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India being one of the major producers of fish contributes 5.5 percent of global fish production and ranks second in the world after China. The production of aquaculture mainly depends on the quality of land selected for aqua farming. Neural Network algorithms have been applied to classify the aquaculture sites based on 6 input variables viz., water, soil, support, infrastructure, input and risk factor. An artificial neural network (ANN) consists of huge number of interconnected elements called neurons that work together to solve a specific problem. An Artificial Neural network can be used for classification, prediction, pattern recognition etc., through a learning process. In this paper, the models were constructed using three Neural Network algorithms viz., Back Propagation Network (BPN), Radial Basis Function (RBF) and Linear Vector Quantization (LVQ). The models classify each aquaculture site into 3 classes viz., suitable, moderate and unsuitable. From the results of the three models, it has been found that Radial Basis Function model not only gives accurate results but also time taken for training the dataset is less when compared with the other two Neural Network models. The results obtained from the neural network models were validated with the results of the fuzzy model. [ABSTRACT FROM AUTHOR]

Sugarcane is one of the leading commercial crops grown in India. The prevailing weather during the various crop-growth stages significantly impacts sugarcane productivity and the quality of its juice. The objective of this study was to predict the yield of sugarcane during different growth periods using machine learning techniques viz., random forest (RF), support vector machine (SVM), stepwise multiple linear regression (SMLR) and artificial neural networks (ANN). The performance of different yield forecasting models was assessed based on the coefficient of determination (R2), root mean square error (RMSE), normalized root mean square error (nRMSE) and model efficiency (EF). Among the models, ANN model was able to predict the yield at different growth stages with higher R2 and lower nRMSE during both calibration and validation. The performance of models across the forecasts was ranked based on the model efficiency as ANN > RF > SVM > SMLR. This study demonstrated that the ANN model can be used for reliable yield forecasting of sugarcane at different growth stages. [ABSTRACT FROM AUTHOR]

Neural networks are powerful forecasting tools that draw on the most recent developments in artificial intelligence research. They are non-linear models that can be trained to map past and future values of time series data and thereby extract hidden structures and relationships that direct the data. Many studies have shown that artificial neural networks have the capacity to learn the underlying mechanics of stock markets. In fact, artificial neural networks have been widely used for forecasting financial markets. However, such applications to Indian stock markets are scarce. The paper investigates the application of artificial neural networks to the dynamic interrelations between macroeconomic variables i.e. Foreign Exchange rate, Foreign Exchange reserves and Wholesale price index. Multilayer perceptron network is used to build the monthly prices model for CNX Nifty and the network is trained using Error Back Propagation algorithm. It is found that the predictive power of the network model is influenced by the previous values. The study shows that satisfactory results can be achieved when applying neural networks to predict the Indian Stock prices. [ABSTRACT FROM AUTHOR]

Identifying current and future informal regions within cities remains a crucial issue for policymakers and governments in developing countries. The delineation process of identifying such regions in cities requires a lot of resources. While there are various studies that identify informal settlements based on satellite image classification, relying on both supervised or unsupervised machine learning approaches, these models either require multiple input data to function or need further development with regards to precision. In this paper, we introduce a novel method for identifying and informal settlements using street intersection data. With such minimal input data, we attempt to provide planners and policy-makers with a pragmatic tool that can aid in identifying informal zones in cities. The algorithm of the model is based on spatial statistics and Artificial Neural Networks (ANN). The proposed model relies on defining informal settlements based on two ubiquitous characteristics of informal settlements. We applied the model in five major cities in Egypt and India that have spatial structures in which informality is present. These cities are Greater Cairo, Alexandria, Hurghada and Minya in Egypt, and Mumbai in India. The predictSLUMS model shows high validity and accuracy for identifying and predicting informality within the same city the model was trained on or in different ones of a similar context. To enable this model to be used as a pragmatic tool for future prediction or further research concerning informal settlements and slums, we have made the python code for the pre-trained ANN models of the five studied cities. • A modelling framework is developed for predicting informal settlements. • The model only requires street intersection data as input, making it broadly applicable. • The model is applied to five cities; Greater Cairo, Alexandria, Hurghada and Minya in Egypt, and Mumbai in India. • By training the model using data from one city, the model can predict informal settlements in another city. • Using ground truth data of planning status for validation, the model achieves accuracy of between 82.1 and 97.6%. [ABSTRACT FROM AUTHOR]

