions for better solving key water resources problems using EAs. Advances in understanding fitness landscape properties and their effects on algorithm performance are critical. Future EA-based applications to real-world problems require a fundamental shift of focus towards improving problem formulations, understanding general theoretic frameworks for problem decompositions, major advances in EA computational efficiency, and most importantly aiding real decision-making in complex, uncertain application contexts