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This study proposes an updated renewal model to estimate active patent value and technical depreciation in five major Indian technology fields. According to a study of 27,000 patents issued by the Indian Patent Office between 1998 and 2018, Indian patents have a lower value than foreign patents. This suggests that India's patenting and R&D qualities differ from those of more advanced countries. Technology-specific factors are important because technology fields depreciate at different rates. The top 5% of patents accounted for most international and domestic patent values. A genetic algorithm is used to optimise the renewal model accuracy. This study has significant implications for Indian policymakers, patent holders, international investors, and technology market participants. This model allows startups and new businesses who want to value their IP at the very early stage to negotiate with their counterpart when licensing or selling the patents. [ABSTRACT FROM AUTHOR]

Emergency Medical Service (EMS) is an integral part of the healthcare system that works dedicatedly to save the lives of people. The prime responsibility of any EMS provider is to offer a timely response to the person in need. The timely assistance increases the chances of survivability of a person. In India, escalating count of road accidents has increased the demand for ambulance services to provide pre-hospital treatment or transportation of victims to the hospital. This paper presents a new optimization strategy of Hybrid Particle Swarm Optimization and Genetic Algorithm (HPSOGA) for ambulance allocation to reduce the ambulance response time. Considering a set of assumptions, the authors have applied the new strategy for allocating 50 ambulances to 11 base stations in Southern Delhi. The working environment of EMS which includes stochastic requests, travel time, and dynamic traffic conditions have been taken into account to attain accurate results. The new optimization strategy of HPSOGA has been implemented in a MATLAB environment to find an optimized allocation plan with minimum response time. With the proposed algorithm the authors have been able to reduce the average response time by 11.61%. The paper also presents the comparison of HPSOGA, Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) for the stated problem. The algorithms are compared in terms of objective value (response time), convergence rate, and constancy repeatability to conclude that HPSOGA performs better than the other two algorithms. [ABSTRACT FROM AUTHOR]

The significance of air quality monitoring for analyzing the impact on public health is growing worldwide. A crucial part of smart city development includes deployment of suitable air pollution sensors at critical locations. Note that there are various air quality measurement instruments ranging from expensive reference stations that provide accurate data to low-cost sensors that provide reasonable air quality measurements. In this research, we use a combination of sensors and monitors, which we call as hybrid instruments and focus on optimal placement of such instruments across a region. The objective of the problem is to maximize a satisfaction function that quantifies the weighted closeness of different regions to the places where such hybrid instruments are placed (here weights for different regions are quantified in terms of the relative population density and relative PM2.5 concentration). Note that there can be several constraints such as those on budget, minimum number of reference stations to be placed, set of important regions where at least one sensor should be placed and so on. We develop two algorithms to solve this problem. The first one is a genetic algorithm that is a metaheuristic and works on the principles of evolution. The second one is a greedy algorithm that selects locally best choice in each iteration. We test these algorithms on different regions from India with varying sizes and other characteristics such as population distribution, PM2.5 concentration, budget available, etc. The insights obtained from this paper can be used to quantitatively place reference stations and sensors in large cities rather than using ad hoc procedures or rules of thumb. [ABSTRACT FROM AUTHOR]

Mathematical validation and comparison of coal milling operations of a horizontal ball tube mill and a vertical bowl mill located in two distinct coal based power plants in India was carried out. The coal mill model was developed and the unknown model coefficients were obtained numerically by genetic algorithm (GA). The input variables of mass flow rates of raw coal and air and temperature of inlet primary air were obtained from the on-site data. The output variables of mill current and mill outlet temperature as obtained by GA were compared with on-site data for the two mills and show good agreement. The ball tube mill requires less mass flow rate of primary air and raw coal per MW of power produced than the bowl mill. It contains larger mass of raw and pulverized coal but less grinding heat is generated in this mill. [ABSTRACT FROM AUTHOR]

In the technological development era, the solution to various complex problems is uncertain, and more attention should be paid for changing certain conditions. Therefore, a precise solution to a complex problem is critical. The traveling salesman problem (TSP) is often fuse for an outbreak of a better solution. Inspired by the successful genetic algorithm (GA) applications, this study proposes a new approach to improve the convergence rate by incorporating a unique feature, namely 'sub-tour division'. The new method consists of multiple zones of TSPs (i.e., active and inactive), which are used to sort and group the critical region for finding the solutions. To illustrate the performance of the new approach, the traveling distance between various cities in India is considered as a problem. The simulation findings show that the new approach provides a more accurate and robust solution to a complex problem than alternative methods. [ABSTRACT FROM AUTHOR]

This paper presents grid-oriented multiobjective harmony search algorithm (GOMOHSA) to incorporate the multiple grid parameters for minimization of the active power loss, reactive power loss, and total voltage deviations (TVD) in a part of practical transmission network of Rajasthan Rajya Vidyut Prasaran Nigam Limited (RVPN) in southern parts of Rajasthan state of India. This is achieved by optimal deployment of optimally sized renewable energy (RE) generators using GOMOHSA. Performance indexes such as active power loss minimization index (APMLI), the reactive power loss minimization index (RPMLI), and the total voltage deviation improvement index (TVDII) are introduced to evaluate the health of the test network with different load scenarios. Performance of proposed GOMOHSA has been tested for five different operating scenarios of loads and RE generation. It is established that the proposed GOMOHSA finds the optimal deployment of optimally sized RE generators, and the investment cost of deployment of these RE generators can be recovered within a time period that is less than 5 years. Performance of GOMOHSA is superior compared to a conventional genetic algorithm (GA) in terms of performance indexes, RE generator capacity, payback period, and parameter sensitivity. Study is performed using MATLAB software for loading scenario of base year 2021 and projected year 2031. [ABSTRACT FROM AUTHOR]

Here, a new naturally-inspired stochastic nonlinear joint and individual inversion technique for integrating direct current (DC) and magnetotelluric (MT) data interpretation-based simulation of a swarm intelligence combo with specific capabilities for exploitation of the variable weight particle swarm optimizer (vPSO) and exploration of the grey wolf optimizer (GWO), vPSOGWO, is used. They are particularly notable for their capacity for information exchange while hunting for food. Through synthetic MT and DC data contaminated with various sets of random noise, the applicability of the anticipated vPSOGWO algorithm based joint and individual inversion algorithm was assessed. The field examples, collected from diversified different geological terrains, including Digha (West Bengal), India; Sundar Pahari (Jharkhand), India; Puga Valley (Ladakh), India; New Brunswick, Canada; and South Central Australia, have shown the practical application of the proposed algorithm. Further, a Bayesian probability density function (bpdf) for estimating a mean global model and uncertainty assessment in posterior; and a histogram for model resolution assessment have also been created using 1000 inverted models. We examined the inverted outcomes and compared them with results from other cutting-edge methodologies, including the GWO, PSO, genetic algorithm (GA), Levenberg–Marquardt (LM), and ridge-regression (RR). Our findings showed that the current methodology is more effective than the GWO, PSO, GA, LM, and RR techniques at consistently improving the convergence of the global minimum. In contrast to earlier approaches, the current cutting-edge strategy vPSOGWO offers an improved resolution of an additional significant crustal thickness of about 65.68 ± 1.96 km over the Puga Valley, in which the inverted crustal thickness determined by vPSOGWO agrees well with the published crustal thickness over the Puga Valley. The new technology brings simulations closer to genuine models by significantly reducing uncertainty and enhancing model resolution. [ABSTRACT FROM AUTHOR]

