Environmental Engineering
1. Forecasting particulate matter concentration using nonlinear autoregression with exogenous input model

M.I. Rumaling; F.P. Chee; H.W.J. Chang; C.M. Payus; S.K. Kong; J. Dayou; J. Sentian

Volume 8, Issue 1 , Winter 2022, , Pages 27-44

http://dx.doi.org/10.22034/GJESM.2022.01.03

Abstract
  BACKGROUND AND OBJECTIVES: Air quality in some developing countries is dominated by particulate matter, especially those with size 10 micrometers and smaller or PM10. They can be inhaled and sometimes can get deep into lungs; some may even get into bloodstream and cause serious health problems. Therefore, ...  Read More

Environmental Management
2. Linking the past, present and future scenarios of soil erosion modeling in a river basin

C. Loukrakpam; B. Oinam

Volume 7, Issue 3 , Summer 2021, , Pages 457-472

http://dx.doi.org/10.22034/GJESM.2021.03.09

Abstract
  BACKGROUND AND OBJECTIVE: Soil erosion is considered one of the major indicators of soil degradation in our environment. Extensive soil erosion process leads to erosion of nutrients in the topsoil and decreases in fertility and hence productivity. Moreover, creeping erosion leads to landslides in the ...  Read More

Environmental Management
3. Forecasting of Covid-19 cases based on prediction using artificial neural network curve fitting technique

S.K. Tamang; P.D. Singh; B. Datta

Volume 6, Special Issue (Covid-19) , 2020, , Pages 53-64

http://dx.doi.org/10.22034/GJESM.2019.06.SI.06

Abstract
  Artificial neural network is considered one of the most efficient methods in processing huge data sets that can be analyzed computationally to reveal patterns, trends, prediction, forecasting etc. It has a great prospective in engineering as well as in medical applications. The present work employs artificial ...  Read More

4. Artificial neural network forecast application for fine particulate matter concentration using meteorological data

M. Memarianfard; A.M. Hatami; M. Memarianfard

Volume 3, Issue 3 , Summer 2017, , Pages 333-340

http://dx.doi.org/10.22034/gjesm.2017.03.03.010

Abstract
  Most parts of the urban areas are faced with the problem of floating fine particulate matter. Therefore, it is crucial to estimate the amounts of fine particulate matter concentrations through the urban atmosphere. In this research, an artificial neural network technique was utilized to model the PM2.5 ...  Read More