In this paper, the problem of site characterization is treated as a task of function approximation of the large existing data from standard penetration tests (SPTs) in three-dimensional subsurface of Bangalore, India. More than 2,700 field SPT values (N) has been collected from 766 boreholes spread over an area of 220-km2 area in Bangalore, India. To get N corrected value (Nc), N values have been corrected for different parameters such as overburden stress, size of borehole, type of sampler, length of connected rod. In three-dimensional analysis, the function Nc=Nc(X,Y,Z), where X, Y, and Z are the coordinates of a point corresponds to Nc value, is to be approximated with which Nc value at any half-space point in Bangalore, India can be determined. An attempt has been made to develop artificial neural network (ANN) model using multilayer perceptrons that are trained with Levenberg-Marquardt back-propagation algorithm. Also, a geostatistical model based on ordinary kriging technique has been adopted. The knowledge of the semivariogram of the Nc values is used in the ordinary kriging method to predict the Nc values at any point in the subsurface of Bangalore, India where field measurements are not available. The results obtained show that ANN model is fairly accurate in predicting Nc values. In case of ordinary kriging, a new type of cross-validation analysis shows that it is a robust model for prediction of Nc values. A comparison between the ANN and geostatistical model demonstrates that the ANN model is superior to Geostatistical model in predicting Nc values in the subsurface of Bangalore, India. [ABSTRACT FROM AUTHOR]

Abstract: Energy is a vital input for social and economic development of any nation. With increasing agricultural and industrial activities in the country, the demand for energy is also increasing. Formulation of an energy model will help in the proper allocation of widely available renewable energy sources such as solar, wind, bioenergy and small hydropower in meeting the future energy demand in India. During the last decade several new concepts of energy planning and management such as decentralized planning, energy conservation through improved technologies, waste recycling, integrated energy planning, introduction of renewable energy sources and energy forecasting have emerged. In this paper an attempt has been made to understand and review the various emerging issues related to the energy modeling. The different types of models such as energy planning models, energy supply–demand models, forecasting models, renewable energy models, emission reduction models, optimization models have been reviewed and presented. Also, models based on neural network and fuzzy theory have been reviewed and discussed. The review paper on energy modeling will help the energy planners, researchers and policy makers widely. [Copyright &y& Elsevier]

This study focuses on the crucial task of forecasting tax revenue for India, specifically the Goods and Services Tax (GST), which plays a pivotal role in fiscal spending and taxation policymaking. Practically, the GST time series datasets exhibit linear and non-linear fluctuations due to the dynamic economic environment, changes in tax rates and tax base, and tax non-compliance, posing challenges for accurate forecasting. Traditional time-series forecasting methods like ARIMA, assuming linearity, often yield inaccurate results. To address this, we explore alternative forecasting models, including Trigonometric Seasonality Box-Cox Transformation ARIMA errors Trend Seasonal components (TBATS) and Neural Networks: Artificial Neural Networks (ANN), Neural Networks for Autoregression (NNAR), which capture both linear and non-linear relationships. First, we test single time series models like Exponential Smoothing (ETS), TBATS, ANN, and NNAR. Second, we also test hybrid models combining linear models, non-linear models, and neural network models. The findings reveal that the Hybrid Theta-TBATS model offers superior forecasting accuracy, challenging recent research favouring neural network models. The study highlights the effectiveness of advanced non-linear models, particularly TBATS and its hybridisations with linear models, in GST revenue forecasting. Our study also found that the single TBATS is the second-best model, which offers better forecasting accuracy. These insights have significant implications for policymakers and researchers in taxation and fiscal planning, emphasising the need to incorporate non-linear dynamics and advanced modelling techniques to enhance the accuracy of GST revenue forecasts. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents the methodologies for incipient fault detection in power transformers both off-line and on-line. An artificial neural network is used to detect faults off-line with dissolved gas analysis reports of transformers and whereas wavelet transforms are being used for on-line fault detection. The accuracy in fault detection through artificial neural networks is compared with Rogers ratio method using the analysis of experimental oil samples for power transformers of power companies in Andhra Pradesh, India. The Wavelet transform techniques have been developed with different mother wavelets to detect incipient faults and to distinguish between incipient fault and short circuit fault. Further their performances with different mother wavelets are compared. [Copyright &y& Elsevier]

