Tripathi, R.C. and Kalyani, V.K. and Ram, L.C. and Jha, S.K. (2015) Prediction of Wheat Yield from Pond Ash Amended Field by Artificial Neural Networks. Journal of Hazardous, Toxic, and Radioactive Waste, 19 (4). ISSN 2153-5493

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Abstract

In India, presently 85 existing thermal power plants (TPPs) produce 118 million metric tons of fly ash per year, which is projected to exceed 170 and 440 million metric tons/year by Years 2012 and 2030, respectively. This huge quantity of fly ash not only poses the problems of environmental concerns but occupies large areas of land for its dumping, which needs urgent and appropriate measures for its safe disposal and gainful utilization on sustainable basis. Based on the detailed study on the bulk utilization of pond ash in agriculture and forestry sectors under different agroclimatic conditions and soil types for last 2 decades, it has been found that pond ash has a very good potential for the utilization on bulk scale as liming agent, soil conditioner, source of essential plant nutrients, and also for boosting the growth and yields of a variety of crops and growth performance of plant species. Some field-scale studies carried out especially in the wastelands of State Agriculture Research Farm and farmers’ fields are discussed in the present paper. The influence of critical parameters on the yield of wheat crops cultivated in different soil types and agroclimatic conditions is discussed. Further, an attempt has been made to develop a three-layer feed-forward artificial neural network (ANN) model, which is inherently trained using error back-propagation algorithm. The results evince that the predictions from the ANN model are in good qualitative and quantitative agreement with the field observations, thereby validating the applicability and accuracy of the developed ANN model.

Item Type:	Article
Subjects:	Enviornmental Management
Divisions:	UNSPECIFIED
Depositing User:	Mr. B. R. Panduranga
Date Deposited:	25 May 2017 05:06
Last Modified:	25 May 2017 05:06
URI:	http://cimfr.csircentral.net/id/eprint/1742

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