Solid waste forecasting using modified ANFIS modeling

Younes, Mohammad K.; Nopiah, Zulkifli Mohd; Basri, Noor Ezlin Ahmad; Basri, Hassan; Abushammala, Mohammed F.M.; K.N.A., Maulud

doi:10.1080/10962247.2015.1075919

Cited by 32 publications

(23 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To serve this purpose, several models are used such as Adaptive Neurofuzzy Inference System (ANFIS), Support Vector Machine (SVM), Genetic Algorithm (GA) and Artificial Neural Network (ANN), that mimics human traits such as learning, problem solving, understanding, perception, reasoning and awareness of surroundings. AI has been applied in forecasting of waste characteristics which includes waste material classification, waste compression ratio and waste generation, trends or patterns ( Younes et al, 2015 ). Most of the studies use ANNs for automated sorting systems, for e.g., multi-layer ANNs and hyperspectral imaging that identifies different types of waste fraction with high accuracy of 99% ( Sudha et al, 2016 ; Tehrani et al, 2017).…”

Section: Innovative Methods Of Bmw Management For Covid-19mentioning

confidence: 99%

“… Golbaz et al, 2019 ; Milojkovic et al, 2008 ; Noori et al, 2009 ; Shamshiry et al, 2011 ; Song et al, 2017 . Adaptive Neuro-Fuzzy Inference System (ANFIS) Used to forecast the waste generation in such developing countries where accurate reliable data is not always available Younes et al, 2015 Genetic Algorithm (GA) Used for the identification of optimal routes, management costs in the case of MSW collection Yang et al, 2012 ; Meyer-Baese et al, 2014 ; Król et al, 2016 Support Vector Machine (SVM) Prediction of bin level status, waste generation, classification, waste heating value and energy recovery. Chen et al, 2016 ; Harrington, 2012 ; Dixon et al, 2008 ; Costa et al, 2018 .…”

Section: Waste Identification Technologiesmentioning

confidence: 99%

See 1 more Smart Citation

Environmental survival of SARS-CoV-2 – A solid waste perspective

Iyer

Tiwari

Renu

et al. 2021

Environmental Research

View full text Add to dashboard Cite

The advent of COVID-19 has kept the whole world on their toes. Countries are maximizing their efforts to combat the virus and to minimize the infection. Since infectious microorganisms may be transmitted by variety of routes, respiratory and facial protection is required for those that are usually transmitted via droplets/aerosols. Therefore this pandemic has caused a sudden increase in the demand for personal protective equipment (PPE) such as gloves, masks, and many other important items since, the evidence of individual-to-individual transmission (through respiratory droplets/coughing) and secondary infection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). But the disposal of these personal protective measures remains a huge question mark towards the environmental impact. Huge waste generation demands proper segregation according to waste types, collection, and recycling to minimize the risk of infection spread through aerosols and attempts to implement measures to monitor infections. Hence, this review focuses on the impact of environment due to improper disposal of these personal protective measures and to investigate the safe disposal methods for these protective measures by using the safe, secure and innovative biological methods such as the use of Artificial Intelligence (AI) and Ultraviolet (UV) lights for killing such deadly viruses.

show abstract

Section: Innovative Methods Of Bmw Management For Covid-19mentioning

confidence: 99%

Section: Waste Identification Technologiesmentioning

confidence: 99%

Environmental survival of SARS-CoV-2 – A solid waste perspective

Iyer

Tiwari

Renu

et al. 2021

Environmental Research

View full text Add to dashboard Cite

show abstract

“…An optimal number of epochs (iterations in learning phase) and type of membership function determines the efficiency of the model. The ANFIS employs «if-then» rules to perform an operation (Jang 1993) which is described below for a first-order model of common two fuzzy rules (Younes et al 2015). Rule 1: If x 1 is A 1 and x 2 is B 1 then f 1 =p 1 x 1 +q 1 x 2 +r 1 Rule 2: if x 1 is A 2 and x 2 is B 2 then f 2 =p 2 x 1 +q 2 x 2 +r 2…”

Section: Methodsmentioning

confidence: 99%

Forecasting Water Level Of Jhelum River Of Kashmir Valley India, Using Prediction And Earlywarning System

Imran

Sheikh

2020

GES

View full text Add to dashboard Cite

The hydrological disasters have the largest share in global disaster list and in 2016 the Asia’s share was 41% of the global occurrence of flood disasters. The Jammu and Kashmir is one of the most flood-prone regions of the Indian Himalayas. In the 2014 floods, approximately 268 people died and 168004 houses were damaged. Pulwama, Srinagar, and Bandipora districts were severely affected with 102, 100 and 148 km 2 respectively submerged in floods. To predict and warn people before the actual event occur, the Early Warning Systems were developed. The Early Warning Systems (EWS) improve the preparedness of community towards the disaster. The EWS does not help to prevent floods but it helps to reduce the loss of life and property largely. A flood monitoring and EWS is proposed in this research work. This system is composed of base stations and a control center. The base station comprises of sensing module and processing module, which makes a localised prediction of water level and transmits predicted results and measured data to the control center. The control center uses a hybrid system of Adaptive Neuro-Fuzzy Inference System (ANFIS) model and the supervised machine learning technique, Linear Multiple Regression (LMR) model for water level prediction. This hybrid system presented the high accuracy of 93.53% for daily predictions and 99.91% for hourly predictions.

show abstract

“…ANFIS is a dynamic, data-driven model that does not require knowledge on the internal system parameters to offer a compact solution [17]. This technique is popular for the modelling of environmental systems due to its accuracy, efficiency, and capacity to handle a large amount of stochastic (linear and non-linear) data [18].…”

Section: Introductionmentioning

confidence: 99%

Evaluation and forecasting of solar radiation using time series adaptive neuro‐fuzzy inference system: Seoul city as a case study

Alsharif

Younes²

2019

IET Renewable Power Generation

Self Cite

View full text Add to dashboard Cite

Given that solar radiation is unpredictable, an accurate solar energy prediction model must be developed. This study aimed to evaluate the changes in solar radiation over the past 37 years in Seoul city. The capability of the adaptive neuro-fuzzy inference system (ANFIS) to forecast solar radiation using chaotic time series inputs was analysed. Results demonstrate the capability of ANFIS to provide a relatively good monthly solar prediction model with a normalised root mean square error of 0.22%, a root mean square error of 55.4, and a coefficient of determination of 0.8. The Jarque-Bera test was implemented as well to test the null hypothesis for the normal distribution of standardised residual. Results support the null hypothesis with Pvalue = 0.222, which indicates the normal distribution of the standardised residual and its goodness. The standardised residual shows that the model can effectively predict solar radiation on a monthly basis.

show abstract

Solid waste forecasting using modified ANFIS modeling

Cited by 32 publications

References 33 publications

Environmental survival of SARS-CoV-2 – A solid waste perspective

Environmental survival of SARS-CoV-2 – A solid waste perspective

Forecasting Water Level Of Jhelum River Of Kashmir Valley India, Using Prediction And Earlywarning System

Evaluation and forecasting of solar radiation using time series adaptive neuro‐fuzzy inference system: Seoul city as a case study

Contact Info

Product

Resources

About