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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]

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]

Artificial neural networks (ANNs) in conjugation with genetic algorithms (GAs) have been demonstrated to be an effective tool for system modelling and optimization in a variety of applications. The current communique is about assessing the capacity of ANN to predict investment on cattle till age at first calving (AFC) and milk production based on the data of 340 Vrindavani crossbreed cattle developed at the ICAR-Indian Veterinary Research Institute in Izatnagar, India. Three distinct artificial neural network (ANN) algorithms, namely, Levenberg–Marquardt (LM), Bayesian regularization (BR), and gradient descent momentum with adaptive learning rate backpropagation (GDX) were used to train the ANN infrastructure for determining milk production and investment based on body weight and AFC as input variables. The results showed that BR with 2 hidden layer neurons showed excellent prediction ability (R2 = 0.999, MSE < 10−6) and was therefore used as an objective function by GA for optimization. The optimized results revealed that higher milk production is achievable at lower investment if the age at first calving is 768 days with a body weight of ∼281 kg. The information generated by this investigation will aid in ensuring food security in terms of higher milk production while making the dairy business more sustainable and profitable for the farmers. [ABSTRACT FROM AUTHOR]

Forecasting of sediment load (SL) is essential for reservoir operations, design of water resource structures, risk management, water resource planning and for preventing natural disasters in the river basin systems. Direct measurement of SL is difficult, labour intensive, and expensive. The development of an accurate and reliable model for forecasting the SL is required. Sediment transport is highly non-linear and is influenced by a variety of factors. Forecasting of the SL using various conventional methods is not highly accurate because of the association of various complex phenomena. In this study, major key factors such as rock type (RT), relief (R), rainfall (RF), water discharge (WD), temperature (T), catchment area (CA), and SL are recognized in developing the one-step-ahead SL forecasting model in the Mahanadi River (MR), which is among India's largest rivers. Artificial neural networks (ANN) in conjunction with multi-objective genetic algorithm (ANN-MOGA)-based forecasting models were developed for forecasting the SL in the MR. The ANN-MOGA model was employed to optimize the two competing objective functions (bias and error variance) with simultaneous optimization of all associated ANN parameters. The performances of the proposed novel model were finally compared to other existing methods to verify the forecasting capability of the model. The ANN-MOGA model improved the performance by 12.81% and 10.19% compared to traditional AR and MAR regression models, respectively. The results suggested that hybrid ANN-MOGA models outperform traditional autoregressive and multivariate autoregressive forecasting models. Overall, hybrid ANN-MOGA intelligent techniques are recommended for the forecasting of SL in rivers because of their relatively better performance as compared to other existing models and simplicity of application. [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]

Rivers are the agents on earth and act as the main pathways for transporting the continental weathered materials into the sea. The estimation of suspended sediment yield (SSY) is important in the design, planning and management of water resources. The SSY depends on many factors and their interrelationships, which are very nonlinear and complex. The traditional approaches are unable to solve these complex nonlear processes of SSY. Thus, the development of a reliable and accurate model for estimating the SSY is essential. The goal of this research was to develop a single hybrid artificial intelligence model, which is a hybridization of the artificial neural network (ANN) and genetic algorithm (GA) (ANN-GA) for the estimation of SSY in the Mahanadi River (MR), India, by combining data from 11-gauge stations into a single hybrid generalized model and applying it to every gauging station for estimating the SSY. All parameters of the ANN model were optimized automatically and simultaneously using GA to estimate the SSY. The proposed model was developed considering the temporal monthly hydro-climatic data, such as temperature (T), rainfall (RF), water discharge (Q) and SSY and spatial data, including the rock type (RT), catchment area (CA) and relief (R), of all 11 gauging stations in the MR. The performances of the conventional sediment rating curve (SRC), ANN and multiple linear regression (MLR) were compared with the hybrid ANN-GA model. It was noticed that the ANN-GA model provided with greatest coefficient of correlation (0.8710) and lowest root mean square error (0.0088) values among all comparative SRC, ANN and MLR. Thus, the proposed ANN-GA is most appropriate model compared to other examined models for estimating SSY in the MR Basin, India, particularly at the Tikarapara measuring station. If no measures of SSY are available in the MR, then the modelling approach could be used to estimate SSY at ungauged or gauge stations in the MR Basin. [ABSTRACT FROM AUTHOR]

