983
Views
1
CrossRef citations to date
0
Altmetric
Research Article

Prediction of land use and land cover change in two watersheds in the Senegal River basin (West Africa) using the Multilayer Perceptron and Markov chain model

, , , , , ORCID Icon, , & show all
Article: 2231137 | Received 21 Jan 2023, Accepted 26 Jun 2023, Published online: 06 Jul 2023

References

  • Amini, S., Saber, M., Rabiei Dastjerdi, H., & Homayouni, S. (2022). Urban land use and land cover change analysis using random forest classification of Landsat time series. Remote Sensing, 14(11), 1–18. https://doi.org/10.3390/rs14112654
  • Andrieu, J. (2018). Land cover changes on the West-African coastline from the Saloum Delta (Senegal) to Rio Geba (Guinea-Bissau) between 1979 and 2015 land cover changes on the West-African coastline from the Saloum Delta. European Journal of Remote Sensing, 51(1), 314–325. https://doi.org/10.1080/22797254.2018.1432295
  • Anwar, Z., Alam, A., Elahi, N., & Shah, I. (2022). Assessing the trends and drivers of land use land cover change in district Abbottabad lower Himalayan Region Pakistan. Geocarto International, 37(25), 10855–10870. https://doi.org/10.1080/10106049.2022.2040604
  • Assede, E., Orou, H., Biaou, S., Geldenhuys, C., Ahononga, F., & Chirwa, P. Understanding drivers of land use and land cover change in Africa: A review. (2023). Landscape Ecol Rep, 8(2), 62–72. (2023), 4(1), 88–100. https://doi.org/10.1007/s40823-023-00087-w
  • Atteyoub, M., & Camara, M. (2020). Impact socioeconomique de l ’ orpaillage dans le socio-economic impact of gold panning in the kadiolo. Revue Malienne de Science et de Technologie, 01(24), 91–103.
  • Azari, M., Billa, L., & Chan, A. (2022). Multi-temporal analysis of past and future land cover change in the highly urbanized state of Selangor, Malaysia. Ecological Processes, 11(1). https://doi.org/10.1186/s13717-021-00350-0
  • Bader, J. C. (2001). Programme d’optimisation de la gestion des réservoirs : manuel de gestion du barrage de Diama : version finale.
  • Barnieh, B. N. A. I. N. A., Jia, L., Menenti, M., & Zhou, J. (2020). Mapping land use land cover transitions at different Spatiotemporal scales in West Africa. Sustainability 2020. https://doi.org/10.3390/su12208565
  • Berihun, M. L., Tsunekawa, A., Haregeweyn, N., Meshesha, D. T., Adgo, E., Tsubo, M., Masunaga, T., Fenta, A. A., Sultan, D., & Yibeltal, M. (2019). Exploring land use/land cover changes, drivers and their implications in contrasting agro-ecological environments of Ethiopia. Land Use Policy, 87(May), 104052. https://doi.org/10.1016/j.landusepol.2019.104052
  • Bodian, A., Diop, L., Panthou, G., Dacosta, H., Deme, A., Dezetter, A., Ndiaye, P. M., Diouf, I., & Visch, T. (2020). Recent trend in hydroclimatic conditions in the Senegal River basin. Water (Switzerland), 12(2), 1–12. https://doi.org/10.3390/w12020436
  • Bohbot, J. (2017). L’orpaillage au Burkina Faso : une aubaine économique pour les populations, aux conséquences sociales et environnementales mal maîtrisées. EchoGéo, 42(42), 15150. https://doi.org/10.4000/echogeo.15150
  • Breiman, L. (2001). Random Forests. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence & Lecture Notes in Bioinformatics). https://doi.org/10.1007/978-3-030-62008-0_35
  • Cabral, A. I. R., & Lagos, F. (2017). Land cover changes and landscape pattern dynamics in Senegal and Guinea Bissau borderland. Applied Geography, 82, 115–128. https://doi.org/10.1016/j.apgeog.2017.03.010
  • Chang, Y., Hou, K., Li, X., Zhang, Y., & Chen, P. (2018). Review of land use and land cover change research progress. IOP Conference Series: Earth and Environmental Science, 113(1). https://doi.org/10.1088/1755-1315/113/1/012087
  • Chinwendu, O. G. (2019). Modeling the hydrological response of the dano catchment, in the volta basin to land use land cover, (B. Sc. Water Resources Management and Agromeotrology, M. Sc. Climate Change and Adapted Land use), Kwame Nkrumah University of science, A thesi.
