A study of cognitive strategies for an autonomous search

Cognitive search is a collective term for search strategies based on information theoretic rewards required in sequential decision making under uncertainty. The paper presents a comparative study of cognitive search strategies for finding an emitting source of unknown strength using sparse sensing cues in the form of occasional non-zero sensor measurements. The study is cast in the context of an emitting source of particles transported by turbulent flow. The search algorithm, which sequentially estimates the source parameters and the reward function for motion control, has been implemented using the sequential Monte Carlo method. The distribution of the search time has been explained by the inverse Gaussian distribution.     

Efficient Monte Carlo simulation method of GERT-type network for project management

This paper proposes a new method to estimate network characteristics such as project time, project cost, etc. There are many kinds of projects such as new product development and plant construction. Since these projects consist of various uncertain jobs, we can model those processes as stochastic networks such as GERT network. Only simulation technique can analyze the networks’ characteristics of practical project model. However, the method is extremely time-consuming. Our method can estimate the network characteristics rapidly and accurately by analyzing one set of Monte Carlo simulation results based on the theory of probability.     

A model integration framework for linking SWAT and MODFLOW

Assessment of long-term anthropogenic impacts on agro-ecosystems requires comprehensive modelling capabilities to simulate water interactions between the surface and groundwater domains. To address this need, a modelling framework, called “SWATmf”, was developed to link and integrate the Soil Water Assessment Tool (SWAT), a widely used surface watershed model with the MODFLOW, a groundwater model. The SWATmf is designed to serve as a project manager, builder, and model performance evaluator, and to facilitate dynamic interactions between surface and groundwater domains at the watershed scale, thus providing a platform for simulating surface and groundwater interactions. Using datasets from the Fort Cobb Reservoir experimental watershed (located in Oklahoma, USA), the SWATmf to facilitate linkage and dynamic simulation of SWAT and MODFLOW models. Simulated streamflow and groundwater levels generally agreed with observations trends showing that the SWATmf can be used for simulating surface and groundwater interactions.     

Managing water in complex systems: An integrated water resources model for Saskatchewan,Canada

Using a system dynamics approach, an integrated water resources system model is developed for scenario analysis of the Saskatchewan portion of the transboundary Saskatchewan River Basin in western Canada. The water resources component is constructed by emulating an existing Water Resources Management Model. Enhancements include an irrigation sub-model to estimate dynamic irrigation demand, including alternative potential evapotranspiration estimates, and an economic sub-model to estimate the value of water use for various sectors of the economy. Results reveal that the water resources system in Saskatchewan becomes increasingly sensitive to the selection of evapotranspiration algorithm as the irrigation area increases, due to competition between hydropower and agriculture. Preliminary results suggest that irrigation expansion would decrease hydropower production, but might increase the total direct economic benefits to Saskatchewan. However, indirect costs include reduction in lake levels and river flows.     

A direct decoupling approach for efficient reliability-based design optimization

This paper develops an efficient methodology to perform reliability-based design optimization (RBDO) by decoupling the optimization and reliability analysis iterations that are nested in traditional formulations. This is achieved by approximating the reliability constraints based on the reliability analysis results. The proposed approach does not use inverse first-order reliability analysis as other existing decoupled approaches, but uses direct reliability analysis. This strategy allows a modular approach and the use of more accurate methods, including Monte-Carlo-simulation (MCS)-based methods for highly nonlinear reliability constraints where first-order reliability approximation may not be accurate. The use of simulation-based methods also enables system-level reliability estimates to be included in the RBDO formulation. The efficiency of the proposed RBDO approach is further improved by identifying the potentially active reliability constraints at the beginning of each reliability analysis. A vehicle side impact problem is used to examine the proposed method, and the results show the usefulness of the proposed method.     

A Bayesian method for multi-pollution source water quality model and seasonal water quality management in river segments

Excessive pollutant discharge from multi-pollution resources can lead to a rise in downriver contaminant concentration in river segments. A multi-pollution source water quality model (MPSWQM) was integrated with Bayesian statistics to develop a robust method for supporting load (I) reduction and effective water quality management in the Harbin City Reach of the Songhua River system in northeastern China. The monthly water quality data observed during the period 2005–2010 was analyzed and compared, using ammonia as the study variable. The decay rate (k) was considered a key factor in the MPSWQM, and the distribution curve of k was estimated for the whole year. The distribution curves indicated small differences between the marginal distribution of k of each period and that water quality management strategies can be designed seasonally. From the curves, decision makers could pick up key posterior values of k in each month to attain the water quality goal at any specified time. Such flexibility is an effective way to improve the robustness of water quality management. For understanding the potential collinearity of k and I, a sensitivity test of k for I_2i (loadings in segment 2 of the study river) was done under certain water quality goals. It indicated that the posterior distributions of I_2i show seasonal variation and are sensitive to the marginal posteriors of k. Thus, the seasonal posteriors of k were selected according to the marginal distributions and used to estimate I_2i in next water quality management. All kinds of pollutant sources, including polluted branches, point and non-point source, can be identified for multiple scenarios. The analysis enables decision makers to assess the influence of each loading and how best to manage water quality targets in each period. Decision makers can also visualize potential load reductions under different water quality goals. The results show that the proposed method is robust for management of multi-pollutant loadings under different water quality goals to help ensure that the water quality of river segments meets targeted goals.     

Towards best practice implementation and application of models for analysis of water resources management scenarios

Water resources management models are widely used to evaluate planning or operational scenarios to support water resource management decision-making. However, the approaches to modelling used in the past have led to problems, such as modellers having difficulty establishing the credibility of their model with stakeholders, and stakeholders having difficulty understanding and trusting model results. A best practice approach to the implementation and application of water resources management models based on a quality assurance procedure is an appropriate means of overcoming these difficulties, and there are a number of guidelines and papers available promoting this approach. However, guidance in these on the use of models to analyse water resource planning scenarios is limited or not provided. This paper therefore provides guidance on the implementation and application of water resources management models with an emphasis on scenario analysis. This guidance is principally intended for practising modellers, and also for peer reviewers and stakeholders such as managers, decision makers, and community-based groups. Adoption strategies and recommendations for future directions are also discussed.