Miguel A. Mariño

HONEY-BEE MATING OPTIMIZATION (HBMO) ALGORITHM FOR OPTIMAL RESERVOIR OPERATION

In recent years, evolutionary and meta-heuristic algorithms have been extensively used as search and optimization tools in various problem domains, including science, commerce, and engineering. Ease of use, broad applicability, and global perspective may be considered as the primary reason for their success. The honey-bee mating process has been considered as a typical swarm-based approach to optimization, in which the search algorithm is inspired by the process of real honey-bee mating. In this paper, the honey-bee mating optimization (HBMO) algorithm is presented and tested with a nonlinear, continuous constrained problem with continuous decision and state variables to demonstrate the efficiency of the algorithm in handling the single reservoir operation optimization problems. It is shown that the performance of the model is quite comparable with the results of the well-developed traditional linear programming (LP) solvers such as LINGO 8.0. Results obtained are quite promising and compare well with the final results of the other approach.

Climate Change Impact on Reservoir Performance Indexes in Agricultural Water Supply

Abstract This paper addresses the impact of climate change on the volume of inflow to a reservoir and the volume of downstream water demand by considering three climate change scenarios in an East Azerbaijan river basin. The HadCM3 model was used to estimate possible scenarios of temperature and rainfall for the period 2026–2039 under an emission scenario (A2). A hydrological model (IHACRES) was first calibrated for the basin; and then, a monthly time series of future temperatures and rainfall were entered into IHACRES. In addition, a 14-year time series of monthly runoff was simulated for 2026–2039. Modeling results indicated that the average long-term annual runoff volume decreased by 0.7% relative to the base period (1987–2000). However, by assuming a nonchanging cultivation area, the average long-term annual water demand volume for crops increased by 16%. Both simulation and optimization models of reservoir operation were used. The simulation of reservoir performance in the delivery of water demand wa...

Multi-reservoir Operation Rules: Multi-swarm PSO-based Optimization Approach

Reservoir operation rules are intended to help an operator so that water releases and storage capacities are in the best interests of the system objectives. In multi-reservoir systems, a large number of feasible operation policies may exist. System engineering and optimization techniques can assist in identifying the most desirable of those feasible operation policies. This paper presents and tests a set of operation rules for a multi-reservoir system, employing a multi-swarm version of particle swarm optimization (MSPSO) in connection with the well-known HEC-ResPRM simulation model in a parameterization–simulation–optimization (parameterization SO) approach. To improve the performance of the standard particle swarm optimization algorithm, this paper incorporates a new strategic mechanism called multi-swarm into the algorithm. Parameters of the rule are estimated by employing a parameterization–simulation–optimization approach, in which a full-scale simulation model evaluates the objective function value for each trial set of parameter values proposed with an efficient version of the particle swarm optimization algorithm. The usefulness of the MSPSO in developing reservoir operation policies is examined by using the existing three-reservoir system of Mica, Libby, and Grand Coulee as part of the Columbia River Basin development. Results of the rule-based reservoir operation are compared with those of HEC-ResPRM. It is shown that the real-time operation of the three reservoir system with the proposed approach may significantly outperform the common implicit stochastic optimization approach.

Prediction of optimal safe ground water yield and land subsidence in the Los Banos-Kettleman City area, California, using a calibrated numerical simulation model

Land subsidence caused by the excessive use of ground water resources has traditionally caused serious and costly damage to the Los Banos-Kettleman City area of Californias San Joaquin Valley. Although the arrival of surface water from the Central Valley Project has reduced subsidence in recent decades, the growing instability of surface water supplies has refocused attention on the future of land subsidence in the region. This paper uses integrated numerical ground water and land subsidence models to simulate land subsidence caused by ground water overdraft. The simulation model is calibrated using observed data from 1972 to 1998, and the responsiveness of the model to variations in subsidence parameters are analyzed through a sensitivity analysis. A probable future drought scenario is used to evaluate the effect on land subsidence of three management alternatives over the next thirty years. The model reveals that maintaining present practices virtually eliminates unrecoverable land subsidence, but may not be a sustainable alternative because of a growing urban population to the south and concern over the ecological implications of water exportation from the north. The two other proposed management alternatives reduce the dependency on surface water by increasing ground water withdrawal. Land subsidence is confined to tolerable levels in the more moderate of these proposals, while the more aggressive produces significant long-term subsidence. Finally, an optimization model is formulated to determine maximum ground water withdrawal from nine pumping sub-basins without causing irrecoverable subsidence during the forecast period. The optimization model reveals that withdrawal can be increased in certain areas on the eastern side of the study area without causing significant inelastic subsidence.

