請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/91389完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 張斐章 | zh_TW |
| dc.contributor.advisor | Fi-john Chang | en |
| dc.contributor.author | 邱普運 | zh_TW |
| dc.contributor.author | Pu-Yun Kow | en |
| dc.date.accessioned | 2024-01-26T16:17:21Z | - |
| dc.date.available | 2024-01-27 | - |
| dc.date.copyright | 2024-01-26 | - |
| dc.date.issued | 2023 | - |
| dc.date.submitted | 2024-01-09 | - |
| dc.identifier.citation | Adams, S. R., Cockshull, K. E., & Cave, C. R. J. (2001). Effect of temperature on the growth and development of tomato fruits. Annals of botany, 88(5), pp. 869-877. https://doi.org/10.1006/anbo.2001.1524
Al-Yahyai, S., Charabi, Y., & Gastli, A. (2010). Review of the use of numerical weather prediction (NWP) models for wind energy assessment. Renewable and Sustainable Energy Reviews, 14(9), 3192-3198. https://doi.org/10.1016/j.rser.2010.07.001 Andoko, E., Liu, W. Y., Zeng, H. J., & Sjoblom, A. (2020). Review of Taiwan’s food security strategy. Food and Fertilizer Technology Center for the Asian and Pacific Region (FFTC-AP)(website). Akkaya, E. (2016). ANFIS based prediction model for biomass heating value using proximate analysis components. Fuel, 180, 687-693. Arias, A., Feijoo, G., & Moreira, M. T. (2023). How could Artificial Intelligence be used to increase the potential of biorefineries in the near future? A review. Environmental Technology & Innovation, 32, 103277. Beck, H. E., Pan, M., Lin, P., Seibert, J., van Dijk, A. I., & Wood, E. F. (2020). Global fully distributed parameter regionalization based on observed streamflow from 4,229 headwater catchments. Journal of Geophysical Research: Atmospheres, 125(17), e2019JD031485. Bessagnet, B., Beauchamp, M., Menut, L., Fablet, R., Pisoni, E., & Thunis, P. (2021). Deep learning techniques applied to super-resolution chemistry transport modeling for operational uses. Environmental Research Communications, 3(8), 085001. https://doi.org/10.1088/2515-7620/ac17f7 Box, G. E. P., & Wilson, K. B. (1951). On the experimental designs for exploring response surfaces. Ann Math Stat, 13, 1-45. Chang, L. T. C., Tsai, J. H., Lin, J. M., Huang, Y. S., & Chiang, H. L. (2011). Particulate matter and gaseous pollutants during a tropical storm and air pollution episode in Southern Taiwan. Atmospheric research, 99(1), 67-79. Chang, L. C., Liou, J. Y., Chang, F. J., 2022. Spatial-temporal flood inundation nowcasts by fusing machine learning methods and principal component analysis. J. Hydrol., 612, 128086. https://doi.org/10.1016/j.jhydrol.2022.128086 Chang, Q., Liu, L., Farooqi, M. U., Thomas, B., & Özkılıç, Y. O. (2023). Data-driven based estimation of waste-derived ceramic concrete from experimental results with its environmental assessment. Journal of Materials Research and Technology, 24, 6348-6368. Chen, F., Lu, S. M., & Chang, Y. L. (2007). Renewable energy in Taiwan: its developing status and strategy. Energy, 32(9), 1634-1646. https://doi.org/10.1016/j.energy.2006.12.007 Chen, Y. S., Wu, H. C., Yu, C. R., Chen, Z. Y., Lu, Y. C., & Yanagida, F. (2016). Isolation and characterization of lactic acid bacteria from xi-gua-mian (fermented watermelon), a traditional fermented food in Taiwan. Italian Journal of Food Science, 28(1), 9-14. https://doi.org/10.14674/1120-1770/ijfs.v451 Chukkapalli, S. S. L., Mittal, S., Gupta, M., Abdelsalam, M., Joshi, A., Sandhu, R., & Joshi, K. (2020). Ontologies and artificial intelligence systems for the cooperative smart farming ecosystem. Ieee Access, 8, 164045-164064. Cui, B., Liu, M., Li, S., Jin, Z., Zeng, Y., & Lin, X. (2023). Deep learning methods for atmospheric PM2. 