In India, the banks have a formidable edge in maintaining their customer retention ratio for past few decades. Downfall makes the private banks to reduce their operations and the nationalised banks merge with other banks. The researchers have used the traditional and ensemble algorithms with relevant feature engineering techniques to better classify the customers. The proposed algorithm uses a Meta classifier instead of an ensemble algorithm with an adaptive genetic algorithm for feature selection. Churn prediction is the number of customers who wants to terminate their services in the banking sector. The model considers twelve attributes like credit score, geography, gender, age, etc, to predict customer churn. The project consists of five modules as follows. First is the pre-processing module that identifies the missing data and fills the value with mean and mode. Second is the data transformation module where, the categorical data is converted into numerical data using label encoding to fasten the computations. The converted numerical data is normalized using the standard scalar technique. The feature selection module identifies the essential attributes using DragonFly and Firefly (Hybrid Fly) algorithms. The classification module designs an intelligent Meta learner, which combines the Ensemble Algorithm Extreme Gradient Boosting (XGBOOST) with base classifiers as "Extra Tree Classifier" and "Logistic Regression" to predict the churn customers. [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]

Autoregressive Integrated Moving Average (ARIMA) and seasonal ARIMA (SARIMA) models are common techniques that are widely used in analysing and forecasting stationary and seasonal time series data. The three essential steps involved to construct ARIMA are identification, estimation, and checking the validity of the model. The most critical step followed in constructing the ARIMA model is model identification. However, overcoming the difficult local optima problem for both ARIMA and SARIMA is still challenging as there is no appropriate method to solve it. In this paper, the proposed parallel GA-SARIMA model is used to solve the problem of local optima, where the genetic algorithm (GA) is used at the initial stage to identify the order and estimation of the parameters for SARIMA. The National Climate Data Centre (NCDC) time series dataset is used for testing the efficiency of the final parallel GA-SARIMA model to forecast the mean temperature of India from 2000 to 2017. The GA algorithm is successfully implemented to solve the optimization problems by introducing better solutions suitable for SARIMA models. The results of the study showed that the implementation of the combined approach of parallel GA and SARIMA enhances the prediction accuracy of the model. The parallel GA-SARIMA method is particularly robust, faster and performs better than sequential SARIMA models in terms of running time and cost function values. [ABSTRACT FROM AUTHOR]

Wind Farm Layout Optimization Problem (WFLOP) is a critical issue when installing a large wind farm. Many studies have focused on the WFLOP but only for a limited number of turbines and idealized wind speed distributions. In this study, we apply the Genetic Algorithm (GA) to solve the WFLOP for large hypothetical offshore wind farms using real wind data. GA mimics the natural selection process observed in nature, which is the survival of the fittest. The study site is the Palk Strait, located between India and Sri Lanka. This site is a potential hotspot of offshore wind in India. A modified Jensen wake model is used to calculate the wake losses. GA is used to produce optimal layouts for four different wind farms at the specified site. We use two different optimization approaches: one where the number of turbines is kept the same as the thumb rule layout and another where the number of turbines is allowed to vary. The results show that layout optimization leads to large improvements in power generation (up to 28 %), efficiency (up to 34 %), and cost (up to 25 %) compared to the thumb rule due to the reduction in wake losses. Optimized layouts where both the number and locations of turbines are allowed to vary produce better results in terms of efficiency and cost but also leads to lower installed capacity and power generation. Wind energy is growing at an unprecedented rate in India. Easily accessible terrestrial wind resources are almost saturated, and offshore wind is the new frontier. This study can play an important role while taking the first steps towards the expansion of offshore wind in India. [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]

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]