  • Descroix, L., Faty, B., Manga, S. P., Diedhiou, A. B., Lambert, L. A., Soumaré, S., Andrieu, J., Ogilvie, A., Fall, A., Mahé, G., Diallo, F. B. S., Diallo, A., Diallo, K., Albergel, J., Tanimoun, B. A., Amadou, I., Bader, J. C., Barry, A., Bodian, A. … Vandervaere, J. P. (2020). Are the fouta djallon highlands still the water tower of west africa? Water (Switzerland), 12(11), 2968. https://doi.org/10.3390/w12112968
  • Diallo, B. A., & Zhengyu, B. A. O. (2018). Land cover change assessment using remote sensing: Case study of Bamako, land cover change assessment using remote sensing: Case study of Bamako, mali. Researcher, 2(4), 7–17.
  • Diop, L., Bodian, A., & Diallo, D. (2016). Spatiotemporal trend analysis of the mean annual rainfall in Senegal. European Scientific Journal, ESJ, 12(12), 231. https://doi.org/10.19044/esj.2016.v12n12p231
  • Doucouré, B. (2015). Des pierres dans les mortiers et non du maïs! Mutations dans les villages aurifères du sud-est du Sénégal.
  • Dubertret, F., Villarreal, M. L., Tourneau, F. L., Dubertret, F., An-, L. N. M., Villarreal, M., Tourneau, F. L., Tourneau, F. L., Villarreal, M., & Dubertret, F. (2022). Monitoring annual land use/land cover change in the Tucson metropolitan area with google earth engine (1986–2020). Remote Sensing, 14(9), 2127. https://doi.org/10.3390/rs14092127
  • Fathizad, H., Rostami, N., & Faramarzi, M. (2015). Detection and prediction of land cover changes using Markov chain model in semi-arid rangeland in western Iran. Environmental Monitoring and Assessment, 187(10). https://doi.org/10.1007/s10661-015-4805-y
  • Faty, A. (2017). Modelisation hydrologique du haut bassin versant du fleuve senegal dans un contexte de variabilite hydro-climatique : Apport de la télédétection et du modèle Mike SHE. Université Cheikh Anta Diop de Dakar.
  • Fikadu, G., & Olika, G. (2023). Impact of land use land cover change using remote sensing with integration of socio-economic data on rural livelihoods in the Nashe watershed, Ethiopia. Heliyon, 9(3), e13746. https://doi.org/10.1016/j.heliyon.2023.e13746
  • Foody Giles, M. (2022). Land cover classification accuracy assessment. Springer Geography, 80, 105–118. https://doi.org/10.1007/978-981-16-5149-6_6
  • Gaur, S., Mittal, A., Bandyopadhyay, A., Holman, I., & Singh, R. (2020). Spatio-temporal analysis of land use and land cover change: A systematic model inter-comparison driven by integrated modelling techniques. International Journal of Remote Sensing, 41(23), 9229–9255. https://doi.org/10.1080/01431161.2020.1815890
  • Girma, R., Fürst, C., & Moges, A. (2022). Land use land cover change modeling by integrating artificial neural network with cellular Automata-Markov chain model in Gidabo river basin, main Ethiopian rift. Environmental Challenges, 6( August 2021), 100419. https://doi.org/10.1016/j.envc.2021.100419
  • Gislason, P. O., Benediktsson, J. A., & Sveinsson, J. R. (2006). Random forests for land cover classification. Pattern Recognition Letters, 27(4), 294–300. https://doi.org/10.1016/j.patrec.2005.08.011
  • Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27. https://doi.org/10.1016/j.rse.2017.06.031
  • Herrmann, S. M., Brandt, M., Rasmussen, K., & Fensholt, R. (2020). Accelerating land cover change in West Africa over four decades as population pressure increased. Communications Earth & Environment, 1(1), 1–10. https://doi.org/10.1038/s43247-020-00053-y
  • Hewitt, R., van Delden, H., & Escobar, F. (2014). Participatory land use modelling, pathways to an integrated approach. Environmental Modelling and Software, 52, 149–165. https://doi.org/10.1016/j.envsoft.2013.10.019
  • Horning, N. (2004). Land cover classification methods.