5 prediction: A comparative study of transformer and CNN-LSTM-attention. Atmospheric Pollution Research, 14(9), 101833. Eccel, E. (2012). Estimating air humidity from temperature and precipitation measures for modelling applications. Meteorological Applications, 19(1), 118-128. https://doi.org/10.1002/met.258 Elisha, O. D. (2020). Moving beyond take-make-dispose to take-make-use for sustainable economy. Int. J. Sci. Res. Educ, 13(3), 497-516. Fajfar, I., Bűrmen, Á., & Puhan, J. (2019). The Nelder–Mead simplex algorithm with perturbed centroid for high-dimensional function optimization. Optimization Letters, 13, 1011-1025. Fan, X. X., Xu, Z. G., Liu, X. Y., Tang, C. M., Wang, L. W., & Han, X. L. (2013). Effects of light intensity on the growth and leaf development of young tomato plants grown under a combination of red and blue light. Scientia Horticulturae, 153, pp. 50-55. Ferrante, A., & Mariani, L. (2018). Agronomic management for enhancing plant tolerance to abiotic stresses: High and low values of temperature, light intensity, and relative humidity. Horticulturae, 4(3), p. 21 Ganguly, S., Ahmed, A., & Wang, F. (2020). Optimised building energy and indoor microclimatic predictions using knowledge-based system identification in a historical art gallery. Neural Computing and Applications, 32, 3349-3366. Gharghory, S. M. (2020). Deep network based on long short-term memory for time series prediction of microclimate data inside the greenhouse. International Journal of Computational Intelligence and Applications, 19(02), 2050013. https://doi.org/10.1142/S1469026820500133 Gupta, P., Rai, R., Vasudev, S., Yadava, D., Dash, P., 2021. Ex-foliar application of glycine betaine and its impact on protein, carbohydrates and induction of ROS scavenging system during drought stress in flax (Linum usitatissimum). J. Biotechnol. 337, 80-89. https://doi.org/10.1016/j.jbiotec.2021.06.012 Hai, T., Wang, D., & Muranaka, T. (2022). An improved MPPT control-based ANFIS method to maximize power tracking of PEM fuel cell system. Sustainable Energy Technologies and Assessments, 54, 102629. Hardwick Jones, R., Westra, S., & Sharma, A. (2010). Observed relationships between extreme sub‐daily precipitation, surface temperature, and relative humidity. Geophysical Research Letters, 37(22). https://doi.org/10.1029/2010GL045081 Hopfield, J. J. (1988). Artificial neural networks. IEEE Circuits and Devices Magazine, 4(5), 3-10. Hsu, H. H., & Chen, C. T. (2002). Observed and projected climate change in Taiwan. Meteorology and Atmospheric Physics, 79, 87-104. https://doi.org/10.1007/s703-002-8230-x Huang, A., & Chang, F. J. (2021). Using a self-organizing map to explore local weather features for smart urban agriculture in northern Taiwan. Water, 13(23), 3457. Huang, N., Cheng, J., Lai, W., Lu, M., 2005. Antrodia camphorata prevents rat pheochromocytoma cells from serum deprivation-induced apoptosis. FEMS Microbiol. Lett. 244, 213-219. Huang, W. C. (2021, January). Human Capital Development for Agribusiness in Higher Education: The Experience of Taiwan. In Conference on International Issues in Business and Economics Research (CIIBER 2019) (pp. 111-114). Atlantis Press. Huang, W. J. (2019). The new spatial planning act in Taiwan: a messy shift from economic development-oriented planning to environmental conservation-oriented planning?. Planning practice & research, 34(1), 120-130. https://doi.org/10.1080/02697459.2018.1523289 Hung, W. C., Hwang, C., Liou, J. C., Lin, Y. S., & Yang, H. L. (2012). Modeling aquifer-system compaction and predicting land subsidence in central Taiwan. Engineering Geology, 147, 78-90. Izady, A., Davary, K., Alizadeh, A., Ziaei, A. N., Alipoor, A., Joodavi, A., & Brusseau, M. L. (2014). A framework toward developing a groundwater conceptual model. Arabian Journal of Geosciences, 7, 3611-3631. https://doi.org/10.1007/s12517-013-0971-9 Jang, J. S. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE transactions on systems, man, and cybernetics, 23(3), 665-685. Jia, W., & Wei, Z. (2022). Short Term Prediction Model of Environmental Parameters in Typical Solar Greenhouse Based on Deep Learning Neural Network. Applied Sciences, 12(24), 12529. Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255-260. Jung, D. H., Kim, H. S., Jhin, C., Kim, H. J., & Park, S. H. (2020). Time-serial analysis of deep neural network models for prediction of climatic conditions inside a greenhouse. Computers and Electronics in Agriculture, 173, 105402. Kamienski, C., Soininen, J. P., Taumberger, M., Dantas, R., Toscano, A., Salmon Cinotti, T., ... & Torre Neto, A. (2019). Smart water management platform: IoT-based precision irrigation for agriculture. Sensors, 19(2), 276. Kao, I. F., Liou, J. Y., Lee, M. H., & Chang, F. J. (2021). Fusing stacked autoencoder and long short-term memory for regional multistep-ahead flood inundation