An Evolutionary Model for Operation of Hydropower Reservoirs

In this study, an optimization model is developed for monthly operation of a multi-purpose hydropower reservoirs using genetic algorithm. The real value encoding approach is used considering alternative representation, selection, crossover, and mutation schemes. The constraints are handled using the Multiplicative Penalty Method (MPM) function, in order to evaluate the objective function in deferent conditions. The reliability of water allocation to different demands and hydropower generation are evaluated using an economic objective function which has been calculated based on the actual value of water and energy of Karoon-I Reservoir in southwestern part of Iran. The results of this study have shown the importance of selecting a suitable mutation operator for reducing the computational run time of the optimization model. The robustness and efficiency of genetic algorithm in developing the operation policies for a multi-purpose hydropower reservoir is discussed in the paper

Inherited deletion of 9p22.3-p24.3 and duplication of 18p11.31-p11.32 associated with neurodevelopmental delay: Phenotypic matching of involved genes.

We describe a 3.5-year-old Iranian female child and her affected 10-month-old brother with a maternally inherited derivative chromosome 9 [der(9)]. The postnatally detected rearrangement was finely characterized by aCGH analysis, which revealed a 15.056 Mb deletion of 9p22.3-p24.3p22.3 encompassing 14 OMIM morbid genes such as DOCK8, KANK1, DMRT1 and SMARCA2, and a gain of 3.309 Mb on 18p11.31-p11.32 encompassing USP14, THOC1, COLEC12, SMCHD1 and LPIN2. We aligned the genes affected by detected CNVs to clinical and functional phenotypic features using PhenogramViz. In this regard, the patient\u27s phenotype and CNVs data were entered into PhenogramViz. For the 9p deletion CNV, 53 affected genes were identified and 17 of them were matched to 24 HPO terms describing the patient\u27s phenotypes. Also, for CNV of 18p duplication, 22 affected genes were identified and six of them were matched to 13 phenotypes. Moreover, we used DECIPHER for in-depth characterization of involved genes in detected CNVs and also comparison of patient phenotypes with 9p and 18p genomic imbalances. Based on our filtration strategy, in the 9p22.3-p24.3 region, approximately 80 pathogenic/likely pathogenic/uncertain overlapping CNVs were in DECIPHER. The size of these CNVs ranged from 12.01 kb to 18.45 Mb and 52 CNVs were smaller than 1 Mb in size affecting 10 OMIM morbid genes. The 18p11.31-p11.32 region overlapped 19 CNVs in the DECIPHER database with the size ranging from 23.42 kb to 1.82 Mb. These CNVs affect eight haploinsufficient genes

Evaluating the Efficiency of Bayesian Networks in River Quality Management: Application of the Trading-Ratio System

In recent decades, river quality management has received enormous attention by researchers as an important water resources management issue. The main reason for this is saving in wastewater treatment costs by optimal allocation of the assimilative capacity of the river system to dischargers. Regarding the unidirectionality of the river flow toward the lowest level, the Trading Ratio System (TRS) and Bayesian Networks are utilized in this paper to develop new, real-time operating policies for discharge permit trading in rivers. TRS is used in a Monte Carlo Analysis to provide the required data for training and validating a Bayesian Network (BN). The trained BN are then used for real time river water quality management to provide probability distribution functions of treatment levels and trading discharge permit policies. The methodology is successfully applied to a case study and its results are compared with those of the TRS. The comparisons show the usefulness of the methodology as a cost-effective and probabilistic decision-making tool in real-time river water quality management

Probabilistic Contaminant Source Identification in Water Distribution Infrastructure Systems

Large water distribution systems can be highly vulnerable to penetration of contaminant factors caused by different means including deliberate contamination injections. As contaminants quickly spread into a water distribution network, rapid characterization of the pollution source has a high measure of importance for early warning assessment and disaster management. In this paper, a methodology based on Probabilistic Support Vector Machines (PSVMs) is proposed for identifying the contamination source location in drinking water distribution systems. To obtain the required data for training the PSVMs, several computer simulations have been performed over multiple combinations of possible contamination source locations and initial mass injections for a conservative solute. Then the trained probabilistic SVMs have been effectively utilized to identify the upstream zones that are more likely to have the positive detection results. In addition, the results of this method were compared and contrasted with Bayesian Networks (BNs) and Probabilistic Neural Networks (PNNs). The efficiency and versatility of the proposed methodology were examined using the available data and information from water distribution network of the City of Arak in the western part of Iran

Optimal Groundwater Monitoring Network Design Using the Entropy Theory

This paper presents a methodology for optimal design of groundwater monitoring networks using the criteria of maximizing information and minimizing monitoring cost. The measure of Transinformation in the Discrete Entropy Theory is used for quantifying the efficiency of the monitoring network. The existing uncertainty in the Transinformation-Distance (T-D) curve is incorporated using the fuzzy set theory. The fuzzy T-D curve is then used in a multi-objective GA-based optimization model, which provides the best locations for monitoring stations. The proposed methodology is applied to groundwater resources in the southern part of Tehran, Iran. The results show the applicability and the efficiency of the model for the optimal design of groundwater monitoring systems

