Open Access
Issue |
BIO Web Conf.
Volume 144, 2024
1st International Graduate Conference on Smart Agriculture and Green Renewable Energy (SAGE-Grace 2024)
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Article Number | 03005 | |
Number of page(s) | 12 | |
Section | Environmental Monitoring and Water Management | |
DOI | https://doi.org/10.1051/bioconf/202414403005 | |
Published online | 25 November 2024 |
- Mott, A., Baba, A., Hadi Mosleh, M., Ökten, H.E., Babaei, M., Gören, A.Y., Feng, C., Recepoğlu, Y.K., Uzelli, T., Uytun, H., Morata, D., Yüksel, A., Sedighi, M.: Boron in geothermal energy: Sources, environmental impacts, and management in geothermal fluid. Renew. Sustain. Energy Rev. 167, (2022). [Google Scholar]
- Soltani, M., Moradi Kashkooli, F., Dehghani-Sanij, A.R., Nokhosteen, A., Ahmadi-Joughi, A., Gharali, K., Mahbaz, S.B., Dusseault, M.B.: A comprehensive review of geothermal energy evolution and development. Int. J. Green Energy. 16, 971 (2019). [CrossRef] [Google Scholar]
- Indriani, R.F., Utama, W., Anjasmara, I.M., Paramita, E.G.K., Nainggolan, R.A.O.: Comparative Analysis of Physiograpic Study for Hydrology of Benowo Region, Surabaya. In: IOP Conference Series: Earth and Environmental Science (2023). [Google Scholar]
- Tantama, E.E., Kumara, M.A., Putra, D.P.E., Marliyani, G.I.: Pattern and direction of groundwater flow and distribution of physical-chemical properties of groundwater in Randublatung basin. IOP Conf. Ser. Earth Environ. Sci. 930, (2021). [Google Scholar]
- Ma, Z., Wang, W., Hou, X., Wang, J., Duan, L., Wang, Y., Zhao, M., Li, J., Jing, J., Li, L.: Examining the change in groundwater flow patterns: A case study from the plain area of the Baiyangdian Lake Watershed, North China. J. Hydrol. 625, 130160 (2023). [CrossRef] [Google Scholar]
- Rusli, S.R., Weerts, A.H., Mustafa, S.M.T., Irawan, D.E., Taufiq, A., Bense, V.F.: Quantifying aquifer interaction using numerical groundwater flow model evaluated by environmental water tracer data: Application to the data-scarce area of the Bandung groundwater basin, West Java, Indonesia. J. Hydrol. Reg. Stud. 50, 101585 (2023). [CrossRef] [Google Scholar]
- Zhao, Z., Islam, F., Waseem, L.A., Tariq, A., Nawaz, M., Islam, I.U., Bibi, T., Rehman, N.U., Ahmad, W., Aslam, R.W., Raza, D., Hatamleh, W.A.: Comparison of Three Machine Learning Algorithms Using Google Earth Engine for Land Use Land Cover Classification. Rangel. Ecol. Manag. 92, 129 (2024). [CrossRef] [Google Scholar]
- Bagchi, D., Kannaujiya, S., Champati ray, P.K., Taloor, A.K., Sarkar, T.: A study on spring rejuvenation and springshed characterization in Mussoorie, Garhwal Himalaya using an integrated geospatial-geophysical approach. Remote Sens. Appl. Soc. Environ. 23, 100588 (2021). [Google Scholar]
- Mazarire, T.T., Ratshiedana, P.E., Nyamugama, A., Adam, E., Chirima, G.: Exploring machine learning algorithms for mapping crop types in a heterogeneous agriculture landscape using Sentinel-2 data. A case study of Free State Province, South Africa. South African J. Geomatics. 9, 333 (2022). [Google Scholar]
- Utama, W., Indriani, R.F., Hermana, M., Anjasmara, I.M., Garini, S.A., Pratama, D., Putra, N.: Towards Improving Sustainable Water Management in Geothermal Fields : SVM and RF Land Use Monitoring. J. Human, Earth, Futur. 5, 216 (2024). [CrossRef] [Google Scholar]
- Hu, F., Huang, C.S., Han, J.H., Huang, W., Li, X., Hou, B.Q., Akram, W., Li, L., Liu, X.H., Chen, W., Zhao, Z.L., Zhan, J., Xu, L.S., Shan, H., Li, X.Z., Han, W.J., Yin, Z. Bin, Wang, Z.Z., Xiao, T.F.: An improved technology for monitoring groundwater flow velocity and direction in fractured rock system based on colloidal particles motion. Sci. Rep. 14, 1 (2024). [CrossRef] [Google Scholar]
- Mao, M., Xia, M.: Seasonal dynamics of water circulation and exchange flows in a shallow lagoon-inlet-coastal ocean system. Ocean Model. 186, 102276 (2023). [CrossRef] [Google Scholar]