forecasts. Journal of Hydrology, 598, 126371. Kang, Y., Khan, S., & Ma, X. (2009). Climate change impacts on crop yield, crop water productivity and food security–A review. Progress in natural Science, 19(12), 1665-1674. https://doi.org/10.1016/j.pnsc.2009.08.001 Kareem, S., Hamad, Z. J., & Askar, S. (2021). An evaluation of CNN and ANN in prediction weather forecasting: A review. Sustainable Engineering and Innovation, 3(2), 148-159. https://doi.org/10.37868/sei.v3i2.id146 Kashyap, H., Ahmed, H. A., Hoque, N., Roy, S., & Bhattacharyya, D. K. (2015). Big data analytics in bioinformatics: A machine learning perspective. arXiv preprint arXiv:1506.05101. Khan, F. A., Abubakar, A., Mahmoud, M., Al-Khasawneh, M. A., & Alarood, A. A. (2019). Cotton crop cultivation oriented semantic framework based on IoT smart farming application. International Journal of Engineering and Advanced Technology, 8(3), 480-484. Khanna, A., & Kaur, S. (2019). Evolution of Internet of Things (IoT) and its significant impact in the field of Precision Agriculture. Computers and electronics in agriculture, 157, 218-231. Kimura, R. (2002). Numerical weather prediction. Journal of Wind Engineering and Industrial Aerodynamics, 90(12-15), 1403-1414. Koncar, N. (1997) Optimisation Methodologies for Direct Inverse Neurocontrol. PhD Thesis, Department of Computing, Imperial College of Science, Technology and Medicine, University of London. Kow, P. Y., Wang, Y. S., Zhou, Y., Kao, I. F., Issermann, M., Chang, L. C., & Chang, F. J. (2020). Seamless integration of convolutional and back-propagation neural networks for regional multi-step-ahead PM2. 5 forecasting. Journal of Cleaner Production, 261, 121285. https://doi.org/10.1016/j.jclepro.2020.121285 Kow, P. Y., Chang, L. C., Lin, C. Y., Chou, C. C. K., & Chang, F. J. (2022a). Deep neural networks for spatiotemporal PM2. 5 forecasts based on atmospheric chemical transport model output and monitoring data. Environmental Pollution, 306, 119348. Kow, P. Y., Lee, M. H., Sun, W., Yao, M. H., & Chang, F. J. (2022b). Integrate deep learning and physically-based models for multi-step-ahead microclimate forecasting. Expert Systems with Applications, 210, 118481. Kow, P. Y., Lu, M. K., Lee, M. H., Lu, W. B., & Chang, F. J. (2023). Develop a hybrid machine learning model for promoting microbe biomass production. Bioresource Technology, 369, 128412. Koszewska, M. (2018). Circular economy—Challenges for the textile and clothing industry. Autex Research Journal, 18(4), 337-347. Liakos, K. G., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine learning in agriculture: A review. Sensors, 18(8), 2674. Lee, C., Lee, K., Kim, S., Yu, J., Jeong, S., & Yeom, J. (2021). Hourly ground-level PM2. 5 estimation using geostationary satellite and reanalysis data via deep learning. Remote Sensing, 13(11), 2121. https://doi.org/10.3390/rs13112121 Lee, M. H., Lu, W. B., Lu, M. K., & Chang, F. J. (2022). A hybrid of response surface methodology and artificial neural network in optimization of culture conditions of mycelia growth of Antrodia cinnamomea. Biomass and Bioenergy, 158, 106349. Lee, W. C., Yusof, S. A. L. M. A. H., Hamid, N. S. A., & Baharin, B. S. (2006). Optimizing conditions for enzymatic clarification of banana juice using response surface methodology (RSM). Journal of food Engineering, 73(1), 55-63. L’heureux, A., Grolinger, K., Elyamany, H. F., & Capretz, M. A. (2017). Machine learning with big data: Challenges and approaches. Ieee Access, 5, 7776-7797. Li, X., Feng, Y. J., & Liang, H. Y. (2017, July). The impact of meteorological factors on PM2. 5 variations in Hong Kong. In IOP Conference Series: Earth and Environmental Science (Vol. 78, No. 1, p. 012003). IOP Publishing. https://doi.org/10.1088/1755-1315/78/1/012003 Li, L., Lei, Y., Wu, S., Chen, J., & Yan, D. (2017). The health economic loss of fine particulate matter (PM2. 