Powiadomienia systemowe
- Sesja wygasła!
Tytuł artykułu
Treść / Zawartość
Pełne teksty:
Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
The rapid growth of smart cities and industry causes an increase in waste production. The amount of municipal solid waste (MSW) increases by several factors, including population growth, economic status, and consumption trends. The inadequacy of basic trash data is a major issue for managing MSW. Numerous existing models based on solid waste prediction have been presented so far, but none of them predict solid waste accurately and also it consumes more time. To address these concerns, a deep convolutional spiking neural network for solid waste prediction (DCSNN-SWP) is proposed in this paper. Here, the real-time solid waste prediction data are gathered from the quantity of municipal corporation of Chennai (MCC), landfill, garden garbage, and coconut shell reports in Tamil Nadu (Chennai), such as Zone 9 (Nungambakkam), Zone 10 (Kodambakkam) and Zone 13 (Adyar). Then the collected solid waste data are pre-processed using the kernel correlation model. Then the pre-processing data is given to DCSNN-hybrid BCMO and Archimedes optimization algorithm which accurately predicts the solid waste as wet waste, dry waste, horticulture waste, and dumping yard for 2022-2032 years. The proposed DCSNN-SWP method has been implemented in Python.
Czasopismo
Rocznik
Tom
Strony
5--25
Opis fizyczny
Bibliogr. 32 poz., rys., tab.
Twórcy
autor
- Department of Computer Science Engineering, Sri Sairam Institute of Technology, Chennai, Tamil Nadu, India
- Department of Computer Science and Engineering, Anna University Regional Campus, Madurai, Tamil Nadu, India
autor
- Department of Computer Science Engineering, Sri Sairam Institute of Technology, Chennai, Tamil Nadu, India
Bibliografia
- [1] NGUYEN X.C., NGUYEN T.T.H., LA D.D., KUMAR G., RENE E.R., NGUYEN D.D., CHANG S.W., CHUNG W.J., NGUYEN X.H., NGUYEN V.K., Development of machine learning-based models to forecast solid waste generation in residential areas. A case study from Vietnam, Res. Cons. Rec., 2021, 167, 105381. DOI: 10.1016/j.resconrec.2020.105381.
- [2] GUO H.N., WU S.B., TIAN Y.J., ZHANG J., LIU H.T., Application of machine learning methods for the prediction of organic solid waste treatment and recycling processes. A review, Biores. Techn., 2021, 319, 124114. DOI: 10.1016/j.biortech.2020.124114.
- [3] GOLBAZ S., NABIZADEH R., SAJADI H.S., Comparative study of predicting hospital solid waste generation using multiple linear regression and artificial intelligence, J. Environ. Health Sci. Eng., 2019, 17, 41–51. DOI: 10.1007/s40201-018-00324-z.
- [4] CHANDRAN J.J.G., JABEZ J., SRINIVASULU S., Autometric graph neural network optimized with capuchin search optimization algorithm for coinciding diabetic retinopathy and diabetic macular edema grading, Biomed. Sign. Proc. Control., 2023, 80, 104386. DOI: 10.1016/j.bspc.2022.104386.
- [5] ABDALLAH M., TALIB M.A., FEROZ S., NASIR Q., ABDALLA H., MAHFOOD B., Artificial intelligence applications in solid waste management. A systematic research review, Waste Manage. Res., 2020, 109, 231–246. DOI: 10.1016/j.wasman.2020.04.057.
- [6] PRAKASH T.S., PATNAYAKUNI S.P., SHIBU S., Municipal solid waste prediction using tree hierarchical deep convolutional neural network optimized with balancing composite motion optimization algorithm, J. Exp. Theor. Artif. In., 2023, 1–21. DOI: 10.1080/0952813X.2023.2243277.