  • Jampani, M., Amerasinghe, P., Liedl, R., Locher-Krause, K., & Hülsmann, S. (2020). Multi-functionality and land use dynamics in a peri-urban environment influenced by wastewater irrigation. Sustainable Cities and Society, 62, 62. https://doi.org/10.1016/j.scs.2020.102305
  • Jean-Claude, B., Rolland, D., & Jean-Christophe, P. (2015). SIMULSEN : logiciel de simulation de gestion d'un barrage à objectifs multiples, au pas de temps journalier : manuel de référence et d'utilisation des versions DOS et Windows XP de décembre 2005 - Mise à jour avril 2015. 103.
  • Kabanza, A., Dondeyne, S., Tenga, J., Kimaro, D., Poesen, J., Kafiriti, E., & Deckers, J. (2013). More people, more trees in South Eastern Tanzania: Local and global drivers of land-use/cover changes. African Geographical Review, 32(1), 44–58. https://doi.org/10.1080/19376812.2012.746093
  • Kaku, D. U., Cao, Y., Al-Masnay, Y. A., & Nizeyimana, J. C. (2021). An integrated approach to assess the environmental impacts of large-scale gold mining: The nzema-gold mines in the ellembelle district of Ghana as a case study. International Journal of Environmental Research and Public Health, 18(13), 7044. https://doi.org/10.3390/ijerph18137044
  • Kulkarni, A. D., & Lowe, B. (2016). Random forest algorithm for land cover classification. International Journal on Recent and Innovation Trends in Computing and Communication, 4(3), 58–63. http://hdl.handle.net/10950/341
  • Kulkarni, K., & Vijaya, P. (2021). NDBI Based prediction of land use land cover change. Journal of the Indian Society of Remote Sensing, 49(10), 2523–2537. https://doi.org/10.1007/s12524-021-01411-9
  • Leta, M. K., Demissie, T. A., & Tränckner, J. (2021). Modeling and prediction of land use land cover change dynamics based on land change modeler (Lcm) in nashe watershed, upper blue nile basin, Ethiopia. Sustainability (Switzerland), 13(7), 3740. https://doi.org/10.3390/su13073740
  • Liping, C., Yujun, S., Saeed, S., & Westergaard-Nielsen, A. (2018). Monitoring and predicting land use and land cover changes using remote sensing and GIS techniques—A case study of a hilly area, Jiangle, China. PLoS ONE, 13(7), 1–23. https://doi.org/10.1371/journal.pone.0200493
  • Lu, D., & Weng, Q. (2007). A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing, 28(5), 823–870. https://doi.org/10.1080/01431160600746456
  • Macarringue, L. S., Bolfe, É. L., & Pereira, P. R. M. (2022). Developments in land use and land cover classification techniques in remote sensing: A review. Journal of Geographic Information System, 14(1), 1–28. https://doi.org/10.4236/jgis.2022.141001
  • Ma, L., Liu, Y., Zhang, X., Ye, Y., Yin, G., & Johnson, B. A. (2019). Deep learning in remote sensing applications: A meta-analysis and review. Isprs Journal of Photogrammetry & Remote Sensing, 152, 166–177. November 2018. https://doi.org/10.1016/j.isprsjprs.2019.04.015
  • Mas, J. F., & Flores, J. J. (2008). The application of artificial neural networks to the analysis of remotely sensed data. International Journal of Remote Sensing, 29(3), 617–663. https://doi.org/10.1080/01431160701352154
  • Mas, J., Kolb, M., Paegelow, M., Camacho, M. T., Houet, T., Mas, J., Kolb, M., Paegelow, M., Teresa, M., Olmedo, C., & Houet, T. (2014). Inductive pattern-based land use/cover change models : A comparison of four software packages. In Environmental Modelling & Software (pp. 94–111). Elsevier. https://doi.org/10.1016/j.envsoft.2013.09.010.hal-01187569HAL
  • Mekonnen, Y. A., & Manderso, T. M. (2023). Land use/land cover change impact on streamflow using Arc-SWAT model, in case of Fetam watershed, Abbay Basin, Ethiopia. Applied Water Science, 13(5), 1–19. https://doi.org/10.1007/s13201-023-01914-5
  • Ministere de l’economie et des finances. (2018). Rapport De L ’ Etude Monographique Sur L ’ Orpaillage Au. 48.
