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In order to survive, mankind needs air. The quality of life depends on the purity of the air we breathe in. Hazardous pollutants are stirred up in our environment by various activities everyday. In a developing country like India, which has huge population, protecting public health is important. In general, air quality is measured using the air quality index, which records the pollutants level in the air. These recordings are to be mentioned across various places, to know about the air quality level. The advancement of the recent artificial intelligence can be replaced by the human efforts and to automate the system in flowless manner. Hence, an effort has been taken in this paper, by proposing a framework of predicting the air quality seasonally using regularized combinational LSTM (REG-CLSTM). For an efficient air quality level prediction with an improved error rate, time, and accuracy, the study has implemented Reg-CLSTM with a large amount of real-time dataset. To improvise the comprehensiveness and the potentiality of the proposed model, the significant feature is extracted by the rough set-wrapper method. The significant challenge is to provide a seasonal limit range for each considered pollutant in place of a generalized range partition. Aiming at this problem, the proposed model is able to identify the highest concerning pollutants occurrence in individual seasons. Through this study, pyramid learning-based hybridized deep learning framework is developed which can play a crucial role in warning the policy maker to reduce the activities that instigate air pollution. [ABSTRACT FROM AUTHOR]

In this paper, we have discussed the application of the artificial neural networks in wind speed prediction. They will be used to predict the average monthly wind speed at three wind gauging stations in Gujarat, India. The wind speed data on an hourly basis are collected by NIWE (National Institute of Wind Energy) and located in the coastal areas of Western India, primarily Gujarat. The short-term and long-term data consisting of wind speeds have been considered for the period from 2015 to 2017. An artificial neural network is utilized for wind speed prediction using data measured from these stations for training and testing the given information. The data were studied using the nonlinear autoregressive models, NAR and NARX and the chaotic time series prediction models. The model is predicted using the historical data of the same station. The data are measured at a height of 100 m. The mean absolute percentage error (MAPE) and mean average error (MAE) concerning the predicted and measured wind speed were found to be 5.09 × 10−3, 5.33 × 10−3 and 2.9 × 10−3, respectively. The results of the ANN technique were compared with the Mackey–Glass equation-based time series prediction. Additionally, studies have been done on calculating the production and supply capacity of wind energy. [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]