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• Optimum design and economic analysis of HRES for rural electrification in Korkadu. • RES are solar PV, wind turbine and bio-diesel generators. • Estimates the load forecasting for selected district in the Pre- HOMER analysis. • Desired HRES has to meet forecasted load demand for reliable electrification. This paper validates the optimal design and techno-economic feasibility of hybrid renewable energy system (HRES) for rural area electrifying applications. Plan to a design of improved performance electrification system through village owned renewable resources, such as solar irradiations, wind speed and bio mass etc. The selected HRE system has to meet out electrical needs in optimum performance manner. Hear conducted a case study on remote village Korkadu is located in Union Territory of Pondicherry, India. The expected village demand of 179.32 Kwh/day and peak of 19.56 Kw was met with proposed HRE structure, which is consists of solar PV array, wind turbine, Bio mass power generator and Battery backup system in effectively. Load growth of the village was predicted through artificial neural network (ANN-BP) feed-back propagation and Levenberg-Marguardt (LM) data training optimum technique. Encounter the optimum performance of different HRE configuration was evaluated over by HOMER software. System's economic dispatch was analysed through various dispatch strategy and come across the proposing companied dispatch strategy has more economical and performance benefits as total NPC of system as INR 1.21 million, one unit energy generation cost as INR 13.71 and annual battery throughput as 36.648 KWh/yr. This study also expresses the comparison analysis between proposed HRES structure performance with basic utility grid extension. The consequence of the proposed work shows the HRES in remote location can be a cost effective solution for sustainable development of rural regions. [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]

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]

Rainfall, like all other natural phenomena is highly unpredictable. Traditionally, principal component analysis and spectral analysis are used to understand trends in rainfall over long periods. In this paper, we present a new method that appears to be better than existing methods to understand the long-term behavior of rainfall phenomena. Using a case study on the rainfall in Kerala State, the southern part of Indian Peninsula, we show that a new kind of neural network known as the adaptive basis function network is a promising tool for climatic studies, especially rainfall analysis. The paper also reveals that in spite of the fluctuations resulting from the nonlinearity in the system, the trends in the rainfall pattern in Kerala state have remained unaffected over the past 87 years from 1893 to 1980. We also successfully filter out the chaotic part of the system and illustrate that its effects are marginal over long-term predictions. [Copyright &y& Elsevier]

The two main challenges of predicting the wind speed depend on various atmospheric factors and random variables. This paper explores the possibility of developing a wind speed prediction model using different Artificial Neural Networks (ANNs) and Categorical Regression empirical model which could be used to estimate the wind speed in Coimbatore, Tamil Nadu, India using SPSS software. The proposed Neural Network models are tested on real time wind data and enhanced with statistical capabilities. The objective is to predict accurate wind speed and to perform better in terms of minimization of errors using Multi Layer Perception Neural Network (MLPNN), Radial Basis Function Neural Network (RBFNN) and Categorical Regression (CATREG). Results from the paper have shown good agreement between the estimated and measured values of wind speed. According to the result, it can be concluded that ANN model with MLPNN could produce the acceptable prediction of the wind speed for given on wind direction. • To predict the wind speed (m/sec) at 65 m height. • To build the ANN Models and Categorical Regression (CATREG) for wind speed prediction. • To produce the validated and acceptable prediction. [ABSTRACT FROM AUTHOR]

Social Networking Sites have significant potential to reveal valuable explicit as well as implicit statistics and patterns when deep learning is applied to their raw and unstructured data. Tweets posted by the users on their timeline not only reflect their mindset, their likes and dislikes but could also be used to unveil significant amount of information about many psychological aspects and behavior that may be hard to study directly. This paper aims to predict the personality of the 100 real-time Twitter users conforming to personality traits in the BIG 5 model by extracting features from their tweets using ensemble of CNN (Convolutional Neural Network) and BiLSTM (Bidirectional Long Short Memory). The findings of our experiment shows that our model performs slightly better than previous baselines methods achieving an accuracy 75.134% on testing data. We have further hypothesized that unrestricted data available on Twitter may contain features that can be used to predict the personality of its user. It was concluded that personality of Twitter users in the real world is reflected in their online behaviour, reinforcing the premise that the nature of online interactions does not significantly differ from that of real-world interactions. Overall, the study provides a deep insight into the impact of social media data in providing predictive indicators of user behavior. • Twitter data can provide predictive indicators of user behavior. • We hypothesized personality of an individual is highly correlated with their online behavior. • The study aims to predict the personality of Indian Twitter users. • Deep learning ensemble model has been employed for the study. • It is observed that nature of online interactions does not differ from real-world interactions. [ABSTRACT FROM AUTHOR]