  • Mishra, V., Rai, P., & Mohan, K. (2014). Prediction of land use changes based on land change modeler (LCM) using remote sensing: A case study of Muzaffarpur (Bihar), India. Journal of the Geographical Institute Jovan Cvijic, SASA, 64(1), 111–127. https://doi.org/10.2298/ijgi1401111m
  • Mishra, V. N., Rai, P. K., Prasad, R., Punia, M., & Nistor, M. M. (2018). Prediction of spatio-temporal land use/land cover dynamics in rapidly developing Varanasi district of Uttar Pradesh, India, using geospatial approach: A comparison of hybrid models. Applied Geomatics, 10(3), 257–276. https://doi.org/10.1007/s12518-018-0223-5
  • Murgante, B., Misra, S., Rocha, A. M. A., Torre, C., Rocha, J. G., Falcao, M. I., Taniar, D., Apduhan, B. O., & Gervasi, O. (2014). Computational science and its applications.
  • Nasiri, V., Deljouei, A., Moradi, F., Sadeghi, S. M. M., & Borz, S. A. (2022). Land use and land cover mapping using sentinel-2, Landsat-8 satellite images, and google earth engine: a comparison of two composition methods. Remote Sensing, 14(9), 1977. https://doi.org/10.3390/rs14091977
  • Ndiaye, K. (2020). Impact Environnemental Et Sanitaire Dans Le Sud-Est Du Senegal : Exemple Du.
  • Noi Phan, T., Kuch, V., & Lehnert, L. W. (2020). Land cover classification using google earth engine and random forest classifier—the role of image composition. Remote Sensing, 12(15), 2411. https://doi.org/10.3390/RS12152411
  • Noszczyk, T. (2019). A review of approaches to land use changes modeling. Human and Ecological Risk Assessment, 25(6), 1377–1405. https://doi.org/10.1080/10807039.2018.1468994
  • Nouaceur, Z., Murarescu, O., & Murătoreanu, G. (2020). Rainfall variability and trend analysis of multiannual. MS Binici and E Acs Water, 17(2), 124–144. https://doi.org/10.1515/avutgs-2017-0012
  • Olofsson, P., Foody, G. M., Herold, M., Stehman, S. V., Woodcock, C. E., & Wulder, M. A. (2014). Remote sensing of environment good practices for estimating area and assessing accuracy of land change. Remote Sensing of Environment, 148, 42–57. https://doi.org/10.1016/j.rse.2014.02.015
  • Ougahi, J. H., & Mahmood, S. A. (2022). Evaluation of satellite-based and reanalysis precipitation datasets by hydrologic simulation in the Chenab river basin. Journal of Water and Climate Change, 13(3), 1563–1582. https://doi.org/10.2166/wcc.2022.410
  • Pontius, R. G., & Batchu, K. (2003). Using the relative operating characteristic to quantify certainty in prediction of location of land cover change in India. Transactions in GIS, 7(4), 467–484. https://onlinelibrary.wiley.com/doi/10.1111/1467-9671.00159
  • Roland, Y. O. (2021). Dynamique spatio-temporelle des états de surface et influence sur le ruissellement sur un bassin de type sahélien : cas du bassin de Tougou (Nord Burkina Faso). https://doi.org/10.13140/RG.2.2.11834.82883
  • Sambou, M. H. A., Liersch, S., Koch, H., Vissin, E. W., Albergel, J., & Sane, M. L. (2023). Synergies and trade-offs in water resources management in the bafing watershed under climate change synergies and trade-offs in water resources management in the bafing watershed under climate change. M S Binici and E Acs Water, 15(11), 2067. https://doi.org/10.3390/w15112067
  • Sane, M. L., Sambou, S., Ndione, D. M., & Leye, I. (2020). Moussé Landing SANE et al. Analyse et traitement des séries de débits annuels et mensuels sur le fleuve sénégal. Rev. Ivoir. Sci. Technol, 30, 102–120. http://www.revist.ci
  • Sane, M. L., Sambou, S., Ndione, D. M., & Leye, I. (2017). Moussé Landing SANE et al. Analyse et traitement des séries de débits annuels et mensuels sur le fleuve sénégal. http://www.revist.ci