5) in Beijing. Journal of cleaner production, 161, 1153-1161. https://doi.org/10.1016/j.jclepro.2017.05.029 Lin, M. Y., Chen, Y. C., Lin, D. Y., Hwang, B. F., Hsu, H. T., Cheng, Y. H., ... & Tsai, P. J. (2020). Effect of implementing electronic toll collection in reducing highway particulate matter pollution. Environmental Science & Technology, 54(15), 9210-9216. Liu, C. H., Pan, Y. W., Liao, J. J., Huang, C. T., & Ouyang, S. (2004). Characterization of land subsidence in the Choshui River alluvial fan, Taiwan. Environmental Geology, 45, 1154-1166. Liu, J. C. E., & Chao, C. W. (2022). Equal rights for gasoline and electricity? The dismantling of fossil fuel vehicle phase-out policy in Taiwan. Energy Research & Social Science, 89, 102571. https://doi.org/10.1016/j.erss.2022.102571 Liu, J. N., Hu, Y., He, Y., Chan, P. W., & Lai, L. (2015). Deep neural network modeling for big data weather forecasting. Information granularity, big data, and computational intelligence, 389-408. https://doi.org/10.1007/978-3-319-08254-7_19 Lu, M., El-Shazly, M., Wu, T., Du, Y., Chang, T., Chen, C., Hsu, Y., Lai, K., Chiu, C., Chang, F., Wu, Y., 2013. Recent research and development of Antrodia cinnamomea. Pharmacol. Therapeu. 139, 124-156. https://doi.org/10.1016/j.pharmthera.2013.04.001 Lu, Y., & Wu, K. (2011). Effect of relative humidity on population growth of Apolygus lucorum (Heteroptera: Miridae). Applied Entomology and Zoology, 46(3), pp. 421-427. Lutz, A. F., Immerzeel, W. W., Siderius, C., Wijngaard, R. R., Nepal, S., Shrestha, A. B., ... , Biemans, H., 2022. South Asian agriculture increasingly dependent on meltwater and groundwater. Nat. Clim. Change, 12(6), 566-573. https://doi.org/10.1038/s41558-022-01355-z Mekonnen, Y., Namuduri, S., Burton, L., Sarwat, A., & Bhansali, S. (2019). Machine learning techniques in wireless sensor network based precision agriculture. Journal of the Electrochemical Society, 167(3), 037522. https://doi.org/10.1149/2.0222003JES Mokhtarzad, M., Eskandari, F., Jamshidi Vanjani, N., & Arabasadi, A. (2017). Drought forecasting by ANN, ANFIS, and SVM and comparison of the models. Environmental earth sciences, 76, 1-10. Moon, T., Lee, J. W., & Son, J. E. (2021). Accurate imputation of greenhouse environment data for data integrity utilizing two-dimensional convolutional neural networks. Sensors, 21(6), 2187. Mostafaei, M., Javadikia, H., & Naderloo, L. (2016). Modeling the effects of ultrasound power and reactor dimension on the biodiesel production yield: Comparison of prediction abilities between response surface methodology (RSM) and adaptive neuro-fuzzy inference system (ANFIS). Energy, 115, 626-636. Mujtaba, M., Fraceto, L., Fazeli, M., Mukherjee, S., Savassa, S. M., de Medeiros, G. A., ... & Vilaplana, F. (2023). Lignocellulosic biomass from agricultural waste to the circular economy: A review with focus on biofuels, biocomposites and bioplastics. Journal of Cleaner Production, 136815. Narvaez, L., Janzen, S., Eberle, C., & Sebesvari, Z. (2022). Technical Report: Taiwan drought. NCKU, 2023. What does the government do to prevent and control ground subsidence at this stage? http://www.lsprc.ncku.edu.tw/zh-tw/strategy.php?action=view&id=11 Nizami, A. S., Rehan, M., Waqas, M., Naqvi, M., Ouda, O. K., Shahzad, K., ... & Pant, D. (2017). Waste biorefineries: Enabling circular economies in developing countries. Bioresource technology, 241, 1101-1117. Peerlinck, A., Sheppard, J., & Maxwell, B. (2018, June). Using deep learning in yield and protein prediction of winter wheat based on fertilization prescriptions in precision agriculture. In International Conference on Precision Agriculture (ICPA). Pereira, L. M. S., Milan, T. M., & Tapia-Blácido, D. R. (2021). Using Response Surface Methodology (RSM) to optimize 2G bioethanol production: A review. Biomass and Bioenergy, 151, 106166. Oruganti, R. K., Biji, A. P., Lanuyanger, T., Show, P. L., Sriariyanun, M., Upadhyayula, V. K., ... & Bhattacharyya, D. (2023). Artificial intelligence and machine learning tools for high-performance microalgal wastewater treatment and algal biorefinery: A critical review. Science of The Total Environment, 876, 162797. Qi, C., Wu, M., Liu, H., Liang, Y., Liu, X., & Lin, Z. (2023). Machine learning exploration of the mobility and environmental assessment of toxic elements in mining-associated solid wastes. Journal of Cleaner Production, 401, 136771. Quy, V. K., Hau, N. V., Anh, D. V., Quy, N. M., Ban, N. T., Lanza, S., ... & Muzirafuti, A. (2022). IoT-enabled smart agriculture: architecture, applications, and challenges. Applied Sciences, 12(7), 3396. https://doi.org/10.3390/app12073396 