M. Janga Reddy

An efficient multi-objective optimization algorithm based on swarm intelligence for engineering design

As there is a growing interest in applications of multi-objective optimization methods to real-world problems, it is essential to develop efficient algorithms to achieve better performance in engineering design and resources optimization. An efficient algorithm for multi-objective optimization, based on swarm intelligence principles, is presented in this article. The proposed algorithm incorporates a Pareto dominance relation into particle swarm optimization (PSO). To create effective selection pressure among the non-dominated solutions, it uses a variable size external repository and crowding distance comparison operator.An efficient mutation strategy called elitist-mutation is also incorporated in the algorithm. This strategic mechanism effectively explores the feasible search space and speeds up the search for the true Pareto-optimal region. The proposed approach is tested on various benchmark problems taken from the literature and validated with standard performance measures by comparison with NSGA-II, one of the best multi-objective evolutionary algorithms available at present. It is then applied to three engineering design problems. The results obtained amply demonstrate that the proposed approach is efficient and is able to yield a wide spread of solutions with good coverage and convergence to true Pareto-optimal fronts.

Optimal reservoir operation for irrigation of multiple crops using elitist-mutated particle swarm optimization

Abstract To achieve social and economic sustainability in arid and semi-arid areas under water scarce situations, it is vital to promote efficient use of water through improved management of water resources. This paper presents a swarm optimization based solution to a detailed operational model for short-term reservoir operation for irrigation of multiple crops. The model integrates the dynamics associated with the water released from a reservoir to the actual water utilized by crops at farm level. It takes into account the nonlinear relationship of root growth, soil heterogeneity, soil moisture dynamics for multiple crops, yield response to water deficit at various growth stages of the crops and economic benefits from the crops. As the developed model is a nonlinear one, it is solved using a novel global optimization technique, namely elitist-mutation particle swarm optimization (EMPSO). The models applicability is demonstrated through a case study of Malaprabha Reservoir system in Southern India. The performance of the model is examined for different water deficit conditions and the sensitivity of the crop yield is analysed for water shortage at various growth stages. Also, the consideration of economic benefits in the objective function and its effect on the water allocation decisions for multiple crops are studied. Consequently, the output from the model includes initial storages, releases, overflows and evaporation losses for each 10-day period on the reservoir side; and allocation of water, actual evapotranspiration and initial soil moisture for each crop for each 10-day period on the field side, thus facilitating decision making for optimal utilization of the available water resources.

Probabilistic assessment of flood risks using trivariate copulas

In this paper, a copula-based methodology is presented for probabilistic assessment of flood risks and investigated the performance of trivariate copulas in modeling dependence structure of flood properties. The flood is a multi-attribute natural hazard and is characterized by mutually correlated flood properties peak flow, volume, and duration of flood hydrograph. For assessing flood risk, many studies have used bivariate analysis, but a more effective assessment can be possible considering all three mutually correlated flood properties simultaneously. This study adopts trivariate copulas for multivariate analysis of flood risks, and applied to a case study of flood flows of Delaware River basin at Port Jervis, NY, USA. On evaluation of various probability distributions for representation of flood variables, it is found that the flood peak flow and volumes can be best represented by Fréchet distribution, whereas flood duration by log-normal distribution. The joint distribution is modeled using four trivariate copulas, namely, three fully nested form of Archimedean copulas: Clayton, Gumbel–Hougaard, Frank copulas; and one elliptical copula: Student’s t copula. Based on distance-based performance measures, graphical tests, and tail-dependence measures, it is found that the Student’s t copula best representing the trivariate dependence structure of flood properties as compared to the other copulas. Similar results are found for bivariate copula modeling of flood variables pairs, where Student’s t copula performed better than the other copulas. The obtained copula-based joint distributions are used for multivariate analysis of flood risks, in terms of primary and secondary return periods. The resultant trivariate return periods are compared with univariate and bivariate return periods, and addressed the necessity of multivariate flood risk analysis. The study concludes that the trivariate copula-based methodology is a viable choice for effective risk assessment of floods.