- Mullissa, A., Vollrath, A., Odongo-Braun, C., Slagter, B., Balling, J., Gou, Y., Gorelick, N., Reiche, J.: Sentinel-1 sar backscatter analysis ready data preparation in google earth engine. Remote Sens. 13, 5 (2021). [Google Scholar]
- Indriani, R.F., Anjasmara, I.M., Utama, W., Dzulfiqar Rafi, M.E., Lumban Gaol, D.J.: Geological Structure Model for Recharge Area in Patuha Geothermal Field. IOP Conf. Ser. Earth Environ. Sci. 1276, (2023). [Google Scholar]
- Van Huynh, C., Pham, T.G., Nguyen, L.H.K., Nguyen, H.T., Nguyen, P.T., Le, Q.N.P., Tran, P.T., Nguyen, M.T.H., Tran, T.T.A.: Application GIS and remote sensing for soil organic carbon mapping in a farm-scale in the hilly area of central Vietnam. Air, Soil Water Res. 15, 1 (2022). [CrossRef] [Google Scholar]
- Ghayour, L., Neshat, A., Paryani, S., Shahabi, H., Shirzadi, A., Chen, W., Al-Ansari, N., Geertsema, M., Amiri, M.P., Gholamnia, M., Dou, J., Ahmad, A.: Performance evaluation of sentinel-2 and landsat 8 OLI data for land cover/use classification using a comparison between machine learning algorithms. Remote Sens. 13, 1349 (2021). [CrossRef] [Google Scholar]
- Rezaei, M., Mousavi, S.R., Rahmani, A., Zeraatpisheh, M., Rahmati, M., Pakparvar, M., Jahandideh Mahjenabadi, V.A., Seuntjens, P., Cornelis, W.: Incorporating machine learning models and remote sensing to assess the spatial distribution of saturated hydraulic conductivity in a light-textured soil. Comput. Electron. Agric. 209, 107821 (2023). [CrossRef] [Google Scholar]
- Zhang, C., Huang, C., Li, H., Liu, Q., Li, J., Bridhikitti, A., Liu, G.: Effect of textural features in remote sensed data on rubber plantation extraction at different levels of spatial resolution. Forests. 11, 399 (2020). [CrossRef] [Google Scholar]
- Demir, S., Sahin, E.K.: Comparison of tree-based machine learning algorithms for predicting liquefaction potential using canonical correlation forest, rotation forest, and random forest based on CPT data. Soil Dyn. Earthq. Eng. 154, (2022). [Google Scholar]
- Chowdhury, M.S.: Comparison of accuracy and reliability of random forest, support vector machine, artificial neural network and maximum likelihood method in land use/cover classification of urban setting. Environ. Challenges. 14, 100800 (2024). [CrossRef] [Google Scholar]
- Rahman, A., Abdullah, H.M., Tanzir, M.T., Hossain, M.J., Khan, B.M., Miah, M.G., Islam, I.: Performance of different machine learning algorithms on satellite image classification in rural and urban setup. Remote Sens. Appl. Soc. Environ. 20, 100410 (2020). [Google Scholar]
- Pande, C.B., Moharir, K.N., Panneerselvam, B., Singh, S.K., Elbeltagi, A., Pham, Q.B., Varade, A.M., Rajesh, J.: Delineation of groundwater potential zones for sustainable development and planning using analytical hierarchy process (AHP), and MIF techniques. Appl. Water Sci. 11, 1 (2021). [CrossRef] [Google Scholar]
- Ocampo-Marulanda, C., Fernández-Álvarez, C., Cerón, W.L., Canchala, T., Carvajal-Escobar, Y., Alfonso-Morales, W.: A spatiotemporal assessment of the high-resolution CHIRPS rainfall dataset in southwestern Colombia using combined principal component analysis. Ain Shams Eng. J. 13, 101739 (2022). [CrossRef] [Google Scholar]
- Ye, L., Fang, L., Shi, Z., Deng, L., Tan, W.: Spatio-temporal dynamics of soil moisture driven by ‘Grain for Green’ program on the Loess Plateau, China. Agric. Ecosyst. Environ. 269, 204 (2019). [CrossRef] [Google Scholar]
- Ilyas, A., Parkinson, S., Vinca, A., Byers, E., Manzoor, T., Riahi, K., Willaarts, B., Siddiqi, A., Muhammad, A.: Balancing smart irrigation and hydropower investments for sustainable water conservation in the Indus basin. Environ. Sci. Policy. 135, 147 (2022). [CrossRef] [Google Scholar]
- Weldeyohannes, T.T., Hailu, B.T., Muluneh, A.A., Kidane, T.: Detection of geothermal anomalies in the Northern Lake Abaya geothermal field, Main Ethiopian Rift. J. Volcanol. Geotherm. Res. 430, 107638 (2022). [CrossRef] [Google Scholar]
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