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This paper describes experience with application of operations research techniques in planning new roads and social service centers for the Dharampur area in India. The work involved designing a road network using a minimal spanning tree algorithm; selecting the number and location of the service centers to be constructed over a five year period using a set covering algorithm and developing a schedule for the construction of the service centers using dynamic programming. Substantial economic benefits resulted from using the mathematical models. The experience shows that operations research models can be useful in providing an objective rationale for administrative decisions, thus playing an important role in the resolution of political conflict. [ABSTRACT FROM AUTHOR]

This article outlines a method of estimating probabilities of gross transfers from one age to another due to misreporting of age. An essential ingredient in the computation is the information on the true age structure of the population, which may be estimated by using generalized stable population relationships. The method consists essentially of iteratively adjusting rows and columns of an initial guess matrix so that the application of the resultant transition matrix to the true age distribution produces the recorded age distribution. The initial guess matrix can be either empirically based or theoretically derived. The method is illustrated by using data on India, 1971-1981. The application reveals that in India, although the tendency to exaggerate age is strong at adult ages, the bias does not increase appreciably at older ages, as is commonly believed. [ABSTRACT FROM AUTHOR]

Objective: To compare hospital outcome prediction using an artificial neural network model, built on an Indian data set, with the APACHE II (Acute Physiology and Chronic Health Evaluation II) logistic regression model.Design: Analysis of a database containing prospectively collected data.Setting: Medical-neurological ICU of a university hospital in Mumbai, India.Subjects: Two thousand sixty-two consecutive admissions between 1996 and 1998.Interventions: None.Measurements and Results: The 22 variables used to obtain day-1 APACHE II score and risk of death were recorded. Data from 1,962 patients were used to train the neural network using a back-propagation algorithm. Data from the remaining 1,000 patients were used for testing this model and comparing it with APACHE II. There were 337 deaths in these 1,000 patients; APACHE II predicted 246 deaths while the neural network predicted 336 deaths. Calibration, assessed by the Hosmer-Lemeshow statistic, was better with the neural network (H=22.4) than with APACHE II (H=123.5) and so was discrimination (area under receiver operating characteristic curve =0.87 versus 0.77, p=0.002). Analysis of information gain due to each of the 22 variables revealed that the neural network could predict outcome using only 15 variables. A new model using these 15 variables predicted 335 deaths, had calibration (H=27.7) and discrimination (area under receiver operating characteristic curve =0.88) which was comparable to the 22-variable model (p=0.87) and superior to the APACHE II equation (p<0.001).Conclusion: Artificial neural networks, trained on Indian patient data, used fewer variables and yet outperformed the APACHE II system in predicting hospital outcome. [ABSTRACT FROM AUTHOR]

We present an inversion technique based on the Marquardt algorithm to estimate the depth of a 2.5-D sedimentary basin in addition to the regional gravity anomaly that is associated with the residual gravity anomaly, wherein the density contrast varies parabolically with depth. Forward modeling is carried out through a derived analytical gravity expression of a 2.5-D vertical prism. Inversion of a theoretical gravity anomaly with and without a regional gravity anomaly illustrates the procedure that it is found to be insensitive to the regional gravity effect. Furthermore, the algorithm is exemplified with the gravity anomalies of the derived density-depth model of the Godavari subbasin, India with a parabolic density profile resulting in a more consistent geological model rather than a constant density profile. The main advantage of this method is that it works well even when the profile of interpretation does not bisect the strike length of the sedimentary basin. [ABSTRACT FROM AUTHOR]

In this research feeder routes and coordinated schedules were developed for integration of feeder public buses with suburban trains. A Heuristic Feeder Route Generation Algorithm was developed for generation of feeder routes. Optimal coordinated schedules for feeder buses were developed in coordination with the existing given schedules of suburban trains. Genetic algorithm, which is a robust optimization technique, is used for the optimizing coordinated schedules. Andheri and Vileparle suburban railway stations of Mumbai, India were selected as study areas. Feeder routes for public buses and coordinated schedules of public buses for the existing given schedules of suburban trains at the two suburban railway stations were successfully developed in this research. [ABSTRACT FROM AUTHOR]

This paper reports results on the LAI Retrieval and Validation Experiment (LRVE) that was conducted for two agricultural areas in Central India during the winter season of 2001–2002. The study aimed at relating field measurements of leaf area index (LAI) to spaceborne Indian Remote Sensing Satellite (IRS) Linear Imaging Self Scanning Sensor-III (LISS-III) data, preparation of site-level LAI maps, and validation of Moderate Resolution Imaging Spectroradiometer (MODIS) 1-km LAI global fields. Measurements of field-level LAI, aerosol optical thickness and water vapor were carried out on the day of LISS-III overpasses. Empirical models based on the site-specific LAI-vegetation index relation were developed and used to generate 23-m resolution LAI maps for two sites (Indore and Bhopal) covering 30 km × 30 km. These LAI images were degraded to 1-km spatial resolution and used for validation of the version 3 and 4 MODIS LAI products (MOD15A2). The results indicate a positive correlation (r = 0.78) between LAI derived from LISS-III data and MODIS data. However an overestimate by a factor of 1.6 to 2.5 in the version 3 MODIS product is observed with root mean square error (RMSE) ranging from 0.20 to 1.26. The factor of overestimation reduces significantly by 50% and RMSE by 40% when version 4 MODIS LAI was analyzed. The improvement in accuracy was observed to be associated with the change in algorithm path adopted for retrieving version 3 and 4 MODIS LAI. Analysis of the MODIS land cover product that is an input in the MODIS LAI retrieval algorithm indicated errors in assigning land cover classes for the study sites, which could be one of the sources of error in MODIS LAI product. [ABSTRACT FROM AUTHOR]

