D. Nagesh Kumar

Ranking alternatives with fuzzy weights using maximizing set and minimizing set

Ranking alternatives (both qualitative as well as quantitative) in a multicriterion environment, employing experts opinion (preference structure) using fuzzy numbers and linguistic variables, are presented in this paper. Fuzzy weights (wJ i ) of alternatives (A i ) are computed using standard fuzzy arithmetic. Concept of maximizing set and minimizing set is introduced to decide total utility or ordering value of each of the alternatives. A numerical example is provided at the end to illustrate the method. ( 1999 Published by Elsevier Science B.V. All rights reserved.

An integrated model for optimal reservoir operation for irrigation of multiple crops

An integrated model is developed, based on seasonal inputs of reservoir inflow and rainfall in the irrigated area, to determine the optimal reservoir release policies and irrigation allocations to multiple crops. The model is conceptually made up of two modules. Module 1 is an intraseasonal allocation model to maximize the sum of relative yields of all crops, for a given state of the system, using linear programming (LP). The module takes into account reservoir storage continuity, soil moisture balance, and crop root growth with time. Module 2 is a seasonal allocation model to derive the steady state reservoir operating policy using stochastic dynamic programming (SDP). Reservoir storage, seasonal inflow, and seasonal rainfall are the state variables in the SDP. The objective in SDP is to maximize the expected sum of relative yields of all crops in a year. The results of module 1 and the transition probabilities of seasonal inflow and rainfall form the input for module 2. The use of seasonal inputs coupled with the LP-SDP solution strategy in the present formulation facilitates in relaxing the limitations of an earlier study, while affecting additional improvements. The model is applied to an existing reservoir in Karnataka State, India.

River Flow Forecasting using Recurrent Neural Networks

Forecasting a hydrologic time series has been one of the most complicated tasks owing to the wide range of data, the uncertainties in the parameters influencing the time series and also due to the non availability of adequate data. Recently, Artificial Neural Networks (ANNs) have become quite popular in time series forecasting in various fields. This paper demonstrates the use of ANNs to forecast monthly river flows. Two different networks, namely the feed forward network and the recurrent neural network, have been chosen. The feed forward network is trained using the conventional back propagation algorithm with many improvements and the recurrent neural network is trained using the method of ordered partial derivatives. The selection of architecture and the training procedure for both the networks are presented. The selected ANN models were used to train and forecast the monthly flows of a river in India, with a catchment area of 5189 km2 up to the gauging site. The trained networks are used for both single step ahead and multiple step ahead forecasting. A comparative study of both networks indicates that the recurrent neural networks performed better than the feed forward networks. In addition, the size of the architecture and the training time required were less for the recurrent neural networks. The recurrent neural network gave better results for both single step ahead and multiple step ahead forecasting. Hence recurrent neural networks are recommended as a tool for river flow forecasting.

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 crop planning and conjunctive use of water resources in a coastal river basin

Due to increasing trend of intensive rice cultivation in a coastal river basin, crop planning and groundwater management areimperative for the sustainable agriculture. For effective management, two models have been developed viz. groundwater balance model and optimum cropping and groundwater management model to determine optimum cropping pattern and groundwater allocation from private and government tubewells according to different soil types (saline and non-saline), type of agriculture(rainfed and irrigated) and seasons (monsoon and winter). A groundwater balance model has been developed considering mass balance approach. The components of the groundwater balance considered are recharge from rainfall, irrigated rice and non-rice fields, base flow from rivers and seepage flow from surface drains. In the second phase, a linear programming optimization model is developed for optimal cropping and groundwater management for maximizing the economic returns. Themodels developed were applied to a portion of coastal river basin in Orissa State, India and optimal cropping pattern forvarious scenarios of river flow and groundwater availability wasobtained.