Abstract: Forecasting of wave heights is essential for planning and operation of maritime activities. Traditionally, wave heights have been predicted using physics-based models, which rely primarily on the energy balance equation. More recently, soft computing techniques such as Artificial Neural Network (ANN), Genetic Programming (GP) have been used to generate forecasts with leads times from a few hours to several days. However, the forecast accuracy of both methods could be improved, particularly at peak wave heights, and at higher lead times. This paper forecasts the wave heights with lead times of 12h and 24h using GP. The data are obtained from two locations, along the North American and Indian coastlines. Wind information is used as an input. The modeling procedure relies heavily on the parameter kurtosis, or fourth moment. The forecasts are satisfactory, especially for the peak wave heights formed by the extreme events like hurricanes. [Copyright &y& Elsevier]

Abstract: Increasing demand of bandwidth in communication satellites has forced satellite links to be designed in Ku bands and above. But at these frequencies, rain and other tropospheric elements result in large attenuation. To mitigate the tropospheric attenuation of microwave satellite signals above 10GHz using any standard Fade Mitigation Technique (FMT), it is essential to have a priori knowledge about the level of attenuation. Hence, short-term rain attenuation prediction models play a key role in maintaining the link in which necessary compensation can be applied depending on the early information of attenuation. This paper presents a method of attenuation prediction using Adaptive Artificial Neural Network. Here In situ Learning Algorithm (ILA) has been used to enable the system to track the non-stationary nature of the attenuation. To validate this, Ku Band data, collected at three different sites in India have been used for the purpose of prediction. The performance of the algorithm is determined through the estimation of prediction accuracy by comparing the predicted values with the measured data. Results obtained using the mentioned technique shows considerably good accuracy even up to 20s of prediction interval with acceptable ratio between the under and over predictions. The prediction performance is evaluated for different prediction intervals. Furthermore the present model is also compared with the persistence model and the relative performance is quantified. [Copyright &y& Elsevier]

Abstract: Coal mine fires due to spontaneous heating are a major concern worldwide. Most of these fires could be averted if suitable preventive measures are taken. Since the spontaneous heating potential of all types of coals are not the same, its accurate prediction is essential to plan efficient preventive measures and improve production and storage capabilities of a mine. The current paper presents a comparison of two approaches viz. fuzzy expert system and the commonly used artificial neural networks (ANN) for forecasting the self heating of coals. To apply these techniques, 30 coal samples of varying ranks were collected from different coalfields of the country. The intrinsic properties of the coal seams were determined by proximate, ultimate and petrographic analyses. The spontaneous heating proneness of the samples was studied using crossing point temperature (CPT), which is treated as an important measure for fire susceptibility of coal seams in Indian mines. Correlation studies between the intrinsic properties and CPT was carried out to identify the parameters for prediction purpose. Using moisture, volatile matter, and ash content as input parameters, CPT is predicted using fuzzy logic based on Takagi–Sugeno–Kang (TSK) model and ANN based on back propagation algorithm. Triangular fuzzy membership function is adopted for describing input variables. The results show both the models predict CPT with reasonable accuracy. Fuzzy modelling being a simpler approach, it can be utilized in the field where experimental data on coal properties are not precisely available but human judgement and intuition can be adopted for prediction purpose. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, the parameter-wise optimization training process is implemented to achieve an optimal configuration of focused time lagged recurrent neural network (FTLRNN) models by embedding the gamma, laguarre, and multi-channel tapped delay line memory structure. The aim is to examine the prediction ability of the proposed models in order to predict one-day-ahead electric power load simultaneously as usual to oppose 1–24h forecast in sequel with a special emphasis on seasonal changes over a year. An improved delta-bar-delta algorithm is used to accelerate the training of neural networks and to improve the stability of the convergence. Experimental results indicate that the FTLRNN with time delay neural network (TDNN) clearly outperformed the gamma and laguarre based short-term memory structure in various performance metrics such as mean square error (MSE), normalized MSE, correlation coefficient (r) and mean absolute percentage error (MAPE) during evaluation process. Empirical results show that the proposed dynamic NN model consistently performs well on daily, weekly, and monthly average basis in terms of prediction accuracy. It is noticed from the literature review that an optimally configured FTLRNN with multi-channel tapped delay line memory structure is not currently available to solve short-term electrical power load prediction. The proposed method gives acceptable errors in all seasons, months and on daily basis. The average prediction error on three weeks is obtained as low as 1.67%. [ABSTRACT FROM AUTHOR]