A fuzzy KNN-based model for significant wave height prediction in large lakes

Summary: Some algorithms based on fuzzy set theory (FST) such as fuzzy inference system (FIS) and adaptive-network-based fuzzy inference system (ANFIS) have been successfully applied to significant wave height (SWH) prediction. In this paper, perhaps for the first time, the fuzzy K-nearest neighbor (FKNN) algorithm is utilized to develop a fuzzy wave height prediction model for large lakes, where the fetch length depends on the wind direction. As fetch length (or wind direction) can affect the wave height in lakes, this variable is also considered as one of the inputs of the prediction model.The results of the FKNN model are compared with those of some soft computing techniques such as Bayesian networks (BNs), regression tree induction (named M5P), and support vector regression (SVR). The developed FKNN model is used for SWH prediction in the western part of Lake Superior in North America. The results show that the FKNN and M5P model can outperform the other soft computing techniques. Keywords: Significant wave height prediction, Fuzzy K-nearest neighbor, Bayesian networks, Support vector regression, Regression tree inductio

Evaluating energy harvesting from water distribution networks using combined stakeholder and social network analysis

The chance of success in implementing a new project in a water distribution network (WDN) significantly depends on the behaviour of the involved stakeholders. In this paper, the feasibility of installing the micro-turbines in WDNs for generating hydro-power energy is studied from the stakeholders' perspective. Stakeholders’ analysis (SA) and social network analysis (SNA) are performed to methodically recognise the environment and the relationships among stakeholders. 18 stakeholders at the local, regional, and national levels were identified and their representatives were interviewed. In SA, the power, interest, access to information, and satisfaction from the current condition of the system were evaluated for each stakeholder. The Water and Wastewater Company and Ministry of Energy were found as the stakeholders with the highest power and interest. Unexpectedly, the Regional Electricity Company was discovered with medium power and low access to information. In SNA, cooperative and non-cooperative relationships between stakeholders were analysed and an assessment was made for the role of each stakeholder in their social network, through four centrality metrics. The correlation between SA and SNA results suggests that SA factors could be estimated using the SNA metrics

A Heuristic Evolutionary Game Theoretic Methodology For Conjunctive Use Of Surface And Groundwater Resources

In this paper, a methodology based on a new heuristic evolutionary game is developed to determine evolutionary stable equilibrium (ESE) strategies for conjunctive surface and groundwater allocation to water users with conflicting objectives. The methodology provides reasonable and realistic framework to illuminate non-cooperative behaviors of water users in the joint usage of surface and groundwater resources. The developed heuristic evolutionary game theoretic approach can be used for finding equilibrium in asymmetric n-person games with continuous strategies. The penalty function is provided in a way that it can control groundwater table drawdown at monitoring points. It is also shown that applying the proposed penalty function may inhibit the water users’ excessive exploitation and can consequently avoid the tragedy of commons. As the methodology needs to run water allocation optimization and groundwater simulation models for several times, an optimization model based on genetic algorithms is linked with MODFLOW groundwater simulation model. Furthermore, a computational cost reduction method has been used to reduce the computation time caused by several consecutive computational steps in the proposed methodology. A pistachio loss function due to the deficit irrigation is also developed and used for evaluating water users’ objective functions. To illustrate the practical utility of the methodology, it is applied to the Rafsanjan plain in Iran and it is shown that this approach can be used for developing surface and groundwater allocation policies

Comparison of three group decision-making frameworks for evaluating resilience time series of water resources systems under uncertainty

This study compared three uncertainty-based decision-making frameworks (considering/not considering the hierarchical structure of stakeholders) using resilience-based indices for evaluating different water resources management (WRM) scenarios under the impacts of climate change. The first step involved identifying significant stakeholders in the study area and establishing their relative weights. In the next step, stakeholders were asked to evaluate the management scenarios in the three different decision-making frameworks based on their decision criteria (nine resilience-based indices, implementation cost, and employment). Different types of weights (explicit and interval) were assigned to each stakeholder and their decision criteria, to account for the uncertainty associated with estimating their respective weights. This methodology was applied to the case of the Zarrinehrud River basin in northwest Iran. The best management scenario identified (MSC1346) was able increase lake elevation by 2.6 m (from 1271.3 m to 1273.9 m), improve the resilience of the system by 25 %, and enhance provisioning ecosystem services such as water and food supply and regulating services such as air quality. Comparing the results of the three decision-making frameworks revealed that the two which considered the hierarchical structure of stakeholders were more effective in determining the best scenario. The best scenario selected in the framework that ignored the hierarchical structure of stakeholders (MSC13567) had USD 202 million higher overall implementation and construction costs and gave a negligible difference in resilience value (0.04 difference) compared with scenario MSC1346

Application of multi-agent decision-making methods in hydrological ecosystem services management

In this paper, a methodology is presented for managing hydrological ecosystem services by taking into account the hierarchy of stakeholders involved in the decision-making process. With this in mind, a water allocation model is first used for allocating water resources to demands. Then, several ecosystem services (ESs)-based criteria are defined to evaluate hydrological ESs of water resources management policies. A set of water and environmental resources management strategies (alternatives) are defined for decision-makers, and several drought management strategies are determined to decrease the area of key crops and water demands of agricultural nodes. To model a multi-agent multi-criteria decision-making problem for managing hydrological ESs, three main steps are considered as follows: • Different ES-based criteria (i.e., economic profit, NPP,11 Net Primary Productivity and ecological index) are defined, and their grade-based values are estimated. • Several strategies are defined for stakeholders at different levels. • A recursive evidential reasoning (ER) approach, which considers a hierarchical structure for decision-makers and a leader-follower game, is used to select the best strategy for each decision-maker.The applicability and efficiency of the methodology are illustrated by applying it to a real-world case study. The methodology is general and can be easily applied to other study areas