- [7] SHAJIN F.H., RAJESH P., NAGOJI RAO V.K., Efficient framework for brain tumour classification using hierarchical deep learning neural network classifier, Comp. Meth. Biomech. Biomed. Eng. Imag. Vis., 2023, 11 (3), 750–757. DOI: 10.1080/21681163.2022.2111719.
- [8] AHMAD S., KIM D.H., Quantum GIS based descriptive and predictive data analysis for effective planning of waste management, IEEE Access, 2020, 8, 46193–46205. DOI: 10.1109/ACCESS.2020.2979015.
- [9] YANG L., NGUYEN H., BUI X.N., NGUYEN-THOI T., ZHOU J., HUANG J., Prediction of gas yield produced by energy recovery from municipal solid waste utilizing deep neural network and moth-flame optimi-zation algorithm, J. Clean. Prod., 2021, 311, 127672. DOI: 10.1016/j.jclepro.2021.127672.
- [10] SHAJIN F.H., RAJESH P., RAJA M.R., An efficient VLSI architecture for fast motion estimation exploiting zero motion prejudgment technique and a new quadrant-based search algorithm in HEVC, J. Circ. Syst. Signal Proc., 2022, 41, 1751–1774. DOI: 10.1007/s00034-021-01850-2.
- [11] RAMKUMAR M., MANJUNATHAN A., PRADEEP D., RAMESH R., Deep convolutional neural network optimized with hybrid marine predator’s and nomadic people optimization for cardiac arrhythmia classification using ECG signals, Biomed. Sign. Proc. Control., 2023, 86, 105157. DOI: 10.1016/j.bspc. 2023.105157.
- [12] RAJESH P., SHAJIN F.H., KUMARAN G.K., An efficient IWOLRS Control technique of brushless DC motor for torque ripple minimization, Appl. Sci. Eng. Prog., 2022, 15 (3), 5514. DOI: 10.14416/j.asep.2021. 10.004.
- [13] KULKARNI B.N., ANANTHARAMA V., Repercussions of COVID-19 pandemic on municipal solid waste management. Challenges and opportunities, Sci. Total Environ., 2020, 743, 140693. DOI: 10.1016 /j.scitotenv.2020.140693.
- [14] KHAN S., ANJUM R., RAZA S.T., BAZAI N.A., IHTISHAM M., Technologies for municipal solid waste management. Current status, challenges, and future perspectives, Chemosphere, 2022, 288, 132403. DOI: 10.1016/j.chemosphere.2021.132403.
- [15] VYAS S., PRAJAPATI P., SHAH A.V., VARJANI S., Municipal solid waste management. Dynamics, risk assessment, ecological influence, advancements, constraints and perspectives, Sci. Total Environ., 2022, 814, 152802. DOI: 10.1016/j.scitotenv.2021.152802.
- [16] RADA E.C., COVID-19 and municipal solid waste management, WIT Trans. Ecol. Environ., 2022, 257, 85–96. DOI: 10.1007/s11356-021-13914-6.
- [17] VANITHA V., ANAND S.J., RADHAMANI V., RAJALAKSHMI N.R., Dual-channel capsule generation adversarial network based blockchain technology for a secured dynamic optimal routing in mobile ad hoc network, Trans. Emerg. Tel. Tech., 2023, 34 (2), e4692. DOI: 10.1002/ett.4692.
- [18] HASHIM F.A., KHURMA R.A., ALBASHISH D., AMIN M., HUSSIEN A.G., Novel hybrid of AOA-BSA with double adaptive and random spare for global optimization and engineering problems, Alex. Eng. J., 2023, 73, 543–577. DOI: 10.1016/j.aej.2023.04.052.
- [19] PRAJAPATI P., VARJANI S., SINGHANIA R.R., PATEL A.K., AWASTHI M.K., SINDHU R., ZHANG Z., BINOD P., AWASTHI S.K., CHATURVEDI P., Critical review on technological advancements for effective waste management of municipal solid waste. Updates and way forward, Environ. Techn. Inn., 2021, 23, 101749. DOI: 10.1016/j.eti.2021.101749.