Radhakrishnan, R., & CA, L. D. (2023). Groundwater Level Prediction Using Support Vector Machine and M5 Model Tree-A Case Study. Available at SSRN 4512253. Rahimi, M., & Ebrahimi, H. (2023). Data driven of underground water level using artificial intelligence hybrid algorithms. Scientific Reports, 13(1), 10359. Rezaeianzadeh, M., Tabari, H., Arabi Yazdi, A., Isik, S., & Kalin, L. (2014). Flood flow forecasting using ANN, ANFIS and regression models. Neural Computing and Applications, 25, 25-37. Rodrigues, E. R., Oliveira, I., Cunha, R., & Netto, M. (2018, October). DeepDownscale: A deep learning strategy for high-resolution weather forecast. In 2018 IEEE 14th International Conference on e-Science (e-Science) (pp. 415-422). IEEE. https://doi.org/10.1109/eScience.2018.00130. Sadhukhan, J., Martinez-Hernandez, E., Murphy, R. J., Ng, D. K., Hassim, M. H., Ng, K. S., ... & Andiappan, V. (2018). Role of bioenergy, biorefinery and bioeconomy in sustainable development: Strategic pathways for Malaysia. Renewable and Sustainable Energy Reviews, 81, 1966-1987. Santibanez-Aguilar, J. E., González-Campos, J. B., Ponce-Ortega, J. M., Serna-González, M., & El-Halwagi, M. M. (2011). Optimal planning of a biomass conversion system considering economic and environmental aspects. Industrial & Engineering Chemistry Research, 50(14), 8558-8570. Sarchani, S., Awol, F. S., & Tsanis, I. (2021). Hydrological analysis of extreme rain events in a medium-sized basin. Applied Sciences, 11(11), 4901. Sawatdeenarunat, C., Nguyen, D., Surendra, K. C., Shrestha, S., Rajendran, K., Oechsner, H., ... & Khanal, S. K. (2016). Anaerobic biorefinery: current status, challenges, and opportunities. Bioresource technology, 215, 304-313. Scher, S., & Messori, G. (2018). Predicting weather forecast uncertainty with machine learning. Quarterly Journal of the Royal Meteorological Society, 144(717), 2830-2841. Semakin, A. N., & Rastigejev, Y. (2016). Numerical simulation of global-scale atmospheric chemical transport with high-order wavelet-based adaptive mesh refinement algorithm. Monthly Weather Review, 144(4), 1469-1486. https://doi.org/10.1175/MWR-D-15-0200.1 Seibert, J., & Bergström, S. (2021). A retrospective on hydrological modelling based on half a century with the HBV model. Hydrology and Earth System Sciences Discussions, 2021, 1-28. Shao, M., Xu, X., Lu, Y., & Dai, Q. (2023). Spatio-temporally differentiated impacts of temperature inversion on surface PM2. 5 in eastern China. Science of The Total Environment, 855, 158785. Sharma, A., Jain, A., Gupta, P., & Chowdary, V. (2020). Machine learning applications for precision agriculture: A comprehensive review. IEEE Access, 9, 4843-4873. https://doi.org/10.1109/ACCESS.2020.3048415. Shen, Y., Chou, C., Wang, Y., Chen, C., Chou, Y., Lu, M., 2004. Anti-inflammatory activity of the extracts from mycelia of Antrodia camphorata cultured with water-soluble fraction from five different Cinnamomum genera, FEMS Microbiol. Lett. 231, 137-143. Siddhartha, E., & Lakkannavar, M. C. (2021, August). Smart irrigation and crop health prediction. In 2021 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT) (pp. 739-742). IEEE. https://doi.org/10.1109/RTEICT52294.2021.9573542. Singh, A., Mishra, S., & Ruskauff, G. (2010). Model averaging techniques for quantifying conceptual model uncertainty. Groundwater, 48(5), 701-715. https://doi.org/10.1111/j.1745-6584.2009.00642.x Singh, D. K., & Sobti, R. (2021, October). Role of Internet of Things and Machine Learning in Precision Agriculture: A Short Review. In 2021 6th International Conference on Signal Processing, Computing and Control (ISPCC) (pp. 750-754). IEEE. Singh, V. K., & Tiwari, K. N. (2017). Prediction of greenhouse micro-climate using artificial neural network. Appl. Ecol. Environ. Res, 15(1), 767-778. Smit, JN & Combrink, N. J. J. (2005). Pollination and yield of winter-grown greenhouse tomatoes as affected by boron nutrition, cluster vibration and relative humidity. South African Journal of Plant and Soil, 22(2), 110-115. Suay, R., López, S., Granell, R., Moltó, E., Fatnassi, H., & Boulard, T. (2008, October). Preliminary analysis of greenhouse microclimate heterogeneity for different weather conditions. In International Workshop on Greenhouse Environmental Control