Treating the occurrence and severity of droughts as random, a hybrid model, combining a linear stochastic model and a nonlinear artificial neural network (ANN) model, is developed for drought forecasting. The hybrid model combines the advantages of both stochastic and ANN models. Using the Standardized Precipitation Index series, the hybrid model as well as the individual stochastic and ANN models were applied to forecast droughts in the Kansabati River basin in India, and their performances were compared. The hybrid model was found to forecast droughts with greater accuracy. [ABSTRACT FROM AUTHOR]

Information on heights of waves and their distribution around harbor entrances is traditionally obtained from the knowledge of incident wave, seabed and harbor characteristics by using experimental as well as numerical models. This paper presents an alternative to these techniques based on the computational tool of neural networks. Modular networks were developed in order to estimate wave heights in and around a dredged approach channel leading to harbor entrance. The data involved pertained to two harbor locations in India. The training of networks was done using a numerical model, which solved the mild slope equation. Test of the network with several alternative error criteria confirmed capability of the neural network approach to perform the wave tranquility studies. A variety of learning schemes and search routines were employed so as to select the best possible training to the network. Mutual comparison between these showed that the scaled conjugate method was the fastest among all whereas the one step secant scheme was the most memory efficient. The Brent''s search and the golden section search routines forming part of the conjugate gradient Fletcher–Reeves update approach of training took the least amount of time to train the network per epoch. Calibration of the neural network with both mean square as well as the sum squared error as performance functions yielded satisfactory results. [Copyright &y& Elsevier]

Reference evapotranspiration (ET0) plays a fundamental role in irrigated agriculture. The objective of this study is to simulate monthly ET0 at a meteorological station in India using a new method, an improved support vector machine (SVM) based on the cuckoo algorithm (CA), which is known as SVM-CA. Maximum temperature, minimum temperature, relative humidity, wind speed and sunshine hours were selected as inputs for the models used in the simulation. The results of the simulation using SVM-CA were compared with those from experimental models, genetic programming (GP), model tree (M5T) and the adaptive neuro-fuzzy inference system (ANFIS). The achieved results demonstrate that the proposed SVM-CA model is able to simulate ET0 more accurately than the GP, M5T and ANFIS models. Two major indicators, namely, root mean square error (RMSE) and mean absolute error (MAE), indicated that the SVM-CA outperformed the other methods with respective reductions of 5–15% and 5–17% compared with the GP model, 12–21% and 10–22% compared with the M5T model, and 7–15% and 5–18% compared with the ANFIS model, respectively. Therefore, the proposed SVM-CA model has high potential for accurate simulation of monthly ET0 values compared with the other models. [ABSTRACT FROM AUTHOR]

Optimal use of scarce water resources is the prime objective for water resources development projects in the developing country like India. Optimal releases have been generally expressed as a function of reservoir state variables and hydrologic inputs by a relationship which ultimately allows the policy/water managers to determine the water to be released as a function of available information. Optimal releases were obtained by using optimal control theory with inflow series and revised reservoir characteristics such as elevation area capacity table, zero elevation level as input in this study. Operating rules for reservoir were developed as a function of demand, water level and inflow. Artificial Neural Network (ANN) with back propagation algorithm, Fuzzy Logic and decision tree algorithms such as M5 and REPTree were used for deriving the operating rules using the optimal releases for an irrigation and power supply reservoir, located in northern India. It was found that fuzzy logic model performed well compared to other soft computing techniques such as ANN, M5P and REPTree investigated in this study. [ABSTRACT FROM AUTHOR]

Evaporation in reservoirs plays a prominent role in water resources planning, operation, and management because a considerable amount of water is lost through evaporation, especially in large reservoirs. Estimating evaporation from surface water usually requires ample data that are not easily measurable. At present, in India, reservoir evaporation is estimated from the pan evaporation and average water spread area. Because reservoir evaporation exhibits a nonlinear relationship with the reservoir storage and other meteorological parameters, accurate prediction of evaporation by the conventional method is a cumbersome process. Recently evolved data-driven techniques will excel in nonlinear processes modeling. In this study, reservoir evaporation is predicted using three different data-driven techniques-artificial neural network (ANN), model tree (MT), and genetic programming (GP)-by time-series modeling. The daily Koyna reservoir evaporation prediction models are developed using 49 years of daily evaporation data. Approximately 70% of the data set is used for training the model, and the remaining 30% is used for testing. From this study, all of the data-driven techniques predicted the reservoir evaporation very accurately, with better performance and a correlation of approximately 0.99. This shows that if the input data series exhibits a good pattern with less noise, the data-driven techniques result in better performances. Among the data-driven techniques used in this study, GP predicts the reservoir evaporation slightly better than ANN and MT models. [ABSTRACT FROM AUTHOR]

The article describes a framework for forecasting the surgery time by considering the surgical environment in an ophthalmology department. Topics covered include the techniques used in the estimation of surgery times, the use of the framework in various ophthalmologic surgeries and the hypothesis that one can schedule the operations optimally by accurately knowing the surgery times.

