Artificial neural network and multi-criteria decision-making methods for the remediation of soil oil pollution in the southwest of Iran
Tóm tắt
Oil pollution has notable effects on soil properties. There are different methods available to remove and decrease oil spill in soil such as the single or the combined use of physical, chemical, and biological methods. The main challenge in the remediation of oil-polluted soils is to find the most efficient methods to remove the contamination. The use of artificial intelligence such as artificial neural network (ANN) and multi-criteria decision-making (MCDM) methods, such as technique for order of preference by similarity to ideal solution (TOPSIS) and fuzzy methods, may be a useful way to investigate the efficiency of remediation methods. Accordingly, the objective of the present study was to find the most efficient method for the remediation of soil oil spill using ANN, TOPSIS, and fuzzy method. The study was carried out in an oil pumping facility in the southwest of Iran in 2017. Soil samples were collected from seven regions around the study area and their physicochemical properties were determined. The main criteria for ranking different methods were on the basis of intuitive methods. A radial basis function (RBF) type of ANN was used. The biological extraction method was the most efficient one. There was a perfect consistency between RBF outputs and the prioritized results by TOPSIS. In addition, a comparison of the results with fuzzy method revealed that ANN (r = 0.931 and MSE = 0.82) leads faster to a decision-making strategy for the remediation of polluted soils. However, using the network for making a decision requires a complete dataset.
Tài liệu tham khảo
Agbogidi OM, Eruotor PG, Akparobi SO, Nnaji GU (2007) Heavy metal contents of maize (Zea mays L.) grown in soil contaminated with crude oil. Int J Bot 3:385–389
Andretta M, Serra R, Villani M (2006) A new model for polluted soil risk assessment. Comput Geosci 32:890–896
Bekins BA, Cozzarelli IM, Erickson ML, Steenson RA, Thorn KA (2016) Crude oil metabolites in groundwater at two spill sites. Groundwater 54:681–691
Bodyanskiy YV, Tyshchenko AK, Deineko AA (2015) An evolving radial basis neural network with adaptive learning of its parameters and architecture. Autom Control Comput Sci 49:255–260
Croat SJ, O’Brien PL, Gasch CK, Casey FX, DeSutter TM (2020) Crop production on heavily disturbed soils following crude oil remediation. Agron J 112:130–138
Duarte FHO, Resende Filho LWD, Azpúrua H, Santos AA, Souza JR, Pessin G, Pabón REC (2021) Contaminated soil detection: a proposal using machine learning and hyperspectral imaging. International conference on engineering applications of neural networks. Springer, Cham, pp 377–388
Ghorbani B, Ziabasharhagh M, Amidpour M (2014) A hybrid artificial neural network and genetic algorithm for predicting viscosity of Iranian crude oils. J Nat Gas Sci Eng 18:312–323
Ginsberg GL, Pullen Fedinick K, Solomon GM, Elliott KC, Vandenberg JJ, Barone S Jr, Bucher JR (2019) New toxicology tools and the emerging paradigm shift in environmental health decision-making. Environ Health Perspect 127:125002
Gordon G, Stavi I, Shavit U, Rosenzweig R (2018) Oil spill effects on soil hydrophobicity and related properties in a hyper-arid region. Geoderma 312:114–120
Gozzi C, Filzmoser P, Buccianti A, Vaselli O, Nisi B (2019) Statistical methods for the geochemical characterisation of surface waters: the case study of the Tiber River basin (Central Italy). Comput Geosci 131:80–88
Jing L, Chen B, Zhang B (2014) Modeling of UV-induced photodegradation of naphthalene in marine oily wastewater by artificial neural networks. Water Air Soil Pollut 225:1906