Bivariate Flood Frequency Analysis of Upper Godavari River Flows Using Archimedean Copulas

In this paper, a copula based methodology is presented for flood frequency analysis of Upper Godavari River flows in India. By using the specific advantages of copula method in modeling the joint dependence structure of uncertain variables, this study applies Archimedean copulas for frequency analysis of flood characteristics annual peak flow, flood volume and flood duration. To determine the best fit marginal distributions for flood variables, few parametric and nonparametric probability distributions are examined and the best fit model is adopted for copula modeling. Four Archimedean family of copulas, namely Ali-Mikhail-Haq, Clayton, Gumbel-Hougaard and Frank copulas are evaluated for modeling the joint dependence of annual peak flow-volume, and flood volume-duration pairs. The performance of two parameter estimation methods, namely method-of-moments-like estimator based on inversion of Kendall’s tau and maximum pseudo-likelihood estimator for copulas are investigated. On performing Monte Carlo simulation to assess the performance of copula distributions in modeling the joint dependence structure of flood variables, it is found that the developed copula models are well representing the observed flood characteristics. From standard statistical tests, Frank copula has been identified as the best fitted copula for both bivariate models. The Frank copula function is used for obtaining joint and conditional return periods of flood characteristics, which can be useful for risk based design of water resources projects.

Evolving strategies for crop planning and operation of irrigation reservoir system using multi-objective differential evolution

In this paper multi-objective differential evolution (MODE) approach is proposed for the simultaneous evolution of optimal cropping pattern and operation policies for a multi-crop irrigation reservoir system. In general, farming community wants to maximize total net benefits by irrigating high economic value crops over larger area, which may also include water-intensive crops and longer duration crops. This poses a serious problem under water-scarce conditions and often results in crop failure. Under varying hydrological conditions, the fixed cropping pattern with conventional operating rule curve policies may not yield economically good results. To provide flexible policies, a nonlinear multi-objective optimization model is formulated. To achieve robust performance by handling interdependent relationships among the decision variables of the model, the recent MODE technique is adopted to solve the multi-objective problem. The developed model is applied for ten-daily reservoir operation to a case study in India. The model results suggest that changes in the hydrologic conditions over a season have considerable impact on the cropping pattern and net benefits from the irrigation system. Towards this purpose, the proposed MODE model can be used to evolve different strategies for irrigation planning and reservoir operation policies, and to select the best possible solution appropriate to the forecasted hydrologic condition.

Regional Rainfall Forecasting using Large Scale Climate Teleconnections and Artificial Intelligence Techniques

This paper presents an Artificial Intelligence approach for regional rainfall forecasting for Orissa state, India on monthly and seasonal time scales. The possible relation between regional rainfall over Orissa and the large scale climate indices like El-Nino Southern Oscillation (ENSO), EQUitorial INdian Ocean Oscillation (EQUINOO) and a local climate index of Ocean-Land Temperature Contrast (OLTC) are studied first and then used to forecast monsoon rainfall. To handle the highly non-linear and complex behavior of the climatic variables for forecasting the rainfall, this study employs Artificial Neural Networks (ANNs) methodology. To optimize the ANN architecture, Genetic Optimizer (GO) is used. After identifying the lagged relation between climate indices and monthly rainfall, the rainfall values are forecast for the summer monsoon months of June, July, August, and September (JJAS) individually, as well as for total monsoon rainfall. The models are trained individually for monthly and for seasonal rainfall forecasting. Then the trained models are tested to evaluate the performance of the model. The results show reasonably good accuracy for monthly and seasonal rainfall forecasting. This study emphasizes the value of using large-scale climate teleconnections for regional rainfall forecasting and the significance of Artificial Intelligence approaches like GO and ANNs in predicting the uncertain rainfall.