and Crop Production in Semi-Arid Regions 797 (pp. 103-109). Sun, J., Hu, L., Li, D., Sun, K., & Yang, Z. (2022). Data-driven models for accurate groundwater level prediction and their practical significance in groundwater management. Journal of Hydrology, 608, 127630. Sun, W., & Chang, F. J. (2023). Empowering Greenhouse Cultivation: Dynamic Factors and Machine Learning Unite for Advanced Microclimate Prediction. Water, 15(20), 3548. Suzuki, M., Umeda, H., Matsuo, S., Kawasaki, Y., Ahn, D., Hamamoto, H., & Iwasaki, Y. (2015). Effects of relative humidity and nutrient supply on growth and nutrient uptake in greenhouse tomato production. Scientia Horticulturae, 187, 44-49. Tabasso, S., Ginepro, M., Tomasso, L., Montoneri, E., Nisticò, R., & Francavilla, M. (2020). Integrated biochemical and chemical processing of municipal bio-waste to obtain bio based products for multiple uses. The case of soil remediation. Journal of Ting, Y. C., Young, L. H., Lin, T. H., Tsay, S. C., Chang, K. E., & Hsiao, T. C. (2022). Quantifying the impacts of PM2. 5 constituents and relative humidity on visibility impairment in a suburban area of eastern Asia using long-term in-situ measurements. Science of The Total Environment, 818, 151759. Tsong, J. L., & Khor, S. M. (2023). Modern analytical and bioanalytical technologies and concepts for smart and precision farming. Analytical Methods, 15(26), 3125-3148. Tyagi, A. K. (2019, February). Machine learning with big data. In Machine Learning with Big Data (March 20, 2019). Proceedings of International Conference on Sustainable Computing in Science, Technology and Management (SUSCOM), Amity University Rajasthan, Jaipur-India. Wallace, J., & Kanaroglou, P. (2009). The effect of temperature inversions on ground-level nitrogen dioxide (NO2) and fine particulate matter (PM2. 5) using temperature profiles from the Atmospheric Infrared Sounder (AIRS). Science of the Total Environment, 407(18), 5085-5095. https://doi.org/10.1016/j.scitotenv.2009.05.050 Wang, B., Eum, K. D., Kazemiparkouhi, F., Li, C., Manjourides, J., Pavlu, V., & Suh, H. (2020). The impact of long-term PM 2.5 exposure on specific causes of death: Exposure-response curves and effect modification among 53 million US Medicare beneficiaries. Environmental Health, 19, 1-12. Wang, H. S. H., & Yao, Y. (2023). Machine learning for sustainable development and applications of biomass and biomass-derived carbonaceous materials in water and agricultural systems: A review. Resources, Conservation and Recycling, 190, 106847. Wang, P. C., & Shoup, T. E. (2011). Parameter sensitivity study of the Nelder–Mead simplex method. Advances in Engineering Software, 42(7), 529-533. Wang, X., Yang, Y., Zhao, X., Huang, M., & Zhu, Q. (2023). Integrating field images and microclimate data to realize multi-day ahead forecasting of maize crop coverage using CNN-LSTM. International Journal of Agricultural & Biological Engineering, 16(2). Wen, C., Liu, S., Yao, X., Peng, L., Li, X., Hu, Y., & Chi, T. (2019). A novel spatiotemporal convolutional long short-term neural network for air pollution prediction. Science of the total environment, 654, 1091-1099. Wei, A., Li, X., Yan, L., Wang, Z., & Yu, X. (2023). Machine learning models combined with wavelet transform and phase space reconstruction for groundwater level forecasting. Computers & Geosciences, 177, 105386. WRA, 2019. Taiwan Landsubsidence report. https://landsubsidence.wra.gov.tw/water_new/RealtimeMonitor/Download?fileName=108%E5%B9%B4%E5%BA%A6%E5%9C%B0%E5%B1%A4%E4%B8%8B%E9%99%B7%E6%AA%A2%E6%B8%AC%E6%88%90%E6%9E%9C%E5%A0%B1%E5%91%8A&filepath=~%5CFile%5CReportData%5C007005%5C108%E5%B9%B4%E5%BA%A6%E6%AD%B7%E5%B9%B4%E5%9C%B0%E5%B1%A4%E4%B8%8B%E9%99%B7%E6%AA%A2%E6%B8%AC%E6%88%90%E6%9E%9C%E5%A0%B1%E5%91%8A.pdf WRA, 2023. Jhuoshuei river basin water demand. https://wuss.wra.gov.tw/annuals.aspx Wu, C., Zhang, X., Wang, W., Lu, C., Zhang, Y., Qin, W., ... & Shu, L. (2021). Groundwater level modeling framework by combining the wavelet transform with a long short-term memory data-driven model. Science of The Total Environment, 783, 146948. Wu, K. Y., Hsia, I. W., Kow, P. Y., Chang, L. C., & Chang, F. J. (2023). High-spatiotemporal-resolution PM2. 