India has made considerable progress as far as creation of irrigation potential is concerned. The gap between irrigation potential created and utilized is a matter of concern. The success of irrigation system operation and planning depends on the quantification of supply and demand and equitable distribution of supply to meet the demand if possible, or, to minimize the gap between the supply and demand. Hence, it is essential to forecast reservoir inflow for proper planning and management of canal irrigation projects. Autoregressive Integrated Moving Average (ARIMA) and X-12-ARIMA are one of the extensively used software packages for time series forecasting. This study focused on the Application of these software packages for Monthly Stream Flow Forecasting of Kangsabati River of India. Here, ARIMA (2, 1, 1) (2, 1, 2) and ARIMA-X-12 (2, 1, 1) (2, 1, 2) models were found to have less Bayesian Information Criterion (BIC), Akaike Information Criterion (AIC) and many other statistical values, selected for mean monthly foresting. In the comparison of ARIMA and X-12-ARIMA models, the X-12-ARIMA model is found more accurate then the ARIMA model for monthly stream flow forecasting. This study suggests that the selected models can be used successfully for monthly stream flow forecasting of Kangsabati river.

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This paper provide a review of the latest work in the sector of electric vehicle. The transport Sector massively depends on Non-Renewable Sources. Continuous release of harmful substances in the surrounding by the vehicle should be confined and restore with alternative transportation. Electric Highway is one of efficient Solution. It is modern technology of electrical road systems. It is a technique in which electricity is taken from power grid through dynamic Pantograph which is attached with overhead transmission line. Thus, EV’s with a combination of Electric Highway can charge the battery in dynamic motion and reduce the time of recharging of a vehicle. Shortly, the paper introduces some features of EV, their limitation and one of the effective alternate modern solution considering the well-being of the environment.

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In the recent year, pre harvest crop yield forecasting has been a topic of interest for producers, policy makers, government and agricultural related organizations. Pre harvest crop forecasting is important for national food security. Construction of appropriate yield forecast promotes the output of scenario analyses of crop production at a farm level, which enables suitable tactical and strategic decision making by the farmer. Indeed, considerable benefits apply when seasonal forecasting of crop performance is applied across the whole value chain in crop production. Timely and accurate yield forecast is essential for crop production, marketing, storage and transportation decisions as well as for managing the risk associated with these activities. In present manuscript efforts were made for development of pre harvest forecast models by using different statistical approaches viz. multiple linear regression (MLR), discriminant function analysis and ordinal logistic regression. The study utilized the crop yield data and corresponding weekly weather data of last 30 years (1985-2014). The model development was carried out at 35th and 36th SMW (Standard Meteorological Week) for getting forecast well in advance of actual harvesting of the field crop. The study revealed that method of discriminant function analysis gave best pre harvest forecast as compare to remaining developed models. It was observed high value of Adj. R2= 0.94, low value of RMSE= 164.24 and MAPE= 5.30. The model can be used in different crop for reliable and dependable forecast and these forecasts have significant value in agricultural planning and policy making.