Chhana cake, locally termed chhana podo, is a baked traditional dairy product of India. The present study was undertaken for optimization of process parameters pertaining to production of chhana podo. Independent variables, namely, moisture content of feed-mix: 52.5 - 62.5% (wb), baking temperature: 60 - 180°C, baking time: 1 - 9 h and height of feed-mix: 1 - 5 cm were selected heuristically and their effect on dependent variables, namely, hardness, whiteness index, yellowness index, tint of crust and crumb, moisture content and expansion ratio of chhana podo were studied. Although quadratic models fitted to responses exhibited relative deviation percent (Rd) ranging from 1.214 to 5.406%; lack of fit was significant for all responses except crust yellowness index and crust tint. Neural network modeling was adopted (Rd for training = 1.739%, Rd for validation = 1.845%) and relative importance of factors on responses were found. Optimum conditions obtained from genetic algorithm were: moisture content of feed-mix = 57.43% (wb), baking temperature = 151.4°C, baking time = 4.35 h, height of mix = 2.9 cm. [ABSTRACT FROM AUTHOR]

In this paper, the authors have suggested and implemented the defined soft computing methods in air quality classification with case studies. The first study relates to the application of Fuzzy C mean (FCM) clustering method in estimating pollution status in cities of Maharashtra State, India. In this study, the computation of weighting factor using a new concept of reference group is successfully demonstrated. The authors have also investigated the efficacy of fuzzy set theoretic approach in combination with genetic algorithm in straightway describing air quality in linguistic terms with linguistic degree of certainty attached to each description using Zadeh-Deshpande (ZD) approach. Two metropolitan cities viz., Mumbai in India and New York in the USA are identified for the assessment of the pollution status due to their somewhat similar geographical features. The case studies infer that the fuzzy sets drawn on the basis of expert knowledge base for the criteria pollutants are not much different from those obtained using genetic algorithm. Pollution forecast using various methods including fuzzy time series forms an integral part of the paper. [ABSTRACT FROM AUTHOR]

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]

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]

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]

With deregulation and open access of the electricity supply industry occurring internationally, there is an increasing need to develop power market oriented optimal power flow (OPF) algorithm for power systems of developing countries. A genetic algorithm based OPF is proposed for this purpose, which inherits both the real and reactive power constraints with loss expressions involving B-coefficients for active power and C-coefficients for reactive power. The significance of the paper is the development of a flexible OPF methodology using both real and reactive power constraints, which can address a very important issue like determination of the spot price for each participant generator in the economic operation of restructured power systems in the developing countries. The proposed methodology has been tested in a 203 buses 267 lines 23 machines real power system of eastern part of India and the simulation results have established that the proposed method is promising for large scale optimizations under deregulated environments. [ABSTRACT FROM AUTHOR]

Abstract: Seed planting equipment with inclined plate seed metering devices is the most commonly used equipment for planting of peanut crop in India. For obtaining the high yield, it is very essential to drop the peanut seeds in rows maintaining accurate seed rate and seed spacing with minimum damage to seeds during metering. This mainly depends on the forward speed of the planting equipment, rotary speed of the metering plate and the area of cells on the plate. The relationship between these factors and the performance parameters viz., seed rate, seed spacing and percent seed damage can be established using regression analysis. But they may not be very accurate and consistent throughout the solution space. Hence, an attempt has been made in this paper to develop the feed forward artificial neural network (ANN) models for the prediction of the performance parameters of an inclined plate seed metering device. The data were generated in the laboratory by conducting experiments on a sticky belt test stand provided with a seed metering device and an opto-electronic seed counter. The generated data was used to develop both statistical and neural network models. The optimum architecture of the neural network models was determined using genetic algorithm (GA) as a single objective constrained optimization problem. The performance of the developed models was compared among themselves for 4 randomly generated test cases. The results show that the ANN model predicted the performance parameters of the seed metering device better than the statistical models. It is possible to determine the optimum levels of the input parameters to obtain the desired performance parameters of the seed metering device by performing reverse mapping of the developed ANN models. [Copyright &y& Elsevier]

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]

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]

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]

• 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]

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]