- [20] PENTEADO C.S.G., DE CASTRO M.A.S., Covid-19 effects on municipal solid waste management. What can effectively be done in the Brazilian scenario?, Res. Cons. Rec., 2021, 164, 105152. DOI: 10.1016 /j.resconrec.2020.105152.
- [21] YOUSEFLOO A., BABAZADEH R., Designing an integrated municipal solid waste management network. A case study, J. Clean. Prod., 2020, 244, 118824. DOI: 10.1016/j.jclepro.2019.118824.
- [22] XIAO S., DONG H., GENG Y., TIAN X., LIU C., LI H., Policy impacts on municipal solid waste management in Shanghai. A system dynamics model analysis, J. Clean. Prod., 2020, 262, 121366. DOI: 10.1016/j.jclepro.2020.121366.
- [23] LIU C., GONG J., ZHU J., ZHANG J., YAN Y., Correlation filter with motion detection for robust tracking of shape-deformed targets, IEEE Access, 2020, 8, 89161–89170. DOI: 10.1109/ACCESS.2020.2993777.
- [24] TURKSON R.E., QU H., MAWULI C.B., EGHAN M.J., Classification of Alzheimer’s disease using deep convolutional spiking neural network, Neural Proc. Lett., 2021, 53, 2649–2663. DOI: 10.1007 /s11063-021-10514-w.
- [25] DUTTA T., BHATTACHARYYA S., DEY S., PLATOS J., Border Collie optimization, IEEE Access, 2020, 8, 109177–109197. DOI: 10.1109/ACCESS.2020.2999540.
- [26] NIU D., WU F., DAI S., HE S., WU B., Detection of long-term effect in forecasting municipal solid waste using a long short-term memory neural network, J. Clean. Prod., 2021, 290, 125187. DOI: 10.1016 /j.jclepro.2020.125187.
- [27] LIN K., ZHAO Y., TIAN L., ZHAO C., ZHANG M., ZHOU T., Estimation of municipal solid waste amount based on one-dimension convolutional neural network and long short-term memory with attention mechanism model. A case study of Shanghai, Sci. Total Environ., 2021, 791, 148088. DOI; 10.1016 /j.scitotenv.2021.148088.
- [28] LIU B., ZHANG L., WANG Q., Demand gap analysis of municipal solid waste landfill in Beijing. Based on the municipal solid waste generation, Waste Manage. Res., 2021, 134, 42–51. DOI: 10.1016 /j.wasman. 2021.08.007.
- [29] ADELEKE O., AKINLABI S.A., JEN T.C., DUNMADE I., Prediction of municipal solid waste generation. An investigation of the effect of clustering techniques and parameters on ANFIS model performance, Environ. Techn., 2022, 43 (11), 1634–1647. DOI: 10.1080/09593330.2020.1845819.
- [30] AYELERU O.O., FAJIMI L.I., OBOIRIEN B.O., OLUBAMBI P.A., Forecasting municipal solid waste quantity using artificial neural network and support vector machine techniques. A case study of Johannesburg, South Africa, J. Clean. Prod., 2021, 289, 125671. DOI: 10.1016/j.jclepro.2020.125671.
- [31] LIANG G., PANAHI F., AHMED A.N., EHTERAM M., BAND S.S., ELSHAFIE A., Predicting municipal solid waste using a coupled artificial neural network with Archimedes optimisation algorithm and socioeconomic components, J. Clean. Prod., 2021, 315, 128039. DOI: 10.1016/j.jclepro.2021.128039.
- [32] DAI F., NIE G.H., CHEN Y., The municipal solid waste generation distribution prediction system based on FIG–GA-SVR model, J. Mater. Cycles Waste Manage., 2020, 22, 1352–1369. DOI: 10.1007/s10163-020 -01022-5.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-644c7d3f-9f8b-4f38-bbc5-6c696535f240