  • Sankarrao, L., Ghose, D. K., & Rathinsamy, M. (2021). Predicting land-use change: Intercomparison of different hybrid machine learning models. Environmental Modelling and Software, 145(September), 105207. https://doi.org/10.1016/j.envsoft.2021.105207
  • Shelestov, A., Lavreniuk, M., Kussul, N., Novikov, A., & Skakun, S. (2017). Exploring Google earth engine platform for big data processing: Classification of multi-temporal satellite imagery for crop mapping. Frontiers in Earth Science, 5(February), 1–10. https://doi.org/10.3389/feart.2017.00017
  • Silva, L. P. E., Xavier, A. P. C., da Silva, R. M., & Santos, C. A. G. (2020). Modeling land cover change based on an artificial neural network for a semiarid river basin in northeastern Brazil. Global Ecology and Conservation, 21, e00811. https://doi.org/10.1016/j.gecco.2019.e00811
  • Singh, S. K., Mustak, S., Srivastava, P. K., Szabó, S., & Islam, T. (2015). Predicting spatial and decadal lulc changes through cellular automata Markov chain models using earth observation datasets and geo-information. Environmental Processes, 2(1), 61–78. https://doi.org/10.1007/s40710-015-0062-x
  • Singh, V. G., Singh, S. K., Kumar, N., & Singh, R. P. (2022). Simulation of land use/land cover change at a basin scale using satellite data and Markov chain model. Geocarto International, 37(26), 11339–11364. https://doi.org/10.1080/10106049.2022.2052976
  • Solly, B., Dieye, E. H. B., Oumar, S. Y., Jarju, A. M., & Sane, T. (2021). Detection des zones de degradation et de regeneration de la couverture vegetale dans le sud du Senegal a travers l’analyse des tendances de series temporelles modis NDVI et des changements d’occupation des sols a partir d’images landsat. Revue Francaise de Photogrammetrie et de Teledetection, 223, 1–15. https://doi.org/10.52638/rfpt.2021.580
  • Stehman, S. V. (2014). Estimating area and map accuracy for stratified random sampling when the strata are different from the map classes. International Journal of Remote Sensing, 35(13), 4923–4939. https://doi.org/10.1080/01431161.2014.930207
  • Szantoi, Z., Jaffrain, G., Gallaun, H., Bielski, C., Ruf, K., Lupi, A., Miletich, P., Giroux, A.-C., Carlan, I., Croi, W., Augu, H., Kowalewski, C., & Brink, A. (2021). Quality assurance and assessment framework for land cover maps validation in the Copernicus hot spot monitoring activity. European Journal of Remote Sensing, 54(1), 538–557. https://doi.org/10.1080/22797254.2021.1978001
  • Tabutin, D., & Schoumaker, B. (2020). La démographie de l’Afrique subsaharienne au XXIe siècle. Population, 75(2), 169. https://doi.org/10.3917/popu.2002.0169
  • Tadese, S., Soromessa, T., & Bekele, T. (2021). Analysis of the Current and Future Prediction. Scientific World Journal. https://doi.org/10.1155/2021/6685045
  • Talukdar, S., Singha, P., Mahato, S., Pal, S., Pal, S., Liou, Y.-A., & Rahman, A. (2020). Land-use land-cover classification by machine learning classifiers for satellite observations — a review. Remote Sensing, 12(7), 1135. https://doi.org/10.3390/rs12071135
  • Thiam, S., Salas, E. A. L., Rholan, N., Delos, A., Almoradie, S., Verleysdonk, S., Adounkpe, J. G., & Komi, K. (2022). Modelling land use and land cover in the transboundary Mono River catchment of togo and Benin using Markov chain and stakeholder ’ s perspectives. Sustainability, 14(7), 4160. https://doi.org/10.3390/su14074160
  • Thiam, S., Villamor, G. B., Faye, L. C., Sène, J. H. B., Diwediga, B., & Kyei-Baffour, N. (2021). Monitoring land use and soil salinity changes in coastal landscape: A case study from Senegal. Environmental Monitoring and Assessment, 193(5). https://doi.org/10.1007/s10661-021-08958-7