Urban features can be analysed using high-resolution satellite images. Satellites procure panchromatic images at a spatial resolution even better than 1 m. However, it cannot cover the large area of a city in one scene. Therefore, satellite images from different dates and times must be mosaicked to study the urban region as a whole. Efficient and seamless mosaicking requires relative radiometric normalization (RRN), which generally is an iterative process. In this paper, an approach using the genetic algorithm has been developed for normalization in a single step. The algorithm has been tested on Jaipur city in India. Results produced for the mosaic using genetic algorithm provide visually seamless mosaic over the study region with significant improvement of the RMSE among the images’ overlap region. The algorithm has been compared against the mean-standard deviation (MS) and histogram matching (HM) methods of RRN with average RMSE improvement of 40% and 38%, respectively [ABSTRACT FROM AUTHOR]

In this paper, we have proposed a stochastic Knapsack Problem (KP) based mathematical model for small-scale vegetable sellers in India and solved it by an advanced Genetic Algorithm. The knapsack problem considered here is a bounded one, where vegetables are the objects. In this model, we have assumed that different available vegetables (objects) have different weights (that are available), purchase costs, and profits. The maximum weight of vegetables that can be transported by a seller is limited by the carrying capacity of the vegetable carrier and the business capital of the seller is also limited. The aim of the proposed mathematical model is to maximize the total profit of the loaded/traded items, with a set of predefined constraints on the part of the vegetable seller or retailer. This problem has been solved in a Type-2 fuzzy environment and the Critical Value (CV) reduction method is utilized to defuzzify the objective value. We have projected an improved genetic algorithm based approach, where we have incorporated two features, namely refinement and immigration. We have initially considered benchmark instances and subsequently some redefined cases for experimentation. Moreover, we have solved some randomly generated proposed KP instances in Type-2 fuzzy environment. [ABSTRACT FROM AUTHOR]

Copious and accessible sun radiation creates an attractive system for converting solar energy into electrical energy. The solar power plants are installed in remote locations and hence needs monitoring and controlling system from remotely. A computerized remote solar plant monitoring system with an enormous range of solar Photovoltaic (PV) exploitation needs well-developed systems with Web-based interfaces. This research work focuses an inter-disciplinary field of renewable solar power plants located in the southern region of India, integrating with recent digital technology of Internet of Things (IoT) which supports to monitor real-time data of solar irradiation (G), temperature (T), clearness index (Kt), panel voltage (Vpv), current (Ipv), peak power (Pmp), and tilt angle of solar panel (β°) and also to control the optimum inclination angle (β°opt) of the solar panel for maximizing of PV power using Genetic Algorithm (GA). An integrated solar power plant with IoT is developed with hardware setup and virtually tested for enhancing the tracking behavior of the proposed system. The IoT-based control of solar power plant significantly improves the performance of monitoring and maintenance of the power plant parameters with cost-effectiveness. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are among the most revolutionary breakthroughs by the context of decarbonization of road transport. The adoption of EVs has drawn increasing attention and is currently considered a potential route towards sustainable transportation. Businesses approaching the EV market must establish lucrative business models that overcome the adoption obstacles for EVs while assuring long-term growth. The intent of the current research exploration is to create a cutting-edge framework for EV enterprises that aims to maximize profit and save delivery time leading to high customer satisfaction. The volume discount strategy is used for profit escalation. The model presented in this paper is highly adaptable for a real-world problem as it incorporates uncertainty in the EV industry through generalized triangular neutrosophic numbers, an extension of fuzzy numbers. A modified version of the neutrosophic compromise programming approach is proposed in this paper. The results from this approach are validated using genetic algorithm. The proposed optimization-based framework is encapsulated in the form of study of a case from Bangalore city, in India. An in-depth exploration of two cases along with sensitivity analysis is carried out that helped in prioritizing some discrete customers and gave insights on which manufacturing site should be designated more focus. • Profit maximizing business model for electric vehicle (EV) merchants is proposed. • Uncertainty in EV industry is captured using fuzzy number. • A modified neutrosophic compromise programming technique is proposed. • Case study is considered involving particular case and sensitivity analysis. [ABSTRACT FROM AUTHOR]

In the hilly regions of India, hydro power potential is available, but it is not completely utilized for power generation till date due to the unavailability of proper electrical structure. Nine micro/mini hydro power plants have been developed in the bageswar district in Uttarakhand. The development of the optimized Mini-grid structure is an effective and reliable solution for the full utilization of these MHPs potentials. Although Ajit Singh et al. proposed some Mini-grid structures for this area, their work is not appropriate due to their calculation method of reliability. For the determination of reliability (failure and repair time), they have considered only the line length. However, its demographic situations such as elevation level of MHPs, availability of roads, the presence of obstacles like streams will also affect the failure and repair time. This paper proposes an optimized Mini-grid structure for interconnection of these nine MHPs and load substations. All the MHPs are spreads in two blocks, namely Kapkote and Bagheswar, so two substations (load canters) are considered. These MHPs and substations are mapped through the Google Earth software and the optimization data such as distances between MHPs and substations, their elevation levels, the highest and lowest elevation in the connecting path and the path profile are collected through it. The Mini-grid structure is optimized through the Genetic Algorithm using the MATLAB tool. The optimization is performed by making the cost function as an objective function considering the line lengths and reliability of the path for its parameters. [ABSTRACT FROM AUTHOR]

This work assessed the effects of joint operation on the performance of multipurpose, multireservoir systems vis-à-vis standalone operation. The analysis used the multipurpose Pong and Bhakra reservoirs in northern India. Zone-based rule curves with or without hedging optimized using genetic algorithms were used for operating the reservoirs. The results showed that standalone configurations without hedging produced irrigation water reliability exceeding 90%, but the vulnerability indices (40% for Pong and 58% for Bhakra) were also high. Hedging tempered the standalone vulnerability; however, integrated operation also caused the vulnerability to reduce without hedging. Flood control was also enhanced during the flood season, with flood freeboard increasing by 12 and 3 m, respectively, at Bhakra and Pong reservoirs, relative to nonoptimized operation. Hydropower generation was lower than installed capacity at both reservoirs for both standalone and joint operations. The main conclusion of the study is that although hedging will moderate the vulnerability of standalone systems, the same outcome can be achieved without the deliberate water rationing of hedging if reservoirs are operated jointly. [ABSTRACT FROM AUTHOR]