Abstract: L-band (1.5GHz) and VHF (244MHz) amplitude scintillations observed from Kolkata (22.58°N lat, 88.38° E long, 32°N dip), over a solar cycle 1996–2006 are presented in this paper. Situated near the northern crest of the equatorial anomaly, it is an excellent platform for scintillation studies. Based on 11 years’ data, an attempt is made to develop models of hourly percentage occurrence of scintillations for the rising and declining phases of solar cycle using Neural Network. The relation between fading rate at VHF with the S4 index at L-band is also investigated. [Copyright &y& Elsevier]

Abstract: This paper presents the suitability of artificial neural network (ANN) to predict the performance of a direct expansion solar assisted heat pump (DXSAHP). The experiments were performed under the meteorological conditions of Calicut city (latitude of 11.15°N, longitude of 75.49°E) in India. The performance parameters such as power consumption, heating capacity, energy performance ratio and compressor discharge temperature of a DXSAHP obtained from the experimentation at different solar intensities and ambient temperatures are used as training data for the network. The back propagation learning algorithm with three different variants (such as, Lavenberg–Marguardt (LM), scaled conjugate gradient (SCG) and Pola-Ribiere conjugate gradient (CGP)) and logistic sigmoid transfer function were used in the network. The results showed that LM with 10 neurons in the hidden layer is the most suitable algorithm with maximum correlation coefficients (R2 ) of 0.999, minimum root mean square (RMS) value and low coefficient of variance (COV). The reported results conformed that the use of ANN for performance prediction of DXSAHP is acceptable. [Copyright &y& Elsevier]

Peak ground motions and spectral accelerations estimated from the prediction equations are highly significant in earthquake hazard studies. Recently, these predictive relationships developed for higher-order parameters obtained paramount importance as they describe different ground motion characteristics. The northeastern region of India experiences extreme seismicity due to the Indian plate subduction under the South Asian plate. However, only a few ground motion prediction equations (GMPEs) are available for such tectonic environments due to insufficient ground motion data. In this regard, it is noticed that the tectonic environment experienced by New Zealand is similar to that of northeast India. So, in this paper, two GMPE models for New Zealand are developed with the help of the artificial neural network (ANN) technique using the GeoNet database. Model-1 corresponds to various higher-order parameters, whereas model-2 developed for spectral accelerations (S a) between 0.01 and 5s. Further, these models are compared against global and region-specific GMPEs. The developed models shows good agreement with other GMPEs and the data but slightly over predicts at distances greater than 300 km. Additional consideration of site-to-site variability in the current models reduced the total standard deviations of model-1 by 19–22 % and model-2 by 20%–23 %. Further, the estimates of these developed models are compared with some of the significant earthquakes in northeast India, and from these results, it is concluded that the current models can be adapted in such regions to estimate ground motion. • In this study, ANN models for GMPs and S a are developed for the GeoNet database. • Non-ergodic assumption reduced the standard deviation of the current models by ~20 %. • Developed models can be used in the subduction regions like northeast India. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, an artificial neural network (ANN) model is developed for estimating beam solar radiation. Introducing a newly defined parameter, known as reference clearness index (RCI), computation of monthly mean daily beam solar radiation at normal incidence has been carried out. This RCI is defined as the ratio of measured beam solar radiation at normal incidence to the beam solar radiation as computed by Hottel''s clear day model. Solar radiation data from 11 stations having different climatic conditions all over India have been used for training and testing the ANN. The feedforward back-propagation algorithm is used in this analysis. The results of ANN model have been compared with measured data on the basis of root mean square error (RMSE) and mean bias error (MBE). It is found that RMSE in the ANN model varies 1.65–2.79% for Indian region. [Copyright &y& Elsevier]