  • Tiné, M., Perez, L., & Molowny-Horas, R. (2019). Hybrid spatiotemporal simulation of future changes in open wetlands: A study of the Abitibi-Témiscamingue region, Québec, Canada. International Journal of Applied Earth Observation and Geoinformation, 74, 302–313. October 2018. https://doi.org/10.1016/j.jag.2018.10.001
  • Traore, S. S., Dembele, S., Dembele, D., Diakite, N., & Diakite, C. H. (2022). Dynamique de l’occupation du sol et trajectoire du couvert végétal autour de trois sites miniers du Sud Mali entre 1988 et 2019. Physio-Géo, 17(Volume 17), 151–166. https://doi.org/10.4000/physio-geo.14565
  • Traore, A., Mawenda, J., & Komba, A. (2018). Land-cover change analysis and simulation in conakry (guinea), using hybrid cellular-automata and Markov model. Urban Science, 2(2), 1–16. https://doi.org/10.3390/urbansci2020039
  • Tsai, Y. H., Stow, D., Chen, H. L., Lewison, R., An, L., & Shi, L. (2018). Mapping vegetation and land use types in Fanjingshan national nature reserve using google earth engine. Remote Sensing, 10(6), 927. https://doi.org/10.3390/rs10060927
  • UCAD. (2019). Final report climate vulnerability and water resources variability in West Africa Senegal and Gambia River Basin Cases. 1–131.
  • Wahap, N. A., & Shafri, H. Z. M. (2020). Utilization of Google Earth Engine (GEE) for land cover monitoring over Klang Valley, Malaysia. IOP Conference Series: Earth and Environmental Science, 540(1). https://doi.org/10.1088/1755-1315/540/1/012003
  • Wang, S. W., Munkhnasan, L., & Lee, W. K. (2021). Land use and land cover change detection and prediction in Bhutan’s high altitude city of Thimphu, using cellular automata and Markov chain. Environmental Challenges, 2, 100017. November 2020 https://doi.org/10.1016/j.envc.2020.100017 https://doi.org/10.1016/j.envc.2020.100017
  • Winkler, K., Fuchs, R., Rounsevell, M., & Herold, M. (2019). Global land use changes are four times greater than previously estimated. Nature Communications, 2021(1), 1–10. https://doi.org/10.1038/s41467-021-22702-2
  • Woodcock, C. E., Allen, R., Anderson, M., Belward, A., Bindschadler, R., Cohen, W., Gao, F., Goward, S. N., Helder, D., Helmer, E., Nemani, R., Oreopoulos, L., Schott, J., Thenkabail, P. S., Vermote, E. F., Vogelmann, J., Wulder, M. A., & Wynne, R. (2008). Free access to Landsat imagery. Science: Advanced Materials and Devices, 320(5879), 1011. https://doi.org/10.1126/science.320.5879.1011a
  • Yang, L., Driscol, J., Sarigai, S., Wu, Q., Chen, H., & Lippitt, C. D. Google Earth Engine and Artificial Intelligence (AI): A Comprehensive Review. (2022). Remote Sensing, 14(14), 3253. 14(14). https://doi.org/10.3390/rs14143253
  • Yang, Y., Yang, D., Wang, X., Zhang, Z., & Nawaz, Z. (2021). Testing accuracy of land cover classification algorithms in the qilian mountains based on gee cloud platform. Remote Sensing, 13(24). https://doi.org/10.3390/rs13245064
  • Zadbagher, E., Becek, K., & Berberoglu, S. (2018). Modeling land use/land cover change using remote sensing and geographic information systems: Case study of the Seyhan Basin, Turkey. Environmental Monitoring and Assessment, 190(8). https://doi.org/10.1007/s10661-018-6877-y
  • Zurqani, H. A., Post, C. J., Mikhailova, E. A., Schlautman, M. A., & Sharp, J. L. (2018). Geospatial analysis of land use change in the Savannah River Basin using google earth engine. International Journal of Applied Earth Observation and Geoinformation, 69, 175–185. December 2017. https://doi.org/10.1016/j.jag.2017.12.006