This paper is concerned with the use of satellite image processing and remote sensing approaches to determine the water-spread area and capacity of a drinking water reservoir in Poondi, near Chennai City, south India. Estimation of water-spread area and volume of the reservoirs using traditional methods like field surveys, acoustic surveys and hydrographic surveys is time-consuming, laborious and expensive. To overcome these problems, many researchers have used satellite images and the per-pixel (hard classification), the sub-pixel (soft classification) and the super resolution mapping approaches. The conventional hard and soft classification approaches do not give accurate results due to the presence of mixed pixels in the image scene of the periphery of the reservoir. In this work, a per-pixel algorithm (Maximum Likelihood), a sub-pixel algorithm (Fuzzy C Means) and a super resolution mapping algorithm (Genetic algorithm) are developed and the water-spread area is estimated for the reservoir using multi-date Landsat8 OLI images. The volume of the reservoir at different water levels are estimated using the water-spread area and the Trapezoidal formula. The capacity estimated from satellite image-derived area is compared with the capacity data obtained from the Poondi reservoir authority. The error in estimation due to the per-pixel approach is 16.74%, while it is 9.06% for the sub-pixel approach and a mere 3.25% for the super-resolution approach. Thus, the super-resolution mapping approach results in minimum error when compared to the sub-pixel approach which in turn gives lesser error result when comparing with the per-pixel approach. [ABSTRACT FROM AUTHOR]

Summary: Intelligent Transport System (ITS) involves a number of Information and Communication Technology (ICT) interventions for efficient road safety management. Over the last few decades, there has been a significant increase in the safety of Vulnerable Road Users (VRU's). This study aims to identify the most frequent, regularly occurring causes of road accidents and outline actions that can be used as a baseline for improving road traffic safety. The main contributions in this paper include the following: a graphical investigation for finding out the overall rate of road accidents (injuries and deaths) in India. Based on graphical analysis, major loopholes responsible for road accidents in India are summed up. This paper discusses various issues faced by already available pedestrian and vehicular safety techniques. After analysis of the available techniques and their related case studies in a systematic way, it is observed that they are not able to overcome the loopholes (Collision Occurrence, Traffic Congestion, Pedestrian Crossing alert, Red Light violation, and Non‐Propagation of Distress Signal) present in the current "Road Transport Management (RTM) System" of India. Finally, after deep analysis of already available techniques, a futuristic approach "Mesh‐Networking based Vehicle Ad‐Hoc System (MN‐VAS) using hybrid of Neuro‐Fuzzy and Genetic Algorithm based Load Balancing on Node" is proposed for the safety of VRU's. This hybrid approach has been discussed with domain experts for practical input, and we concluded that this might be a good option for better RTM System in India. [ABSTRACT FROM AUTHOR]

Carrier Aggregation (CA) technology, introduced in 3GPP release 10, aims at increasing Spectral Efficiency (SE) only, which may lead to huge undesirable circuit power consumption. Hence, not only SE, but also Energy Efficiency (EE), particularly the tradeoff between both the parameters, is crucial in the design of CA systems. In this work, a novel approach, using Genetic Algorithm (GA) is proposed to solve the SE-EE tradeoff problem for a 5 G heterogeneous network with intra-band contiguous Carrier Aggregation (CA). Complete physical layer simulation is performed for generating the CA waveform, with parameters set in accordance with practical deployment scenarios, as specified in 3GPP TS 38.104. Carrier aggregated waveform is generated in 5 G New Radio (NR) frequency band FR1, using frequency bands in India which are currently not allocated for licenced usage. From simulations, it is observed that the proposed technique ensures that neither SE nor EE is seriously degraded for improving the other, for any given value of transmit power. The proposed technique, which performs Multi Objective Optimisation (MOO) using SPEA-2 GA outperforms Single Objective Optimisation (SOO) methods providing a maximum of 37% improvement in SE and 43% improvement in EE. [ABSTRACT FROM AUTHOR]

With the increasing hazard to the environment and progressions in renewable energy technologies, hybrid renewable energy systems can meet the energy demand. Total dependability can be attained by adding battery banks to hybrid systems. This paper aims to optimally design a multi-source grid isolated hybrid generation system for future smart cities in the state of Tamil Nadu, India. This off-grid power generation consists of wind turbine generators, photovoltaic panels, inverters, diesel generators, and batteries. The design intents at minimizing Net Present Cost, Unmet Load, and CO2 emissions which are conventionally contradictory to each other. This paper analyzes the optimal off-grid combination of components and control strategies, utilizing improved Hybrid Optimization using the Genetic Algorithm. The results obtained portrays that a mix of hybrid renewable energy generators at off-grid locations without diesel generators can be a cost-effective choice of new smart cities, and it is sustainable, environmentally viable, and techno-economically feasible. Subvention is imperative to take on the large-scale system due to the high capital cost. [ABSTRACT FROM AUTHOR]

Rivers are an integral part of the hydrological cycle and are the major geological agents which erode the continents and transport water and sediments to the oceans. Thus rivers act an important link between continents and oceans for the mass balance. Estimating the suspended sediment yield is one of the crucial aims in the field of managing, designing and planning of any river system or reservoir. To determine the suspended sediment yield in a river basin demands more labour or it is more costly when automatic sampling devices are used. The number of variables and the inter-relationship existing among them influence the suspended sediment yield and the nature of these inter-relationships is neither linear nor simple. Unfortunately, it is a difficult task to determine the suspended sediment yield when traditional mathematical models are used as complex variables and processes are involved. The major key factors, such as basin geology (rock type), relief, rainfall, temperature, water discharge and catchment area that affect sediment yield, are used as inputs to develop the model for predicting the suspended sediment yield in the Mahanadi River. In this paper, a multi-objective genetic algorithm for artificial neural network (MOGA-ANN)-based approach is used for predicting the suspended sediment yield. The MOGA assists ANN to minimize the two competing objectives, i.e. mean error and variance simultaneously. Thus in this study, a hybrid artificial intelligence-based method, MOGA-ANN model, is developed using the hydro-geological-climatic factors where all parameters associated with the ANN models are optimized simultaneously using MOGAs to estimate the suspended sediment yield in the Mahanadi River basin. The ANN's parameters are optimized globally by the MOGA to accurate estimation. The study has been carried out to develop MOGA-ANN for estimating the suspended sediment load using 20-year data at the Tikarapara gauging station which is the last downstream station in the Mahanadi River. The MOGA-ANN model provided the root mean square error 0.0281, correlation coefficient (r) 0.966 and efficiency factor 0.919 during the testing phase. The results suggested that the hybrid MOGA-ANN model exhibited satisfactory performance with an accuracy of 96.41%. [ABSTRACT FROM AUTHOR]