Evaluation of Real-Time Operation Rules in Reservoir Systems Operation

Reservoir operation rules are logical or mathematical equations that take into account system variables to calculate water release from a reservoir based on inflow and storage volume values. In fact, previous experiences of the system are used to balance reservoir system parameters in each operational period. Commonly, reservoir operation rules have been considered to be linear decision rules (LDRs) and constant coefficients developed by using various optimization procedures. This paper addresses the application of real-time operation rules on a reservoir system whose purpose is to supply total downstream demand. Those rules include standard operation policy (SOP), stochastic dynamic programming (SDP), LDR, and nonlinear decision rule (NLDR) with various orders of inflow and reservoir storage volume. Also, a multi-attribute decision method, elimination and choice expressing reality (ELECTRE)-I, with a combination of indices, objective functions, and reservoir performance criteria (reliability, resiliency, and vulnerability) are used to rank the aforementioned rules. The ranking method employs two combinations of indices: (1) performance criteria and (2) objective function and performance criteria by using the same weights for all criteria. Results show that the NLDR gives an appropriate rule for real-time operation. Moreover, NLDR validation is presented by testing predefined curves for dry, normal, and wet years.

Cokriging of aquifer transmissivities from field measurements of transmissivity and specific capacity

This paper presents a new application of the cokriging technique for constructing maps of aquifer transmissivity from field measurements of transmissivity and specific capacity. The technique is illustrated using data from Yolo Basin, California. Cokriging is well-suited for estimating undersampled variables. To improve the accuracy of the estimation, cokriging considers the spatial auto-correlation of the variable to be estimated and the spatial cross-correlation between the variable to be estimated and other, better-sampled variables. Consequently, in regions that lack data of the variable to be estimated, accurate estimation can still be made on the basis of auto- and cross-correlation. In addition, estimation variances can be obtained with a little additional computation effort.

Finding the shortest path with honey-bee mating optimization algorithm in project management problems with constrained/unconstrained resources

Effective project management requires the development of a realistic plan and a clear communication of the plan from the beginning to the end of the project. The critical path method (CPM) of scheduling is the fundamental tool used to develop and interconnect project plans. Ensuring the integrity and transparency of those schedules is paramount for project success. The complex and discrete nature of the solution domain for such problems causes failing of traditional and gradient-based methods in finding the optimal or even feasible solution in some cases. The difficulties encountered in scheduling construction projects with resource constraints are highlighted by means of a simplified bridge construction problem and a basic masonry construction problem. The honey-bee mating optimization (HBMO) algorithm has been previously adopted to solve mathematical and engineering problems and has proven to be efficient for searching optimal solutions in large-problem domains. This paper presents the HBMO algorithm for scheduling projects with both constrained and unconstrained resources. Results show that the HBMO algorithm is applicable to projects with or without resource constraints. Furthermore, results obtained are promising and compare well with those of well-known heuristic approaches and gradient-based methods.

Kriging of water levels in the Souss aquifer, Morocco

Universal kriging is applied to water table data from the Souss aquifer in central Morocco. The procedure accounts for the spatial variability of the phenomenon to be mapped. With the use of measured elevations of the water table, an experimental variogram is constructed that characterizes the spatial variability of the measured water levels. Spherical and Gaussian variogram models are alternatively used to fit the experimental variogram. The models are used to develop contour maps of water table elevations and corresponding estimation variances. The estimation variances express the reliability of the kriged water table elevation maps. Universal kriging also provides a contour map of the expected elevation of the water table (drift). The differences between the expected and measured water table elevations are called residuals from the drift. Residuals from the drift are compared with residuals obtained by more traditional least-squares analysis.

Risk Analysis of Water Demand for Agricultural Crops under Climate Change

AbstractThis paper assesses the risk of increase in water demand for a wide range of irrigated crops in an irrigation network located downstream of the Aidoghmoush Dam in East Azerbaijan by considering climate change conditions for the period 2026–2039. Atmosphere-ocean global circulation models (AOGCMs) are used to simulate climatic variables such as temperature and precipitation. The Bayesian approach is used to consider uncertainties of AOGCMs. Climate change scenarios of climatic variables are first weighted by using the mean observed temperature-precipitation (MOTP) method, and related probability distribution functions are produced. Outputs of AOGCMs are used as input to water requirement models. Then, produced by using the Monte Carlo method, 200 samples (discrete values) from the probability distribution functions of monthly downscaled temperature and precipitation in the study area are extracted by using a software for sensitivity and uncertainty analysis. Time series of climatic variables in fut...

Reservoir Operation for Simultaneously Meeting Water Demand and Sediment Flushing: Stochastic Dynamic Programming Approach with Two Uncertainties

AbstractRiver bed materials are commonly removed and conveyed downstream. In this process, some sediments are deposited in reservoirs, causing a decrease in reservoir active storage capacity and thus its ability to meet water demand. Flushing is a sediment-release method operated from the bottom outlet gates that releases stored water to flush sediments. As a result, water shortages may occur after the flushing operation. Thus, it is important to develop a reservoir operation policy for time and volume release of sediment that meets water demand. Uncertainties in water and sediment inflows to the reservoir are also important issues that add to the complexity of such policies. This paper presents a stochastic dynamic programming (SDP) model with two uncertainties to determine the simultaneous optimal operation policies for meeting water demand and sediment flushing. To evaluate the capability of the SDP model with two uncertainties, four other operation policies are developed, and all five scenarios are ev...