5 forecasting by hybrid deep learning models with ensembled massive heterogeneous monitoring data. Journal of Cleaner Production, 139825. Xu, Y., Cao, W., Cui, J., Shen, F., Luo, J., & Wan, Y. (2022). Developing a sustainable process for the cleaner production of baker''s yeast: An approach towards waste management by an integrated fermentation and membrane separation process. Journal of Environmental Management, 323, 116197. Xue, T., Zheng, Y., Tong, D., Zheng, B., Li, X., Zhu, T., & Zhang, Q. (2019). Spatiotemporal continuous estimates of PM2. 5 concentrations in China, 2000–2016: A machine learning method with inputs from satellites, chemical transport model, and ground observations. Environment international, 123, 345-357. https://doi.org/10.1016/j.envint.2018.11.075 Yu, J. H., Lin, H. H., Lo, Y. C., Tseng, K. C., & Hsu, C. H. (2021). Measures to cope with the impact of climate change and drought in the island region: A study of the water literacy awareness, attitude, and behavior of the Taiwanese public. Water, 13(13), 1799. https://doi.org/10.3390/w13131799 Zhang, J., Zhou, Z., Wang, C., Xue, K., Liu, Y., Fang, M., ... & Sheng, Y. (2019, July). Research on the influence of indoor relative humidity on PM2. 5 concentration in residential buildings. In IOP Conference Series: Materials Science and Engineering (Vol. 585, No. 1, p. 012086). IOP Publishing. Zhang, X. (2015). Conjunctive surface water and groundwater management under climate change. Frontiers in Environmental Science, 3, 59. https://doi.org/10.3389/fenvs.2015.00059 Zhang, X., Shu, K., Rajkumar, S., & Sivakumar, V. (2021). Research on deep integration of application of artificial intelligence in environmental monitoring system and real economy. Environmental Impact Assessment Review, 86, 106499. Zhao, J., Deng, F., Cai, Y., & Chen, J. (2019). Long short-term memory-Fully connected (LSTM-FC) neural network for PM2. 5 concentration prediction. Chemosphere, 220, 486-492. Zhou, Y., Chang, F.J., Chang, L.C., Kao, I.F., Wang, Y.S. and Kang, C.C. (2019a). Multi-output support vector machine for regional multi-step-ahead PM2. 5 forecasting. Science of the Total Environment, 651, pp.230-240. Zhou, Y., Chang, F. J., Chang, L. C., Kao, I. F., & Wang, Y. S. (2019b). Explore a deep learning multi-output neural network for regional multi-step-ahead air quality forecasts. Journal of cleaner production, 209, 134-145. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/91389 | - |
| dc.description.abstract | 台灣的水、能源、材料和勞動資源受到氣候變遷的影響，因此，確保環境永續性對於未來世代的福祉至關重要。本研究提出了一種全面應對綜合性環境系統的方法，需要在宏觀、中觀和微觀層面實施政策變革。為追求與自然環境的和諧共生中，本研究重視環境管理、智慧農業的實踐，並積極探索創新的菌絲體策略。本研究所提到的環境系統中，包括四個關鍵方面：地下水位、PM2.5空氣污染嚴重程度、氣候等問題之預測及提昇生成牛樟芝的效益。在地下水位預測中，混合的人工智慧(AI)模型在R2和RMSE指標方面均超過基準，其高精確度使得其成為決策者潛在的參考依據，以進行高效的地下水資源規劃。對於PM2.5預測，混合AI模型由於額外的ACT輸入而顯著提升性能，對於準確的區域預測至關重要。該混合AI模型對於公眾意識和減少污染的政策實施是不可缺少的工具。
混合AI模型在基於中央氣象局(CWB)數據生成精確氣象預測方面表現出色，更減少對物聯網設備的依賴。這些氣象預測提供於農民將有助於溫室微氣候預測和智慧溫室的建構。該模型萃取高維數據的特徵能力强大，使其能夠準確預測農試所(TARI)和伸港(Shengang)溫室的微氣候趨勢的變化。此外，混合AI模型有助於提取顯著特徵，可靠地估計菌絲體產量並實現條件的人工智慧優化，展示了提高生產的巨大潛力，成功減少了75％的時間消耗(相比於基準)。 透過協同整治宏觀、中觀和微觀的環境系統，使得台灣更有潛力進一步朝向永續發展目標（SDGs），有效地對抗資源匱乏的威脅。此方法適用於應對眼前的環境挑戰，為未來奠定永續社會的基礎。 | zh_TW |
| dc.description.abstract | Ensuring a sustainable environment is paramount for the well-being of future generations, particularly in the face of climate-induced challenges impacting Taiwan's resources—water, energy, materials, and labor. This study advocates for a holistic approach, necessitating policy shifts at macro, meso, and micro levels, encompassing environmental management, intelligent agriculture practices, and innovative mycelia strategies—all geared towards achieving harmony with the environment. The environmental system under examination comprises four critical facets: forecasting groundwater levels, predicting PM2.5 air pollution levels, delivering climate forecasts, and enhanced mycelia yield efficiency. In groundwater level forecasting, the proposed hybrid AI model surpasses benchmarks in both R2 and RMSE metrics, providing decision-makers with robust tools for efficient groundwater resource planning. For PM2.5 level forecasting, the hybrid AI model's notable performance improvement, attributed to additional ACT inputs, is pivotal for accurate regional forecasts crucial in public awareness and policy implementation for pollution reduction.