This paper reports the development of a public transport trip planner to help the urban traveller in planning and preparing for his commute using public transportation in the city. A Genetic Algorithm (GA) approach that handles realtime Global Positioning Systems (GPS) data from buses of the Metropolitan Transport Corporation (MTC) in Chennai City (India) has been used to develop the planner. The GA has been shown to provide good solutions within the problem's computation time constraints. The developed trip planner has been implemented for static network data first and subsequently extended to use real-time data. The "walk mode" and Chennai Mass Rapid Transit System (MRTS) have also been included in the geospatial database to extend the route-planner's capabilities. The algorithm has subsequently been segmented to speed up the prediction process. In addition, a temporal cache has also been introduced during implementation, to handle multiple queries generated simultaneously. The results showed that there is promise for scalability and citywide implementation for the proposed real-time route-planner. The uncertainty and poor service quality perceived with public transport bus services in India could potentially be mitigated by further developments in the route-planner introduced in this paper. [ABSTRACT FROM AUTHOR]

Internet made a drastic change in the way data are collected. There has been huge and huge collections of data. All these data serve no purpose unless some useful information is mined from it. Prediction of future instances will be major research problem. In this work, we adapt a rough and real coded genetic algorithm-based prediction system for prediction of future instances. We adapt rough set in this work because of uncertainties present in the data. Additionally, it is used to eliminate the unwanted attributes. Real coded genetic algorithm is used to predict the values for the unknown instances by making use of multiple linear regression. The model is experimented over agriculture data obtained from Tiruvannamalai district of Tamil Nadu. The experimental results show the viability of proposed research. [ABSTRACT FROM AUTHOR]

Context: Computer-aided diagnosis (CAD) combining mammographic features from cranio-caudal (CC) and medio-lateral-oblique (MLO) views improve the diagnostic performance of breast cancer. This could help doctors incorrect diagnosis at the earlier stage thereby reducing mortality.Aim: The aim of this study is to propose a breast cancer diagnostic technique for improving the diagnostic accuracy and reducing the false positive rate by fusing mammographic features from CC and MLO views.Settings and Design: The MLO and CC view mammograms of same patients must be used to extract k-Gabor features and then fused to form a single feature vector.Subjects and Methods: Mammograms from the digital database for screening mammography (DDSM) and INbreast datasets are collected. k-Gabor features extracted from both MLO and CC view mammograms are fused serially and reduced by principal component analysis (PCA) or genetic algorithm. The reduced features are classified using a multi-layer perceptron feed forward neural network with backpropagation learning algorithm.Statistical Analysis Used: Various relevant performance metrics such as accuracy, sensitivity, specificity, discriminant power, Mathews correlation coefficient (MCC), F1 score and Kappa are used to analyze the classification results.Results: The accuracy, sensitivity, specificity, discriminant power, MCC, F1 score, and Kappa obtained as 92.5%, 93%, 91.8%, 1.198, 0.845, 0.936, and 0.845, respectively, for DDSM. For INbreast, the above specified metrics are 87.5%, 90.9%, 85.7%, 0.980, 0.741, 0.833, and 0.734, respectively. The results show 4.4%, 4.3%, and 9.4% improvements in accuracy, sensitivity, and specificity, respectively, compared to the previous works.Conclusions: Detailed analysis of the results implies that the serial fusion of k-Gabor features extracted from MLO and CC views with PCA reduction in CAD significantly improves the diagnostic performance. [ABSTRACT FROM AUTHOR]

The present study focuses on optimal sizing of an integrated renewable energy (IRE) system with battery bank to meet the load demand of a cluster of four zones in Karnataka state of India. Hourly load demand has been simulated using HOMER software based on three scenarios - high investment with low (HILR) rating appliances, medium investment with moderate (MIMR) rating appliances, and low investment with high (LIHR) rating appliances. Simulation results of optimal size, total net present cost (TNPC) and cost of energy (COE) have been obtained using genetic algorithm (GA) considering different scenarios with and without DSM strategy by keeping energy index at 1. Results of three scenarios with DSM strategy were compared with those without DSM strategy. Based on the comparison of the results, scenario of HILR with DSM strategy is found to have minimum TNPC and COE for zone 1 and zone 2, while scenario of MIMR with DSM for zone 3 and LIHR with DSM for zone 4 are found to have least TNPC and COE. Further, obtained results of TNPC of GA have been validated by comparing PSO technique. It has been observed that, results of GA and PSO were found to be almost same. [ABSTRACT FROM AUTHOR]

This paper proposes a fuzzy interval optimization approach to solve the Environmental/ Economic Dispatch (EED) problem with uncertain parameters in the constraints and the objective functions. The objective functions considered are fuel cost and the gaseous emissions of the generating units. Two different types of fuel cost functions are considered in this study, namely, the conventional quadratic function and the augmented quadratic function to introduce more accurate modeling that incorporates the valve loading effects. The latter model presents non-differentiable and nonconvex regions that challenge most gradient-based optimization algorithms. In the proposed approach, objective functions are fuzzified and integrated to represent the fuzzy decision value. On the other hand, load uncertainties are modeled using fuzzy intervals. This fuzzy EED problem formulation provides a modeling flexibility, relaxation in constraints and allows the method to seek a practical solution. The obtained fuzzy multi-objective optimization problem is solved using non-dominated sorting genetic algorithm-II, known for its global searching capabilities, to get the best compromise among all the objectives. The performance of this solution is examined and applied to the standard IEEE 30-bus six-generator test system and the Indian power network of 82-bus by comparing its results with that of the existing methods. Different cases with different complexities have been considered in the study reported in this paper. [ABSTRACT FROM AUTHOR]

• Evaluated the performance of SWAT in reproducing daily-scale reservoir inflow. • HEC-ResSim derived reservoir outflow estimates are reliable for baseline scenario. • Proposed 10-daily scale E-flow estimation approach is highly reliable. • EPRC and SPRC scenarios based rule curves have more practical implications. • Proposed SWAT-HEC-ResSim-GA approach could effectively govern reservoir operation. The complex hydrological connectivity of integrated river-reservoir catchment command regions in conjunction with abrupt societal development and population growth impose adverse water availability scenario in the global reservoirs. In this context, to formulate optimal reservoir operation policies, numerous reservoir simulation and optimization models are currently on board. However, lack of combined hydrologic and hydraulic conceptualizations in the existing reservoir operation framework fetches considerable uncertainty; thereby leading to formulation of erroneous reservoir operation rule. This study proposed an integrated Soil and Water Assessment Tool (SWAT) based hydrologic, HEC-ResSim based hydraulic, and genetic algorithm (GA) based optimization framework (SWAT-HEC-ResSim-GA) for formulating the optimal reservoir operation rule of the Kangsabati reservoir located in the eastern India. Unlike the existing reservoir operation rules, the present study incorporates a 10-daily-scale environmental flow estimation approach to quantify the water demand. The proposed reservoir operation framework generated rule curve based HEC-ResSim simulation revealed improved reservoir performance with reservoir time reliability, volume reliability, and resiliency magnitudes of 0.631, 0.736, and 0.591, respectively. Finally, this study endorses to use the developed methodology for supply–demand management, and subsequent, reservoir operation in any global reservoir catchment-command regions with utmost accuracy. [ABSTRACT FROM AUTHOR]

