Prediction of municipal solid waste quantities using a BiLSTM model, and analysis of biogas and electricity generation potential and greenhouse gas impacts: A case study of Şırnak province

dc.contributor.authorÜren, Ceylan
dc.contributor.authorTaşkesen, Edip
dc.date.accessioned2026-01-22T19:33:32Z
dc.date.issued2025
dc.departmentŞırnak Üniversitesi
dc.description.abstractThis study focuses on predicting the Municipal Solid Waste (MSW) quantities in Şırnak province for the 2025-2045 period using a BiLSTM deep learning model, and analyzes the related Biogas (methane gas, CH4), Electricity Energy Produciton Potential (EEPP), and Greenhouse Gas (GHG) emissions based on these predictions. The model was developed using a dataset of 12 financial, social, and demographic variables with an 80% training and 20% testing split, implemented in Python with the NumPy library. Trained on data from 2007 to 2024, the model achieved a low Mean Absolute Percentage Error (MAPE) of 3.83% after hyperparameter optimization, with an average MAPE of 7.99% from k-fold cross-validation. MAPE values below 10% indicate high accuracy and reliability. Findings suggest that the MSW amount, approximately 298,090 tons in 2025, will increase to around 825,929 tons by 2045, representing a 63.9% rise driven mainly by rapid urbanization, population growth, and economic development. Using LandGEM software, CH4 production potential was estimated at about 2.47 million m³ in 2025 and 6.86 million m3 in 2045. Assuming 75% effective CH4 collection, 1.85 million m³ and 5.14 million m³ of CH4 could be recovered in 2025 and 2045, respectively. Corresponding electricity generation potentials are 5,157 MWh in 2025 and 14,315 MWh in 2045, with a total of 225,478 MWh predicted over 21 years. The optimal required plant capacity is calculated as 1.63 MW. Additionally, approximately 141,375 tons of CO2 equivalent GHG emissions are expected to be avoided over this period. These results highlight the environmental benefits of replacing fossil fuels with biogas. The study concludes that BiLSTM based MSW predictions provide a reliable tool for waste management and energy planning, supporting sustainable environmental policies and strategic decision making.
dc.identifier.doi10.58559/ijes.1725711
dc.identifier.endpage676
dc.identifier.issn2717-7513
dc.identifier.issue3
dc.identifier.startpage647
dc.identifier.trdizinid1345636
dc.identifier.urihttps://doi.org/10.58559/ijes.1725711
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1345636
dc.identifier.urihttps://hdl.handle.net/11503/2755
dc.identifier.volume10
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.relation.ispartofInternational journal of energy studies (Online)
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_TR_20260122
dc.subjectDeep learning
dc.subjectEnvironment
dc.subjectEnergy
dc.subjectWaste
dc.subjectBiLSTM
dc.titlePrediction of municipal solid waste quantities using a BiLSTM model, and analysis of biogas and electricity generation potential and greenhouse gas impacts: A case study of Şırnak province
dc.typeArticle

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