Seasonal climate prediction offers potentially useful information for water managers. However, implementing forecast information is challenging due to the probabilistic nature of forecasts and limited demonstrations of usefulness. In this study, an adaptive groundwater pricing model utilizing operational seasonal climate forecasts was evaluated for groundwater management. The price for groundwater in the upcoming season was selected according to an algorithm that incorporates the current groundwater elevation and a prediction of seasonal rainfall. A simulation based on 37 years of rainfall data and operational monsoon forecasts for Tamil Nadu, India, was conducted to assess the effect of forecast-based pricing on societal benefits, groundwater elevation, and farmer income. Results indicate the adaptive pricing model is far more effective at maintaining groundwater elevations and maximizing societal benefits than a static groundwater price. Current groundwater elevation was a more effective input to the pricing algorithm than the forecast of seasonal rainfall when evaluated separately. A comparison of forecast use by water managers to select prices and use by farmers to choose crop patterns found similar societal benefits for each approach. Controlling demand for groundwater through pricing will cause hardship for current groundwater users who currently access groundwater without tariffs. Water managers should consider using tariff revenue to provide drought relief and transitional assistance. [ABSTRACT FROM AUTHOR]

This paper presents a Multi-objective Evolutionary Algorithm (MOEA) to derive a set of optimal operation policies for a multipurpose reservoir system. One of the main goals in multiobjective optimization is to find a set of well distributed optimal solutions along the Pareto front. Classical optimization methods often fail in attaining a good Pareto front. To overcome the drawbacks faced by the classical methods for Multi-objective Optimization Problems (MOOP), this study employs a population based search evolutionary algorithm namely Multi-objective Genetic Algorithm (MOGA) to generate a Pareto optimal set. The MOGA approach is applied to a realistic reservoir system, namely Bhadra Reservoir system, in India. The reservoir serves multiple purposes irrigation, hydropower generation and downstream water quality requirements. The results obtained using the proposed evolutionary algorithm is able to offer many alternative policies for the reservoir operator, giving flexibility to choose the best out of them. This study demonstrates the usefulness of MOGA for a real life multi-objective optimization problem. [ABSTRACT FROM AUTHOR]

In this paper a metaheuristic technique called Ant Colony Optimization (ACO) is proposed to derive operating policies for a multi-purpose reservoir system. Most of the real world problems often involve non-linear optimization in their solution with high dimensionality and large number of equality and inequality constraints. Often the conventional techniques fail to yield global optimal solutions. The recently proposed evolutionary algorithms are also facing problems, while solving large-scale problems. In this study, it is intended to test the usefulness of ACO in solving such type of problems. To formulate the ACO model for reservoir operation, the problem is approached by considering a finite time series of inflows, classifying the reservoir volume into several class intervals, and determining the reservoir release for each period with respect to a predefined optimality criterion. The ACO technique is applied to a case study of Hirakud reservoir, which is a multi-purpose reservoir system located in India. The multiple objectives comprise of minimizing flood risks, minimizing irrigation deficits and maximizing hydropower production in that order of priority. The developed model is applied for monthly operation, and consists of two models viz., for short-time horizon operation and for long-time horizon operation. To evaluate the performance of ACO, the developed models are also solved using real coded Genetic Algorithm (GA). The results of the two models indicate that ACO model performs better, in terms of higher annual power production, while satisfying irrigation demands and flood control restrictions, compared to those obtained by GA. Finally it is found that ACO model outperforms GA model, especially in the case of long-time horizon reservoir operation. [ABSTRACT FROM AUTHOR]

This paper provides a study on the development and the use of a small autonomous surface vehicle (ASV) that automatically follows programmed mission transects, while measuring sensor outputs along the tracks. It discusses the mechanical construction of the ASV, the distributed architecture of controller area network (CAN)-based nodes for science and vehicle payloads, high-speed radio-frequency (RF) communications, the performance of the heading autopilot, global positioning system (GPS)-based guidance algorithm, and the mission programming technique. The field trials of the ASV, performed off the coast of Goa, India, are focused on retrieving the 2-D spatial distribution of surface chlorophyll, which is one of the useful parameters in characterizing the nature of calibration-validation (CALVAL) sites for ocean remote sensing needs. A further benefit of ASVs is that they can be built at a low cost and used in monitoring applications of diverse coastal ecosystems. [ABSTRACT FROM AUTHOR]