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Mining from Landfills as a Remediation Strategy Regarding Open Dumpsites Using Artificial Intelligence Hybrid Models


Affiliations
1 University Institute of Technology, Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal 462 033, Madhya Pradesh, India
2 Civil and Applied Mechanics Department, Shri G. S. Institute of Technology and Science, Indore 452 003, Madhya Pradesh, India
 

Due to the subsequent environmental effects of volatility, precise data on waste properties and their seasonal change are essential for sustainable waste management planning. As traditional waste characterization methods are time consuming and costly in most developing countries, it is necessary to approach the problem from a modelling perspective. Objective of this study was to identify the most efficient combinations of network architecture, activation function and formation strategy to reliably estimate the proportion of physical waste streams using meteorological parameters. The city of Gwalior is also affected by this global issue. The goal of this case study was to look at the potential and issues related to solid waste in Gwalior. Extensive investigations on the collection, transportation, treatment, storage, destruction, and disposal of solid waste generated in the city of Gwalior were done. Through interactions with people and website visits, GDS-related data is gathered. This study demonstrates that the city lacks a suitable system to deal with the solid waste generated, resulting in waste being dumped into vacant space, creating a number of issues for the local population as well as the environment. The three regions that make up the city of Gwalior are the city of Gwalior, Morar and Laskhar regions.

Keywords

Gwalior, Solid Waste, Waste Characterization, Waste Streams.
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  • Abraham A, Intelligent systems: Architectures and perspectives, Rece Adv Intell Paradig Appl, (2003) 1–35.
  • Abraham A, Meta learning evolutionary artificial neural networks, Neurocomputing, 56 (2004) 1–38.
  • Abraham A, Hybrid intelligent systems: evolving intelligence in hierarchical layers. Do smart adaptive systems exist? Best practice for selection and combination of intelligent methods, (2005) 159–179.
  • Abrahamsson P, Salo O, Ronkainen J & Warsta J, Agile, Software development methods: Review and analysis, arXiv preprint arXiv:1709.08439 (2017), https://doi.org/10.48550/arXiv.1709.08439.
  • Abrishami S, Goulding J S, Pour-Rahimian P & Ganah A, Integration of BIM and generative design to exploit AEC conceptual design innovation, J Inf Technol Constr, 19(1) (2014) 350–359.
  • Adachi Y, Overview of partial model query language, Proc ISPE Conf Concurrent Eng, (2003) 549–555.
  • Nandwana R & Chhipa R C, Impact of solid waste disposal on ground water quality in different disposal site at Jaipur, India, Int J Eng Sci Res Technol, 3 (2014) 93–101.
  • Bundela, P S, Gautam S P, Pandey A K, Awasthi M K & Sarsaiya, S, Municipal solid waste management in Indian cities–review, Int J Environ Sci, 1(4) 591–606.
  • Cobb C E & Ruckstuhl, K, Mining and reclaiming existing sanitary landfills, In Proc National Waste Process Conf (Detroit, MI, USA) 1988, 145–151.
  • Kumar S, Bhattacharyya J K, Vaidya A N, Chakrabarti T, Devotta S & Akolkar A B, Assessment of the status of municipal solid waste management in metro cities, state capitals, class I cities, and class II towns in India: An insight, Waste Manag, 29(2) (2009) 883–895.
  • Kurian J, Esakku S, Palanivelu K & Selvam A, Studies on landfill mining at solid waste dumpsites in India. In Proc Sardinia, 3 (2003) 248–255.
  • Singh C R & Dey M, Surface Water Quality with respect to municipal solid waste disposal within the Imphal municipality area, Manipur, Magnesium (mg/l), 3 (2.15) 0–7.
  • Abbasi M, Hanandeh E A, Forecasting municipal solid waste generation using artificial intelligence modelling approaches, Waste Manag, 56 (2016) 13–22.
  • Kolekar K A, Hazra T, Chakrabarty S N, A review on prediction of municipal solid waste generation models, Procedia Environ Sci, 35 (2016) 238–244.
  • Soni U, Roy A, Verma A & Jain V, Forecasting municipal solid waste generation using artificial intelligence models—A case study in India, SN Appl Sci, 1 (2019) 1–10.
  • Sayyahi F, Farzin S & Karami H, Forecasting daily and monthly reference evapo transpiration in the Aidoghmoush basin using multi layer perceptron coupled with water wave optimization, Complexity, 2021, 1–2.
  • Noori R, Abdoli M A, Ghazizade M J & Samieifard R, Comparison of neural network and principal component-regression analysis to predict the solid waste generation in Tehran, Iran J Public Health, 38(1) (2009) 74–84.
  • Jalali G Z M & Nouri R E, Prediction of municipal solid waste generation by use of artificial neural network: A case study of Mashhad, Int J Environ Res, (2) (2008) 13–22.
  • Yaghini M, Khoshraftar M M & Fallahi M, A hybrid algorithm for artificial neural network training, Eng Appl Artif Intell, 26 (1) (2013) 293–301.
  • Bahrami M, Akbari M, Bagherzadeh S A, Karimipour A, Afrand M & Goodarzi M, Develop 24 dissimilar ANNs by suitable architectures & training algorithms via sensitivity analysis to better statistical presentation: Measure MSEs between targets & ANN for Fe–CuO/Eg–Water nanofluid, Phys A: Stat Mech Appl, 519 (2019) 159–168.

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  • Mining from Landfills as a Remediation Strategy Regarding Open Dumpsites Using Artificial Intelligence Hybrid Models

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Authors

Ramkishore Shukla
University Institute of Technology, Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal 462 033, Madhya Pradesh, India
Sudhir Singh Bhadauria
University Institute of Technology, Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal 462 033, Madhya Pradesh, India
R K Shrivastava
Civil and Applied Mechanics Department, Shri G. S. Institute of Technology and Science, Indore 452 003, Madhya Pradesh, India

Abstract


Due to the subsequent environmental effects of volatility, precise data on waste properties and their seasonal change are essential for sustainable waste management planning. As traditional waste characterization methods are time consuming and costly in most developing countries, it is necessary to approach the problem from a modelling perspective. Objective of this study was to identify the most efficient combinations of network architecture, activation function and formation strategy to reliably estimate the proportion of physical waste streams using meteorological parameters. The city of Gwalior is also affected by this global issue. The goal of this case study was to look at the potential and issues related to solid waste in Gwalior. Extensive investigations on the collection, transportation, treatment, storage, destruction, and disposal of solid waste generated in the city of Gwalior were done. Through interactions with people and website visits, GDS-related data is gathered. This study demonstrates that the city lacks a suitable system to deal with the solid waste generated, resulting in waste being dumped into vacant space, creating a number of issues for the local population as well as the environment. The three regions that make up the city of Gwalior are the city of Gwalior, Morar and Laskhar regions.

Keywords


Gwalior, Solid Waste, Waste Characterization, Waste Streams.

References