Abstract
Due to the rapid change in climate, visualization of environmental factors over a Geo-graphic map using machine learning techniques have become one of the active research area(s) of computer science. Since the trees are considered one of the biggest environmental cleaning contributing factor. Specially; tree plantation is one of the important problem around the globe to improve the environmental variables. For example, raise of temperature, impurity of water and many other problems are associated with the huge number of tree cuts and replantation of trees as per requirements will help to combat the ecological problems in upper Sindh, Pakistan. This paper attempts to get attention of concerned stockholders such as (government, NGOs (Non-Government organizations) and so on) to properly formulate the policies for replantation of trees and to reestablish the ecological system. In-order to address all above stated problems, this paper proposes a system for the restoration of environmental factors so called “Decision Support System for Visualization of Tree Plantation in Upper Sindh” which offers a forecasting system using machine learning techniques and visualization of results over a geographic map using Q-GIS (Geographic information system). This paper contributes three contributions (1) a methodology to fill the missing values by constructing the decision model on historical data (2) forecasting of critical regions for tree plantation on geographic information system and (3) overall best accuracy for tree plantation problem which is estimated as 96.53% with 10-k fold cross validation.
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References
Mark, J.: The genomics of climate change. Science 359(6371), 29–30 (2018)
Jingyi, H.: The location- and scale- specific correlation between temperature and soil carbon sequestration across the globe. Sci. Total Environ. 615(15), 540–548 (2018)
Yan, Y.: Integrate carbon dynamic models in analyzing carbon sequestration impact of forest biomass harvest. Sci. Total Environ. 615(15), 581–587 (2018)
Mal, S., Singh, R.B., Huggel, C., Grover, A.: Introducing linkages between climate change, extreme events, and disaster risk reduction. In: Mal, S., Singh, R.B., Huggel, C. (eds.) Climate Change, Extreme Events and Disaster Risk Reduction. SDGS, pp. 1–14. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-56469-2_1
Chen, R., Zhang, Y., Xu, D., Liu, M.: Climate change and coastal megacities: disaster risk assessment and responses in Shanghai city. In: Mal, S., Singh, R.B., Huggel, C. (eds.) Climate Change, Extreme Events and Disaster Risk Reduction. SDGS, pp. 203–216. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-56469-2_14
Allen, S.K.: Glacial lake outburst flood risk in Himachal Pradesh, India: an integrative and anticipatory approach considering current and future threats. Nat. Hazards 84(3), 1741–1763 (2017)
Dickinson, C.: Scientists agree changes in the working practices, including publishing in DRR needed to support the implementation of Sendai framework. NAM Today LV 2, 5–7 (2016)
Poterie, A.T.: From Yokohama to Sendai: approaches to participation in international disaster risk reduction frameworks. Int. J. Disaster Risk Reduct. 6, 128–129 (2015)
Kelman, I.: Climate change and the Sendai framework for disaster risk reduction. Int. J. Disaster Risk Reduct. 6, 117–127 (2015)
Rogelj, J.: Paris agreement climate proposals need a boost to keep warming well below 2° C. Nature 534, 631–639 (2016)
Schickhoff, U., Singh, R.B., Mal, S.: Climate change and dynamics of glaciers and vegetation in the himalaya: an overview. In: Singh, R.B., Schickhoff, U., Mal, S. (eds.) Climate Change, Glacier Response, and Vegetation Dynamics in the Himalaya, pp. 1–26. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-28977-9_1
Xu, J.: The melting Himalayas: cascading effects of climate change on water, biodiversity, and livelihoods. Conserv. Biol. 23(3), 520–530 (2016)
Zolotov, D.V., Chernykh, D.V., Biryukov, R.Yu., Pershin, D.K.: Changes in the activity of higher vascular plants species in the Ob Plateau landscapes (Altai Krai, Russia) due to anthropogenic transformation. In: Mal, S., Singh, R.B., Huggel, C. (eds.) Climate Change, Extreme Events and Disaster Risk Reduction. SDGS, pp. 147–157. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-56469-2_10
Michael, H., et al.: The wood from the trees: the use of timber in construction. Renew. Sustain. Energy Rev. 68, 333–359 (2017)
Crowther, T.W.: Predicting global forest reforestation potential. bioRxiv preprint first posted online 4, 1–13 (2009)
Habibullah, U.: Deforestation analysis of riverine forest of sindh using remote sensing techniques. Mehran Univ. Res. J. Eng. Technol. 30(3), 477–482 (2011)
Benewinde, J.-B.: Multi-temporal landsat images and ancillary data for land use/cover change (LULCC) detection in the Southwest of Burkina Faso, West Africa. Remote Sens. 7, 12076–12102 (2015)
Acknowledgement
This research is conducted at Department of Computer Science, Shah Abdul Latif University, Khairpur, Pakistan and we would like to say thanks to our domain experts who guided to label the training/testing datasets.
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Chandio, J.A., Malah, G.A., Kashif, U.a., Solangi, Y.A., Jameel, A. (2019). Decision Support System for Visualization of Tree Plantation in Upper Sindh. In: Bajwa, I., Kamareddine, F., Costa, A. (eds) Intelligent Technologies and Applications. INTAP 2018. Communications in Computer and Information Science, vol 932. Springer, Singapore. https://doi.org/10.1007/978-981-13-6052-7_55
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DOI: https://doi.org/10.1007/978-981-13-6052-7_55
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