The hybrid AI model excels in generating precise climate forecasts based on Central Weather Bureau (CWB) data, reducing reliance on IoT devices. These climate forecasts are extended to farmers, aiding in greenhouse microclimate predictions and the construction of intelligent greenhouses. The model's adept feature extraction from high-dimensional datasets enables accurate forecasts of greenhouse microclimate trends for TARI and Shengang greenhouses. Furthermore, integrating AI facilitates the extraction of salient features for reliable mycelia yield estimation and AI-driven optimization of conditions, showcasing significant potential for production enhancement and a notable 75% reduction in time consumption compared to conventional methods. In synergizing macroscale, mesoscale, and microscale environmental systems, Taiwan takes strides toward realizing Sustainable Development Goals (SDGs), effectively countering the specter of resource scarcity. This comprehensive approach addresses immediate environmental challenges and sets the stage for a sustainable and resilient future. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-01-26T16:17:21Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2024-01-26T16:17:21Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 謝誌 ii
中文摘要 iv Abstract vi Table of Contents viii List of Figures x List of Tables xi List of Supplementary Materials xi Chapter 1 Introduction 1 1.1 Research background 1 1.2 Research Motivation 3 Chapter 2 Literature Review 5 2.1 Macroscale: Smart Environmental Management - Environmental Factors Forecasting 5 2.1.1 The River Watershed Management: Groundwater Level Forecasting 5 2.1.2 An early warning approach of air pollution: Regional PM2.5 forecasting 6 2.1.3 Disaster Preparedness Measures: Agrometeorological Forecasting 8 2.2 Mesoscale: Precision agriculture – Greenhouse microclimate Forecasting 10 2.3 Microscale: The Smart Antrodia Camphorata Cultivation in petri dish with ANN cooperation 11 Chapter 3 Methodology 14 3.1 Deep Learning 14 3.2 CNN 16 3.3 LSTM 19 3.4 The hybrid AI model 22 3.5 Evaluation indicators 24 Chapter 4 Results analysis for each environmental problem 25 4.1 The River Watershed Management: Groundwater Level Forecasting 25 4.1.1 Water Demand and Land Subsidence in the Jhuoshuei River basin 25 4.1.2 Data analysis 29 4.1.4 Region of interest and Data Collection 33 4.1.5 The hybrid AI model (ConvAE-LSTM) 34 4.1.7 Groundwater level forecasting performance 38 4.1.8 Discussion 41 4.2 An early warning approach of air pollution: Regional PM2.5 forecasting 44 4.2.1 Health effects of PM2.5 exposure 44 4.2.2 Study area and materials 45 4.2.3 Statistical analysis of the input datasets 47 4.2.4 Input factor Selection 50 4.2.5 The hybrid AI model (MCNN-BP) 51 4.2.6 Exploring the Effectiveness of Various DNN Architectures in PM2.5 Prediction via Ensemble Inputs 54 4.2.7 PM2.5 forecasts for Taiwan 56 4.2.8 Discussion 58 4.3 Agrometeorological Forecasting and greenhouse microclimate Forecasting 60 4.3.1 An alternative software approach for precision agriculture 60 4.3.2 Research Locale and data collection 61 4.3.3 Statistical analysis 66 4.3.4 The hybrid AI model (Me1 (ConvLSTM-BP)) 69 4.3.5 Agrometeorological Forecasting 70 4.3.6 The hybrid AI model (Mi1 (ConvLSTM*CNN-BP)) 76 4.3.7 Greenhouse microclimate Forecasting 78 4.3.8 Smart greenhouse management system 86 4.4 Smart Cultivation of A. cinnamomea in Laboratory Culture Dishes 89 4.4.1 Fermentation application and cultivation Challenges of A. cinnamomea 89 4.4.2 Materials 91 4.4.3 Response Surface Methodology (RSM) 93 4.4.4 The hybrid AI model (ANFIS-NM) 96 4.4.5 The estimation performance obtained from the ANFIS for mycelia yield. 97 4.4.6 Determination of the pivotal factors impacting mycelia yield. 99 4.4.7 The optima cultivation conditions of mycelia yield 101 4.4.8 The Role of the Hybrid ANFIS-NM in Smart Cultivation of A. cinnamomea 105 Chapter 5 Conclusion 108 Reference 114 | - |
| dc.language.iso | en | - |
| dc.subject | 地下水位 | zh_TW |
| dc.subject | 深度學習 | zh_TW |
| dc.subject | 生物質 | zh_TW |
| dc.subject | 永續續環境目標 | zh_TW |
| dc.subject | 溫室微氣候 | zh_TW |
| dc.subject | 空氣污染 | zh_TW |
| dc.subject | groundwater level | en |
| dc.subject | Deep Learning (DNN) | en |
| dc.subject | biomass | en |
| dc.subject | Sustainable environment | en |
| dc.subject | greenhouse microclimate | en |
| dc.subject | air pollution | en |
| dc.title | 永續環境願景：發掘人工智慧的潛力以建構智慧生態系統 | zh_TW |
| dc.title | A Sustainable Environment Vision: Harnessing the Potential of Artificial Intelligence towards an Innovative Ecosystem | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 112-1 | - |
| dc.description.degree | 博士 | - |
| dc.contributor.oralexamcommittee | 盧虎生;黃文政;張麗秋;胡明哲 | zh_TW |
| dc.contributor.oralexamcommittee | Huu-Sheng Lur;Wen-Cheng Huang;Li-Chiu Chang;Ming-Che Hu | en |
| dc.subject.keyword | 永續續環境目標,溫室微氣候,空氣污染,地下水位,生物質,深度學習, | zh_TW |
| dc.subject.keyword | Sustainable environment,greenhouse microclimate,air pollution,groundwater level,biomass,Deep Learning (DNN), | en |
| dc.relation.page | 124 | - |
| dc.identifier.doi | 10.6342/NTU202400046 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2024-01-10 | - |
| dc.contributor.author-college | 生物資源暨農學院 | - |
| dc.contributor.author-dept | 生物環境系統工程學系 | - |
| dc.date.embargo-lift | 2029-01-08 | - |
| 顯示於系所單位： | 生物環境系統工程學系 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-112-1.pdf 未授權公開取用 | 5.49 MB | Adobe PDF | 檢視/開啟 |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。