Over the years, renewable energy based power generation has proven to be a cost-effective solution in stand-alone applications in the regions where grid extension is difficult. The present study focused on the development of models for optimal sizing of integrated renewable energy (IRE) system to satisfy the energy needs in different load sectors of four different zones considered in Chamarajanagar district of Karnataka state in India. The objective of the study is to minimize the total cost of generation and cost of energy using genetic algorithm (GA) based approach. Considering optimization power factor (OPF) and expected energy not supplied (EENS), optimum system feasibility has been investigated. Based on the study, it has been found that IRES is able to provide a feasible solution between 1.0 and 0.8 OPF values. However, power deficit occurs at OPF values less than 0.8 and the proposed model becomes infeasible under such conditions. Customer interruption cost (CIC) and deficit energy (DE) for all zones were also computed to quantify the reliability of the systems. [ABSTRACT FROM AUTHOR]

Renewable energy, a viable alternative of the world, can overcome the energy crisis that hinders growth of today's economies. The present work includes sizing of hybrid energy system using various energy management strategies. The energy management strategies considered in the present work are cycle charging strategy, load following strategy and peak shaving strategy. The sizing of hybrid energy system for all the mentioned strategies are carried out using genetic algorithm, particle swarm optimization and biogeography based optimization techniques by keeping energy index ratio at 1. The model also incorporates net present cost, cost of energy, renewable fraction and emissions of carbon di-oxide from diesel generator. From the analysis, it can be concluded that the 10th combination of cycle charging strategy is most cost effective as compared to other dispatch strategies. Seven unelectrified villages of Dhauladevi block of Almora district in Uttarakhand, India, has been selected for the development of hybrid energy system, comprising of renewable energy resources available locally along with the addition of diesel generator, for meeting the energy deficit. The results show that cycle charging strategy is most cost effective compared to load following and peak shaving strategies, with a minimum cost of energy value of 5.64 ₹/kWh. [ABSTRACT FROM AUTHOR]

In this study, a support vector machine (SVM)-based ensemble model was developed for reliability forecasting. The hyperparameters of the SVM were selected by applying a genetic algorithm. Input variables of the SVM model were selected by maximizing the mean entropy value. The diverse members of the ensemble model were obtained by a k-means clustering algorithm, and one ensemble member was selected from each cluster by choosing the closest from the cluster center. The optimum cluster number was selected using the Davies-Bouldin index. The developed model was validated by a benchmark turbocharger data set. A comparative study reveals that the proposed method performs better than existing methods on benchmark data sets. A case study was conducted investigating a dumper operated at a coal mine in India. Time-to-failure historical data for the dumper were collected, and cumulative time to failure was calculated for reliability forecasting. Study results demonstrate that the developed model performs well with high accuracy (R² = 0.97) in the prediction of dumper failure, and a comparison with other methods demonstrates the superiority of the proposed ensemble SVM model. [ABSTRACT FROM AUTHOR]

In recent years, renewable energy can be seen as one of the important prospect of today's research, as it is likely to enlighten the lives of millions of people by fulfilling demand of electricity in their daily life. The present work focuses on the development of optimal hybrid energy system sizing model based on comparative analysis of particle swarm optimization, genetic algorithm and Homer software for energy index ratio of 1. The model also incorporates renewable fraction, emissions of carbon di oxide from diesel generator, net present cost and cost of energy. The system is developed to supply the demand of 7 un-electrified villages of Dhauladevi block of Almora district in Uttarakhand, India with the help of the available resources of solar, hydro, biomass and biogas energy along with the addition of diesel generator, for meeting out the energy deficit. From the optimization results, minimum cost of energy and maximum renewable fraction are obtained as 5.77 Rs/kWh and 92.6% respectively. [ABSTRACT FROM AUTHOR]

Highlights: [•] Optimal Electricity Generation Portfolio for India’s 12th Five Year Plan. [•] Electricity Portfolio Optimization for Climate Change Mitigation. [•] Intelligent Pareto-search Genetic Algorithm (IPGA) for portfolio optimization. [•] Comprehensive risk-barrier index (CRBI) for energy planning in developing countries. [•] Barriers more significant than risks for nuclear and hydro power projects. [Copyright &y& Elsevier]

Optimal planning of renewable energy generator (REG) units helps to meet future power demand with improved flexibility. Hence, this paper proposes a grid-oriented genetic algorithm (GOGA) based on a hybrid combination of a genetic algorithm (GA) and a solution using analytical power flow equations for optimal sizing and placement of REG units in a power system network. The objective of the GOGA is system loss minimization and flexibility improvement. The objective function expresses the system losses as a function of the power generated by different generators, using the Kron equation. A flexibility index (FI) is proposed to evaluate the improvement in the flexibility, based on the voltage deviations and system losses. A power flow run is performed after placement of REGs at various buses of the test system, and system losses are computed, which are considered as chromosome fitness values. The GOGA searches for the lowest value of the fitness function by changing the location of REG units. Crossover, mutation, and replacement operators are used by the GOGA to generate new chromosomes until the optimal solution is obtained in terms of size and location of REGs. A study is performed on a part of the practical transmission network of Rajasthan Rajya Vidyut Prasaran Nigam Ltd. (RVPN), India for the base year 2021 and the projected year 2031. Load forecasting for the 10-year time horizon is computed using a linear fit mathematical model. A cost–benefit analysis is performed, and it is established that the proposed GOGA provides a financially viable solution with improved flexibility. It is established that GOGA ensures high convergence speed and good solution accuracy. Further, the performance of the GOGA is superior compared to a conventional GA. [ABSTRACT FROM AUTHOR]

Using JTWC (Joint Typhoon Warning Center) best track analysis data for the Indian Ocean cyclones, we developed an empirical equation for prediction of maximum surface wind speed of tropical cyclones during first 6-12 hours of landfall along the coastline of Indian subcontinent. A non-linear data fitting approach, the Genetic Algorithm, has been used to develop the above empirical equation using data for 74 tropical cyclones that made landfall on the coasts of India, Bangladesh and Myanmar during the period 1978-2011. For an out of sample validation test, the mean absolute error of the prediction was found to be 5.2 kt, and a correlation of 0.97. Our analysis indicates that time-integration of land area intercepted by cyclones during the landfall is a better predictor of post-landfall intensity compared to post-landfall time span. This approach also helps to tackle the complexity of coastline geometry of Indian subcontinent area. [ABSTRACT FROM AUTHOR]