Approximately 42% of Indian government-funded construction projects are facing time overruns. With a number of challenging projects around the corner, there is a definite need to overcome these delays. In an earlier study conducted by one of the authors, 55 project performance attributes were identified based on expert opinions and literature surveys, which were subsequently reduced to 20 factors (11 success and nine failure factors) using factor analysis. A second-stage questionnaire survey based on these factors was used to identify the significant schedule performance factors. The analysis of the survey responses led us to conclude that factors such as: a project manager’s competence; monitoring and feedback by project participants; commitment of all project participants; owner’s competence; interaction between external project participants; and good coordination between project participants significantly affect schedule performance. The survey also provided the basis for the development of a schedule performance prediction model. For this, an artificial neural network (ANN) method was used to construct the model, and the best was determined to be a 6-3-1 feed-forward neural network based on a back-propagation algorithm with a mean absolute percentage deviation (MAPD) of 11%. This enables project team members to understand the factors they must monitor closely in order to complete the projects on schedule and to predict performance throughout the course of the project. [ABSTRACT FROM PUBLISHER]

All reservoirs are subjected to sediment inflow and deposition to a certain extent resulting in reduction of their capacity. Trap efficiency (Te), a most important parameter for reservoir sedimentation studies, is being estimated using conventional empirical methods till today. A limited research has been carried out on estimating the variation of Te with time. In the present study, an attempt has been made to incorporate the age of the reservoir to estimate the Te. This study investigates the suitability of conventional empirical approaches along with soft computing data-driven techniques to estimate the reservoir Te. The incorporation of reservoir age, in empirical model, has resulted in a better Te estimation. Further, to estimate Te at different time steps, soft computing approaches such as artificial neural networks (ANNs) and genetic programming (GP) have been attempted. Based on correlation analysis, it was found that ANN model (4-4-1) resulted better than conventional empirical methods but inferior to GP. The results show that the GP model is parsimonious and understandable and is well suited to estimate Te of a large reservoir. [ABSTRACT FROM AUTHOR]

Conventional methods and models available for estimation of reservoir sedimentation process differ greatly in terms of complexity, inputs, and other requirements. An artificial neural network (ANN) model was used to estimate the volume of sediment retained in a reservoir. Annual rainfall, annual inflow, and capacity of the reservoir were chosen as inputs. Thirty Two years of data pertaining to Gobindsagar Reservoir on the Satluj River in India, were used in this study (23 years for training and 9 years for testing). The pattern of the sediment volume retained in this reservoir was well captured by the Multi-Layer Perceptron (3–5-1) ANN model using the back propagation algorithm. Based on several performance indices, it was found that the ANN model estimated the volume of sediment retained in the reservoir with better accuracy and less effort as compared to conventional regression analysis. [ABSTRACT FROM AUTHOR]

The article discusses a study which used an Artificial Neural Network (ANN) methodology to predict groundwater levels in individual wells. Groundwater levels were predicted in neighbouring wells using model parameters from the best network of a well. The methodology was applied to an urban coastal aquifer in Andhra Pradesh state, India. The performance of various ANN algorithms were compared in predicting the groundwater levels with a one month lead in six observation wells independently. The study revealed that the feed-forward neural network with Lavenberg-Marquadt (LM) algorithm is a good choice for predicting groundwater levels in individual wells.

Respirable particulate matter (PM10) concentration at one residential site in Delhi, India was predicted using the neural network approach. The concepts of chaotic systems theory were utilized to build the neural network model. The embedding dimension was estimated to provide the inputs to the neural network. The model evaluation results indicated the importance of noise reduction before selecting the embedding dimension of the time series. The selection of a proper embedding dimension is considered to be essential for obtaining reliable predictions. The model’s performance shows the capability of neural networks in modelling the chaotic time series. [ABSTRACT FROM AUTHOR]