Kang CU, Kim DH, Khan MA, Kumar R, Ji SE, Choi KW, Paeng KJ, Park S, Jeon BH (2020) Pyrolytic remediation of crude oil-contaminated soil. Sci Total Environ 713:136498
Korb KB, Nicholson AE (2010) Bayesian artificial intelligence. CRC Press, London
Li YP, Huang GH, Huang YF, Zhou HD (2009) A multistage fuzzy-stochastic programming model for supporting sustainable water-resources allocation and management. Environ Model Softw 24:786–797
Lima AR, Cannon AJ, Hsieh WW (2013) Nonlinear regression in environmental sciences by support vector machines combined with evolutionary strategy. Comput Geosci 50:136–144
Mamdani EH, Assilian S (1999) An experiment in linguistic synthesis with a fuzzy logic controller. Int J Hum Comput 51:135–147
Miransari M, Bahrami HA, Rejali F, Malakouti MJ (2008) Using arbuscular mycorrhiza to alleviate the stress of soil compaction on wheat (Triticum aestivum L.) growth. Soil Biol Biochem 40:1197–1206
Mohammadi F, Samaei MR, Azhdarpoor A, Teiri H, Badeenezhad A, Rostami S (2019) Modelling and optimizing pyrene removal from the soil by phytoremediation using response surface methodology, artificial neural networks, and genetic algorithm. Chemosphere 237:124486
Noubactep C, Caré S, Crane R (2012) Nanoscale metallic iron for environmental remediation: prospects and limitations. Water Air Soil Pollut 223:1363–1382
Olawoyin R, Nieto A, Grayson RL, Hardisty F, Oyewole S (2013) Application of artificial neural network (ANN)–self-organizing map (SOM) for the categorization of water, soil and sediment quality in petrochemical regions. Expert Syst Appl 40:3634–3648
Raj AS, Srinivas Y, Damodharan R, Oliver DH, Viswanath J (2021) Presentation of neurofuzzy optimally weighted sampling model for geoelectrical data inversion. Model Earth Syst Environ 7:1927–1938
Rouzkhosh M, Jaafarzadeh N, Varshosaz K, Orak N, Dashti S (2023) The emission of greenhouse gases from flare gas condensates of petroleum units and the climatic index of Emberger in southern Iran. Pet Sci Technol 41:1099–1112
Sadeghi A, Ataabadi M, Abolhasani MH (2021) Chromium removal from a contaminated soil using nano zero-valent iron and magnetite affected by temperature and moisture. Soil Sediment Contam 30:610–621
Talvenmäki H, Saartama N, Haukka A, Lepikkö K, Pajunen V, Punkari M, Yan G, Sinkkonen A, Piepponen T, Silvennoinen H, Romantschuk M (2021) In situ bioremediation of Fenton’s reaction–treated oil spill site, with a soil inoculum, slow release additives, and methyl-β-cyclodextrin. Environ Sci Pollut Res 28:20273–20289
Vijayaraghavan V, Lau EV, Goyal A, Niu X, Garg A, Gao L (2019) Design of explicit models for predicting the efficiency of heavy oil-sand detachment process by floatation technology. Measurement 137:122–129
Wang B, Xie HL, Ren HY, Li X, Chen L, Wu BC (2019) Application of AHP, TOPSIS, and TFNs to plant selection for phytoremediation of petroleum-contaminated soils in shale gas and oil fields. J Clean Prod 233:13–22
Wu M, Dick WA, Li W, Wang X, Yang Q, Wang T, Xu L, Zhang M, Chen L (2016) Bioaugmentation and biostimulation of hydrocarbon degradation and the microbial community in a petroleum-contaminated soil. Int Biodeter Biodegr 107:158–164
Yang L (2021) Marine oil pollution remediation and enterprise capital efficiency based on improved neural network. Arab J Geosci 14:1–14
Yang Z, Chen Z, Lee K, Owens E, Boufadel MC, An C, Taylor E (2021) Decision support tools for oil spill response (OSR-DSTs): approaches, challenges, and future research perspectives. Mar Pollut Bull 167:112313
Yemashova NA, Murygina VP, Zhukov DV, Zakharyantz AA, Gladchenko MA, Appanna V, Kalyuzhnyi SV (2007) Biodeterioration of crude oil and oil derived products: a review. Rev Environ Sci Biotechnol 6:315–337