In this paper, we propose elitist genetic algorithms-based artificial neural network (ANN) model for setting up an early warning system for occurrence of high inflation. The proposed warning system uses values of an appropriate set of economic fundamental variables as input and builds an ANN model for quantifying the possibility of high inflation within a fixed period of time window. Elitism-based generational genetic algorithm is used for optimizing the architecture of the ANN model. We empirically evaluate the proposed neuro-genetic approach to identify the class of leading economic indicators and build an early warning signalling system of an occurrence of high inflation (overall and component inflations) using the data from the Indian economy. We further compare the results of the proposed approach with the commonly used data-driven signals approach. In the empirical studies, we observe promising performance of the proposed neuro-genetic warning system, which is capable of generating accurate early warning signals of an impending high inflation. [ABSTRACT FROM AUTHOR]

For efficient irrigation water management and hydro-meteorological studies at both the field and catchment scales, a need exists to evaluate the existing evapotranspiration (ET) estimation methods under varied physiographical and data availability conditions. In this study, using the FAO-56 Penman-Monteith model as the benchmark model, a total of 16 various ET estimation methods were evaluated using both the continuous daily time series and average time series weather data of a subhumid valley cultivated rangeland in the eastern Indian Himalayas. The results revealed that some pan evaporation models have restricted performances. Consequently, for an improved performance, all of these models were standardized using a genetic algorithm-based linear corrector transformation model. Almost all of the models performed reasonably well when using the average daily time series data. Furthermore, the trend analysis of reference ET showed an increasing trend during the months of February to July, including October, and a decreasing trend during August to January, except October, with an annual decreasing rate of . [ABSTRACT FROM AUTHOR]

Map-matching algorithms integrate data from positioning sensors with a digital map in order, first, to identify the road link on which a vehicle is traveling, and second, to determine the vehicle's location on that link. Due to errors in positioning sensors, digital maps, and the map-matching (MM) process, MM algorithms sometimes fail to identify the correct road segment from the candidate segments. This phenomenon is known as mismatching. Identification of the wrong road link may mislead users and degrade the performance of a location-based intelligent transportation system (ITS) and services. The main objective of this article is to improve a topological map-matching (tMM) algorithm by error detection, correction, and performance re-evaluation. Errors in a tMM algorithm were determined using data comprising 62,887 positioning points collected in three different countries (the United Kingdom, the United States, and India). After map matching, each mismatched case was examined to identify the primary causes of the mismatches. A number of strategies were developed and applied to reduce the risk of mismatching thus enhancing the tMM algorithm. An independent data set of 5,256 positioning points collected in and around Nottingham, UK, was employed to re-evaluate the performance of the enhanced tMM algorithm. The original tMM algorithm correctly identified the vehicle's position 96.5% of the time; after enhancement this increased to 97.8%. This compares very well with the performance of tMM algorithms reported in the literature. The enhanced tMM algorithm developed in this research is simple, fast, efficient, and easy to implement. Since the accuracy offered by the enhanced algorithm is found to be high, the developed algorithm has potential to be implemented in real-time location-based ITS applications. [ABSTRACT FROM AUTHOR]

Integrated production-distribution plan considering three major objectives, viz., total cost minimization, change in labor level reduction, and underutilization minimization, is developed for a renowned bearing manufacturing industry in India. The total cost minimization objective minimizes the regular, overtime, and outsourced production costs along with inventory holding, backorder, hiring/laying-off, and trip-wise distribution costs. The multi-criteria model is solved using a novel simulation-based analytic hierarchy process (AHP)-discrete particle swarm optimization (DPSO) algorithm. The solutions of the AHP-DPSO algorithm are verified using the AHP-binary-coded genetic algorithm solutions. The proposed simulation-based AHP-DPSO solutions are found to be superior. Demand is assumed to vary uniformly, and the simulation-based AHP-DPSO algorithm is used for obtaining the best production-distribution plan that serves as a trade-off between holding inventory and backordering products. In addition to bearing manufacturing industry dataset, two other test datasets are also solved. [ABSTRACT FROM AUTHOR]

Richards nonlinear growth model, which is a generalization of the well-known logistic and Gompertz models, generally provides a realistic description of many phenomena. However, this model is very rarely used as it is extremely difficult to fit it by employing nonlinear estimation procedures. To this end, utility of using a very powerful optimization technique of genetic algorithm is advocated. Parametric bootstrap methodology is then used to obtain standard errors of the estimates. Subsequently, bootstrap confidence-intervals are constructed by two methods, viz. the Percentile method, and Bias-corrected and accelerated method. The methodology is illustrated by applying it to India's total annual foodgrain production time-series data. [ABSTRACT FROM AUTHOR]

The effect of data pre-processing while developing artificial intelligence (AI) -based data-driven techniques, such as artificial neural networks (ANN), model trees (MT) and linear genetic programming (LGP), is studied for Pawana Reservoir in Maharashtra, India. The daily one-step-ahead inflow forecasts are compared with flows generated from a univariate autoregressive integrated moving average (ARIMA) model. For the full-year data series, a large error is found mainly due to the occurrence of zero values, since the reservoir is located in an intermittent river. Hence, all the techniques are evaluated using two data series: 18 years of daily full-year inflow data (from 1 January to 31 December); and 18 years of daily monsoon season inflow data (from 1 June to 31 October) to take into account the intermittent nature of the data. The relevant range of inputs for each category is selected based on autocorrelation and partial autocorrelation analyses of the inflow series. Conventional pre-processing methods, such as transformation and/or normalization of data, do not perform well because of the large variation in magnitudes, as well as the many zero values (65% of the full-year data series). Therefore, the input data are pre-processed into un-weighted moving average (MA) series of 3 days, 5 days and 7 days. The 3-day MA series performs better, maintaining the peak inflow pattern as in the actual data series, while the coarser-scale (5-day and 7-day) MA series reduce the peak inflow pattern, leading to more errors in peak inflow prediction. The results indicate that AI methods are powerful tools for modelling the daily flow time series with appropriate data pre-processing, in spite of the presence of many zero values. The time-lagged recurrent network (TLRN) ANN modelling technique applied in this study maps the inflow forecasting in a better way than the standard multilayer perceptron (MLP) neural networks, especially in the case of the seasonal data series. The MT technique performs equally well for low and medium inflows, but fails to predict the peak inflows. However, LGP outperforms the other AI models, and also the ARIMA model, for all inflow magnitudes. In the LGP model, the daily full-year data series with more zero inflow values performs better than the daily seasonal models. Citation Jothiprakash, V. & Kote, A. S. (2011) Improving the performance of data-driven techniques through data pre-processing for modelling daily reservoir inflow. Hydrol. Sci. J. 56(1), 168-186. [ABSTRACT FROM AUTHOR]