Bayesian dynamic modeling for monthly Indian summer monsoon rainfall using El Niño–Southern Oscillation (ENSO) and Equatorial Indian Ocean Oscillation (EQUINOO)

There is an established evidence of climatic teleconnection between El Nino–Southern Oscillation (ENSO) and Indian summer monsoon rainfall (ISMR) during June through September. Against the long-recognized negative correlation between ISMR and ENSO, unusual experiences of some recent years motivate the search for some other causal climatic variable, influencing the rainfall over the Indian subcontinent. Influence of recently identified Equatorial Indian Ocean Oscillation (EQUINOO, atmospheric part of Indian Ocean Dipole mode) is being investigated in this regard. However, the dynamic nature of cause-effect relationship burdens a robust and consistent prediction. In this study, (1) a Bayesian dynamic linear model (BDLM) is proposed to capture the dynamic relationship between large-scale circulation indices and monthly variation of ISMR and (2) EQUINOO is used along with ENSO information to establish their concurrent effect on monthly variation of ISMR. This large-scale circulation information is used in the form of corresponding indices as exogenous input to BDLM, to predict the monthly ISMR. It is shown that the Indian monthly rainfall can be modeled in a better way using these two climatic variables concurrently (correlation coefficient between observed and predicted rainfall is 0.82), especially in those years when negative correlation between ENSO and ISMR is not well reflected (i.e., 1997, 2002, etc.). Apart from the efficacy of capturing the dynamic relationship by BDLM, this study further establishes that monthly variation of ISMR is influenced by the concurrent effects of ENSO and EQUINOO.

Multisite disaggregation of monthly to daily streamflow

Streamflow disaggregation is used to preserve statistical attributes of time series across multiple sites and timescales. Several algorithms for spatial disaggregation and for disaggregation of annual to monthly flows are available. However, the disaggregation of monthly to daily or weekly to daily flows remains a challenge. A new algorithm is presented for simultaneously disaggregating monthly flows at a number of sites and daily flows at an index site to daily flows at a number of sites on a drainage network. The continuity of flow in time across months at each site as well as the intersite flow pattern are preserved. The disaggregated daily flows at the multiple sites are conditioned on the spatial (across site) pattern of monthly flows at the respective sites. The probability distribution of the vector of disaggregated flows conditional on the multisite monthly flows is approximated nonparametrically using the k nearest neighbors of the monthly spatial flow pattern. A constrained optimization problem is solved to adaptively estimate the disaggregated flows in space and time for each such neighborhood. An application to data from a tributary of the Colorado River is used to illustrate the modeling process.

Ranking multi-criterion river basin planning alternatives using fuzzy numbers

Abstract The methodology proposed by Anand Raj and Nagesh Kumar [5] to rank the river basin planning and development alternatives under multi-criterion environment using fuzzy numbers is applied to a case study. The purpose is to find the most suitable planning of reservoirs with their associated purposes aimed at the development of one of the major peninsular river basins (Krishna river basin) in India. A set of 7 alternative systems with 8 main objectives, which are further subdivided into 18 criteria, are considered for ordering or ranking them employing the opinion (preference structure) of three experts: an acadamician, a field engineer and an official from Ministry of Water Resources, using fuzzy numbers. The fuzzy weights ( w i ) of alternatives ( A i ) are computed using standard fuzzy arithmetic. The concepts of maximizing set and minimizing set are introduced to decide total utility or order value of each of the alternatives.

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.

Artificial neural networks and multicriterion analysis for sustainable irrigation planning

The objective of the present paper is to select the best compromise irrigation planning strategy for the case study of Jayakwadi irrigation project, Maharashtra, India. Four-phase methodology is employed. In phase 1, separate linear programming (LP) models are formulated for the three objectives, namely, net economic benefits, agricultural production and labour employment. In phase 2, nondominated (compromise) irrigation planning strategies are generated using the constraint method of multiobjective optimisation. In phase 3, Kohonen neural networks (KNN) based classification algorithm is employed to sort nondominated irrigation planning strategies into smaller groups. In phase 4, multicriterion analysis (MCA) technique, namely, Compromise Programming is applied to rank strategies obtained from phase 3. It is concluded that the above integrated methodology is effective for modeling multiobjective irrigation planning problems and the present approach can be extended to situations where number of irrigation planning strategies are even large in number.