Multivariate modeling of droughts using copulas and meta-heuristic methods

This study investigated the utility of two meta-heuristic algorithms to estimate parameters of copula models and for derivation of drought severity–duration–frequency (S–D–F) curves. Drought is a natural event, which has huge impact on both the society and the natural environment. Drought events are mainly characterized by their severity, duration and intensity. The study adopts standardized precipitation index for drought characterization, and copula method for multivariate risk analysis of droughts. For accurate estimation of copula model parameters, two meta-heuristic methods namely genetic algorithm and particle swarm optimization are applied. The proposed methodology is applied to a case study in Trans Pecos, an arid region in Texas, USA. First, drought severity and duration are separately modeled by various probability distribution functions and then the best fitted models are selected for copula modeling. For modeling the joint dependence of drought variables, different classes of copulas, namely, extreme value copulas, Plackett and Student’s t copulas are employed and their performance is evaluated using standard performance measures. It is found that for the study region, the Gumbel–Hougaard copula is the best fitted copula model as compared to the others and is used for the development of drought S–D–F curves. Results of the study suggest that the meta-heuristic methods have greater utility in copula-based multivariate risk assessment of droughts.

Overtopping Probability Constrained Optimal Design of Composite Channels Using Swarm Intelligence Technique

In this paper swarm intelligence based methodology is proposed for optimal and reliable design of irrigation channels. The input parameters involved in channel design are prone to uncertainty and the solution of deterministic model may result in flooding risk and affect the stability of the channel. To provide reliability in the design, an overtopping probability constrained design is presented in this study. The deterministic equivalent of the probabilistic constraint is derived by following the principle of first order uncertainty analysis. In order to account for the uncertainty of design parameters in the objective function, a modified cost function is proposed. A methodology is propounded to solve it in a metaheuristic environment and solved it using elitist-mutated particle swarm optimization (EMPSO) method. The EMPSO based solutions are found to be quite successful and better than the classical optimization methods. Finally, it is concluded that the proposed methodology has a good potential for reliable design of composite channels for designer specified reliability values.

Reliability analysis of composite channels using first order approximation and Monte Carlo simulations

Artificial open channels being costlier infrastructure, their design should ensure reliability along with optimality in project cost. This paper presents reliability analysis of composite channels, considering uncertainty associated with various design parameters such as friction factors, longitudinal slope, channel width, side slope, and flow depth. This study also considers uncertainties of watershed characteristics, rainfall intensity and drainage area to quantify the uncertainty of runoff. For uncertainty modeling, the advanced first order second moment method and Monte Carlo simulation are used and it is found that the results by both approaches show good agreement. Then, a reliability index that can be used to design a composite channel to convey design discharge for a specified risk or probability of failure is presented, and its sensitivity with different channel design parameters are analyzed. To validate the effectiveness of the present approach, the reliability values and safety factors for variable system loading scenario are obtained under static and dynamic environment. The sensitivity analysis shows that flow depth and bed width are the most influencing parameters that affect the safety factor and reliability.

Optimal Reservoir Operation for Hydropower Production Using Particle Swarm Optimization and Sustainability Analysis of Hydropower

This paper presents a derivation of optimal operation policies for hydropower production in the Upper Seti Hydro-Power Reservoir system in Nepal using particle swarm optimization (PSO) technique. A reservoir operation model for the Upper Seti project is formulated with an objective of maximising the annual hydropower production operated at a weekly time scale subjected to various physical and operational constraints. An elitist-mutated PSO (EMPSO) technique is applied for solving the weekly reservoir operational model, and the EMPSO-based solutions are found to result in 3% more hydropower than the planned hydropower production. The reservoir operation policies are also compared for wet, dry and normal water years, and it is noted that there exist significant differences among the release policies for those hydrologic conditions. Later, the reservoir operation model is modified with an objective of minimising the annual sum of squared deviation between weekly energy production and target hydropower. Then the hydropower analysis is carried out for various target hydropower values with an aim of finding suitable firm-power for the project. The performances of various reservoir operation policies are evaluated using reliability, resilience and vulnerability measures. The sustainability of the system is evaluated by computing the sustainability index, which is then used to evolve suitable hydropower targets. It is found that a target hydropower of 4.8 GWh with a sustainability index of 0.75 may result in better overall performance of the system.