The accurate prediction of groundwater level is important for the efficient use and management of groundwater resources, particularly in sub-humid regions where water surplus in monsoon season and water scarcity in non-monsoon season is a common phenomenon. In this paper, an attempt has been made to develop a hybrid neural model (ANN-GA) employing an artificial neural network (ANN) model in conjunction with famous optimization strategy called genetic algorithms (GA) for accurate prediction of groundwater levels in the lower Mahanadi river basin of Orissa State, India. Three types of functionally different algorithm-based ANN models (viz. back-propagation (GDX), Levenberg-Marquardt (LM) and Bayesian regularization (BR)) were used to compare the strength of proposed hybrid model in the efficient prediction of groundwater fluctuations. The ANN-GA hybrid modeling was carried out with lead-time of 1 week and study mainly aimed at November and January months of a year. Overall, simulation results suggest that the Bayesian regularization model is the most efficient of the ANN models tested for the study period. However, a strong correlation between the observed and predicted groundwater levels was observed for all the models. The results reveal that the hybrid GA-based ANN algorithm is able to produce better accuracy and performance in medium and high groundwater level predictions compared to conventional ANN techniques including Bayesian regularization model. Furthermore, the study shows that hybrid neural models can offer significant implications for improving groundwater management and water supply planning in semi-arid areas where aquifer information is not available. [ABSTRACT FROM AUTHOR]

Accurate forecasting of streamflow is essential for the efficient operation of water resources systems. The streamflow process is complex and highly nonlinear. Therefore, researchers try to devise alterative techniques to forecast streamflow with relative ease and reasonable accuracy, although traditional deterministic and conceptual models are available. The present work uses three data-driven techniques, namely artificial neural networks (ANN), genetic programming (GP) and model trees (MT) to forecast river flow one day in advance at two stations in the Narmada catchment of India, and the results are compared. All the models performed reasonably well as far as accuracy of prediction is concerned. It was found that the ANN and MT techniques performed almost equally well, but GP performed better than both these techniques, although only marginally in terms of prediction accuracy in normal and extreme events. Citation Londhe, S. & Charhate, S. (2010) Comparison of data-driven modelling techniques for river flow forecasting. Hydrol. Sci. J. 55(7), 1163-1174. [ABSTRACT FROM AUTHOR]

The highly pathogenic avian influenza (HPAI) type H5N1 has become a menace in the recent years, causing huge economic losses to the poultry industry. However, a proper understanding of the factors governing its transmission cycle and possible outbreaks in space and time is lacking. The ecological niche modelling tool, Genetic Algorithm for Rule-Set Prediction (GARP) has been used to model the 'black box' of H5N1 transmission in the Indian subcontinent. Ninety outbreak records for veterinary cases of H5N1 in West Bengal and Bangladesh, and 18 environmental parameters with a pixel resolution of ≈4 km were used in the modelling. The final optimized rules generated by GARP were projected on a broad geographical scale covering India, Bangladesh, Nepal and many parts of Pakistan to anticipate the potential areas for the presence of H5N1. The niche model predicted vast areas in the Indo-Bangladesh region for the presence of H5N1. The areas having high level of model agreements are in the Gangetic Plains, Assam, Tripura, West Bengal and some parts of Andhra Pradesh, Maharashtra and Gujarat. The model also predicted large areas in northern Pakistan and some parts of Nepal to be suitable for H5N1 transmission. The modelled ecological niche of H5N1 in the present study is characterized by a mean annual temperature range of 21-26°C, lower slope angle, areas with a tendency to pool water, a significant monthly variation in the degree of greenness and human population density of more than 100 persons/km2. The resultant niche model validated 49 cases of HPAI outbreak out of the total 52 past as well as the latest outbreaks in India, Bangladesh, Pakistan and Nepal. [ABSTRACT FROM AUTHOR]

Abstract: This paper is concerned with the Indian design of a 220MWe Pressurized Heavy Water Reactor having Natural Uranium fuel and heavy water as moderator and coolant. At the beginning of life, it is necessary to flatten the power by loading some Thorium bundles to achieve a nearly full power operation. The determination of best possible locations of Thorium bundles, which maximize fuel economy as well as safety, is a complex combinatorial optimization problem. About two decades ago, an optimum configuration of Thorium bundles was successfully arrived at by using a gradient based method and this pattern was actually loaded in the Indian PHWR at Kakrapar which went critical in 1992 [Balakrishnan, K., Kakodkar, A., 1994. Optimization of the initial fuel loading of the Indian PHWR with Thorium bundles for achieving full power. Annals of Nuclear Energy 21, 1–9]. Here, the same problem is revisited for two reasons. Firstly, computational techniques based on completely different philosophy namely “Genetic Algorithm” (GA) and “Estimation of Distribution Algorithm” (EDA) have been used. Secondly, the enormous increase in computing power during the last two decades is expected to provide a more exhaustive search. Indeed, it has been possible to find out many feasible Thorium configurations of comparable merit. Our results are similar with the result of the earlier BARC study but provide a range of additional configurations. As in earlier BARC work, we find that one can get from 95% to 97% full power without violating various safety aspects such as maximum bundle power, maximum channel power, channel outlet temperature and worth of the two shutdown systems. In the present work, the number of Thorium bundles which can be loaded range from 22 to 34. One of the outcomes of this study is that the computational techniques suitable for this type of problems have been identified and developed. Further studies involving the use of some other evolutionary methods and problems such as optimization of depleted Uranium loading are in progress. [Copyright &y& Elsevier]

A genetic algorithm (GA) is introduced to fix ranges of parameters, in a quantitative manner, pertaining to forecasting the genesis of severe thunderstorms in the pre-monsoon season over Calcutta (22°32′, 88°20′) in the northeastern part of India (20°–24°N latitude, 85°–93°E longitude). The results depict that definite quantitative ranges of parameters can be made available before as well as after the occurrence of severe thunderstorms using a GA. Thus, a suitable combination of these ranges of parameters before the onset can represent a possible predictor of thunderstorm genesis. [ABSTRACT FROM AUTHOR]

Tropical cyclone prediction is essential to limit death toll and damage caused by them. In this paper, a model has been formulated to classify the cyclone as no cyclone, minimal, moderate, extensive, extreme and catastrophic in view of attributes such as Wind Speed(10 m), Rainfall, Wind Direction(10 m), Sea Surface Temperature(2 m), Sea Level Pressure(2 m) and Relative humidity(2 m). The models have been trained using the meteorological data from MERRA-2 Web service which provides time-series data with spatial resolution of approximately 50 km. The cyclone data is taken from RSMC — New Delhi for Tropical cyclone disturbances over Indian Oceans. Initially, the models are trained using deep learning networks like MLP, LSTM, GRU, RNN, BI-LSTM and CNN. Since CNN gives better results, the CNN model is chosen for further analysis. The hyper parameters of the CNN model are optimized using genetic algorithm. The values drawn from genetic algorithm appear to be promising than the values which were chosen manually in random. The model is then modified by removing the fully connected layer which operates as a classifier in CNN network. The conventional machine learning classifiers like — Decision Tree, K-Nearest neighbor, logistic regression, Naive Bayes, Random Forest, SVM and XGBoost are used as a classifier in the place of a fully connected layer in CNN. Further to increase the prediction accuracy, C4.5 Decision tree algorithm is modified to be used as a classifier in CNN. Classification is performed by considering the Spatio-temporal data of various important cities in India. The model was tested to classify the category for 5 different cyclones and was also compared with Saffir–Simpson's scale to validate the correctness of the model. The proposed model gives a better performance compared to the conventional machine learning and deep learning classifiers in terms of time complexity, accuracy, precision and recall and this can supplement the cyclone prediction process of current NWP models. • Cyclone Prediction is proposed using CNN, modified C4.5 and genetic algorithms. • CNN is used for feature extraction and genetic algorithm for hyper parameter tuning. • Modified C4.5 is proposed to solve the limitations of C4.5 decision tree algorithm. • Accuracy of the proposed model is significantly improved compared to the existing works. [ABSTRACT FROM AUTHOR]

