ABSTRACT
A new approach for the development of vehicular traffic noise prediction models is presented. Four different soft computing methods, namely, Generalized Linear Model, Decision Trees, Random Forests and Neural Networks, have been used to develop models to predict the hourly equivalent continuous sound pressure level, Leq, at different locations in the Patiala city in India. The input variables include the traffic volume per hour, percentage of heavy vehicles and average speed of vehicles. The performance of the four models is compared on the basis of performance criteria of coefficient of determination, mean square error and accuracy. 10-fold cross validation is done to check the stability of the Random Forest model, which gave the best results. A t-test is performed to check the fit of the model with the field data.
Subject(s)
Models, Theoretical , Noise, Transportation , Cities , Decision Trees , India , Linear Models , Neural Networks, ComputerABSTRACT
Mycoherbicides are exclusive biotechnology products which offer a non-chemical solution to control noxious weeds on the land as well as aquatic in systems, viz a viz saving environment from hazardous impact of synthetic chemicals. The present paper highlights the mycobiota associated with Eichhornia crassipes infesting Harike wetland area of Punjab and evaluation of their pathogenic potential for futuristic application as a mycoherbicide. Of the 20 isolates tested by leaf detached assay and whole plant bioassays, only one isolate (#8 BJSSL) caused 100% damage to E. crassipes. Further, the culture filtrate of this isolate also exhibited a similar damage to the leaves in an in vitro detached leaf assay. The potential isolate was identified as Phaeoacremonium italicum using classical and modern molecular methods. This is the first report of P. italicum as a pathogen of E. crassipes and of its potential use as a biological control agent for the management of water hyacinth.