Abstract: The tremendous increase in offshore operational activities demands improved wave forecasting techniques. With the knowledge of accurate wave conditions, it is possible to carry out the marine activities such as offshore drilling, naval operations, merchant vessel routing, nearshore construction, etc. more efficiently and safely. This paper describes an artificial neural network, namely recurrent neural network with rprop update algorithm and is applied for wave forecasting. Measured ocean waves off Marmugao, west coast of India are used for this study. Here, the recurrent neural network of 3, 6 and 12 hourly wave forecasting yields the correlation coefficients of 0.95, 0.90 and 0.87, respectively. This shows that the wave forecasting using recurrent neural network yields better results than the previous neural network application. [Copyright &y& Elsevier]

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]

Time-series modeling forms an important area of research in geophysics. Time-series models can be linear, like linear state-space models or non-linear, like artificial neural networks. One way to judge the goodness of different models associated with a given time-series is to assess the prediction capabilities of these models. Some of the important techniques used in time-series forecasting are: (i) Minimum mean squared error (MMSE) forecast obtained using conditional means of ARIMA(p,d,q) models, (ii) Kalman filter approach and (iii) Artificial neural networks (ANN) approach. However, the wavelet-based versions of these techniques, respectively denoted as W-MMSE, W-Kalman and W-ANN, rather than the original techniques themselves, have been found to possess better capabilities in forecasting the highly nonlinear geophysical data. Using the original and predicted data, these observations have been validated by determining the RMSE (root mean squared error) and correlation coefficients between them. The prediction capabilities of both versions of the above techniques are tested on (i) the ionospheric total electron content (TEC) data, (ii) the daily average rainfall data and (iii) the gamma-ray log data from an offshore oil well, off the west coast of India. The TEC and daily average rainfall data sets designate as examples of data with very high correlations pertaining over very large lags. They also have a strong seasonality component associated with them. However, the gamma-ray log data sets show no seasonality component and have no trends associated with them. Therefore, the choice of different nonlinear data sets having diverse sources of their origins are apt to test the forecasting capabilities of these techniques. It has been observed that W-ANN gives the best prediction, when compared with the other algorithms discussed in this paper. This is believed to be due to the use of non-linear activation function by ANNs to produce regression that results in capturing the inherent non-linear dynamics of the process effectively. The results also show the usefulness of discrete wavelet transform (DWT) coefficients as training features for both linear and non-linear forecasting approaches. The better performance of the wavelet-based forecasting algorithms can be attributed to the fact that DWT coefficients are wide-sense stationary sequences. Thus the wavelet-based versions of these models like W-MMSE and W-Kalman provide better fit to these coefficients, compared to the original time-series data itself. • Wavelet-based forecasting techniques, W-MMSE, W-Kalman and W-ANN are introduced. • W-MMSE, W-Kalman and W-ANN perform better than the regular known techniques. • W-MMSE, W-Kalman and W-ANN are tested on different nonlinear geophysical signals. • W-ANN produces best forecast compared to the other tested wavelet-based techniques. [ABSTRACT FROM AUTHOR]

Investigations of changing the flow regime and flow prediction are of vital societal and hydro-ecological importance in a transboundary river like Punarbhaba river between India and Bangladesh. The present paper investigated flow regime though advance periodicity models (Morlet's wavelet transformation) at the seasonal scale. Flow prediction using advanced machine learning techniques like Support vector machine (SVM), Artificial Neural Network (ANN), Hybrid wavelet ANN (W-ANN), Random forest(RF) capturing the periodicity, duration, cyclic or semi-cyclic nature of flow wave or wavelet from the historical time series data (1978–2017) is very crucial for environmental flow management and estimating present and future states of environmental flow. Flow alteration modeling (using heat map) and estimation of environmental flow (using duration curve shifting and RVA methods) are another vital objectives of this work to know the present and future hydro-ecological state. The result of periodicity clearly identified two distinct flow regimes before and after 1992-93 triggered by damming over there in 1992. All the prediction models identified the declining trend of flow in all the seasons, however hybrid wavelet ANN model could be treated as the best suited because of its very high-performance level. The degree of hydrological alteration is identified very high in the post-hydrological alteration (PHA) (post-1992) period and it is likely to be intensified predicted period. The estimated environmental flow state in PHA falls under moderate to critically modified states but if alteration goes on in this way ecological deficit will be the obvious result. For the survival of the river estimated environmental flow could be released primarily. Image 1 • The periodicity analyses for annual and seasonal flow were performed using wavelet. • The flow has been forecasted for 13 years using machine-learning methods. • The hydrological alteration has been estimated. • The present and future environmental flow has been estimated. [ABSTRACT FROM AUTHOR]