• A multi-period multi-objective portfolio optimization model is developed. • Coherent fuzzy numbers are used to model the returns and the investor attitude. • Credibilistic optimization model is solved through a real-coded genetic algorithm. • The models' performance is tested using real-time return data of assets. In this paper, we use an extension of fuzzy numbers, called coherent fuzzy numbers, to model asset returns and an investor's perception of the stock market (pessimistic, optimistic, or neutral) simultaneously. Two multi-period multi-objective portfolio optimization models are formulated using mean absolute semi-deviation and Conditional Value-at-Risk (CVaR) as risk measures, respectively. We aim to provide more flexibility to the investor in specifying the risk tolerance and devise optimum investment plans for different investment horizons. The proposed models also incorporate bound, cardinality, and skewness constraints for each investment period to capture various stock market scenarios. A real-coded genetic algorithm is employed to solve the resultant models. Two real-life case studies involving 20 assets of the National Stock Exchange (NSE), India, and another involving 50 assets listed in the S&P 500 and NASDAQ-100 indexes have been provided to illustrate the efficacy and advantages of the models. An in-sample and out-of-sample analysis have been done for both the models to analyze the performance in the real-world scenario. The conclusion drawn from the analysis strongly emphasizes on accurately assessing the current stock market prospects, i.e., adopting the right attitude (pessimistic, optimistic, or neutral), is of paramount importance and must be included in the portfolio optimization problem. [ABSTRACT FROM AUTHOR]

• It is to estimate river discharges solely using satellite images only without ground data. • Monitoring upstream discharges using this approach will help in flood early warning. • Flows in ungauged and in transboundary rivers can be monitored. Many countries experiences floods due to heavy flows carried by trans -boundary Rivers. Hydrological modelling in un-gauged catchments and trans -boundary Rivers has become a challenging task for hydrological modellers. This research aims at estimating river discharges computed solely using satellite images, with no ground-based or a prior information at all. It is also aimed at testing the applicability of approach in very well defined channel to braided channels and in very wide range of discharge conditions. The approach works owing to principle of hydraulic geometry of natural rivers. Monitoring these discharges in upstream areas will help in forecasting flows in the downstream areas. In this paper, an attempt is made to estimate the river discharge using river flow width information derived from temporal satellite remote sensing data alone. The approach is based on Many Stations Hydraulic Geometry (AMHG) principle through Genetic Algorithm Technique. The technique is demonstrated in four major rivers in India for various discharge values using temporal remote sensing data of Indian Remote Sensing Satellites (IRS) and LANDSAT. Computed discharges are validated with observed discharges in all the identified river reaches using statistical method and found to have very good match. This novel approach is useful globally for quantifying river discharges with continuous imaging of the rivers and has strong advantage in flood early warning and water resources assessment. [ABSTRACT FROM AUTHOR]

When multiple thermal retrofits are to be installed in a building envelope for improving its energy performance, several questions arise such as "what should be the thickness of retrofits and where they should be placed within the wall/roof", "which retrofit should be installed towards the exterior and which one should be installed towards the interior of the envelope". Such judgements are made in contemporary studies by comparing limited predefined configurations where either the thickness or the location of retrofit assumes only a few discrete values within the envelope. The novel approach proposed in this study utilizes spatial discretization of structural layers in a composite envelope for two fold benefit. A new version of Genetic Algorithm (GA) is developed for this purpose by modifying its key operational stages. The GA is implemented in a practical scenario to optimally configure a multi-retrofit envelope (carrying phase change material and thermal insulator) of a common residential building in hot climate of India. Analysis of a single housing unit demonstrates that up to 33.5% of heat gain reduction and 9.2 kWh/day of electricity saving are achievable with improved envelope design. • Comprehensive guideline for designing multi-retrofit building envelope is presented. • All possible sizes and locations of thermal retrofits can be checked simultaneously. • Thermal performance assessment of multiple combinations of PCMs and insulators. • A multifaceted Genetic Algorithm is modelled to drive the optimization. • Optimized envelope resists up to 33.5% of diurnal heat gain in hot Indian climate. [ABSTRACT FROM AUTHOR]

Techno-economic, social, and environmental factors influence a large part of society, predominantly in developing countries. Due to energy poverty and bloating populations, developing countries like India are striving to meet the energy balance. One initiative of India to achieve the country's Renewable Energy Target (RET) is the setting up of the National Solar Mission (NSM) to meet a target of 175 GW (non-hydro) by the year 2022. Prioritizing Renewable Energy (RE) utilization to achieve techno-economic balance is India's primary objective and creating a positive environmental impact is a bonus. In this study, various scenarios are explored by investigating the techno-economic and environmental impact on RE adoption for a small community in India by optimally sizing the Hybrid Renewable Energy System (HRES). This study is an exemplar in understanding and exploring RE utilization, whilst examining the recent RE market in depth and exploring the advantages and disadvantages of the current RE situation by initiating it in a smaller community. Improved Hybrid Optimization using Genetic Algorithm (iHOGA) PRO+ software, (Version 2.4 -Pro+, Created by Dr Rodolfo Dufo López, University Zaragoza (Spain)) is used to size the RE systems. The results are categorized using triple bottom line analysis (TBL analysis) and for different scenarios, the techno-economic, environmental, and social merits are weighed upon. The probable hurdles that India has to surpass to achieve easy RE adoption are also discussed in this work. The influential merits for analyzing the TBL for a real-time scenario are Net Present Cost (NPC), Carbon-di-oxide (CO2) emissions, and job criteria. Compared to Hybrid Optimization of Multiple Energy Resources (HOMER) software, iHOGA remains less explored in the literature, specifically for the grid-connected systems. The current study provides a feasibility analysis of grid-connected RE systems for the desired location. iHOGA software simulated 15 sets of results for different values of loads considered and various acquisition costs of HRES. At least 70% of RE can be penetrated for the Aralvaimozhi community with the lowest value of NPC of the HRES. From the TBL analysis conducted, integrating HRES into a micro-grid for the community would result in mitigating CO2 emissions and provide job opportunities to the local community; although, the economic impact should be minimized if the acquisition costs of the HRES are reduced, as has been established through this study. [ABSTRACT FROM AUTHOR]