本研究透過線性模型(線性判別模型(LDA)、多元邏輯斯回歸(MLR))及非線性模型(二次判別模型(QDA)、支撐向量機(SVM)、貝氏網路(BN))，針對兩種目標物種(半紋小䰾(Puntius semifasciolatus)、短吻紅斑吻鰕虎(Rhinogobius rubromaculatus))，在不同資料形態下(全域研究地點、上游河段、中游河段、下游河段及單獨2022年分資料)下，比較各模型的預測能力差異，並透過全域敏感度分析，了解各環境變量對於目標物種的重要程度。本研究更針對貝氏網路，以不同結構學習演算法，結合專家經驗的方式求解網路模型，更進一步了解環境變量與物種之間可能存在之因果關係。
本研究結果顯示，半紋小䰾喜好地表水水深相對偏深(40cm ~ 60cm)、流速相對偏低(0m/s ~ 0.5m/s)、pH相對偏高(7.5 ~ 8.5)且導電度相對偏低(150μmhos/cm ~ 250μmhos/cm)的環境。地表水水深影響其豐度類別程度遠高於其它環境變量，且該變量與地表水pH值的HSI曲線在特定範圍內接近線性分布。當給定的採樣數據量越大，線性模型LDA與非線性模型SVM的豐度類別預測能力無顯著差距，且模型平均準確度越高。當以資料量較少的時間及空間尺度進行討論時，雖然資料雜訊亦減少，但資料量的縮減更大程度降低線性模型的優勢，進而使非線性模型的預測能力顯著高於其它模型，模型平均準確度降低。
短吻紅斑吻鰕虎喜好地表水水深相對偏淺(0cm ~ 40cm)的棲息地，但比起半紋小䰾，其喜好的棲息地較為多元，更偏向廣適型魚種，且該物種的重要環境變量HSI普遍不具有特定趨勢。線性與非線性模型的預測能力與採樣數據量的多寡無明顯關係。但本研究發現，在資料量足夠的前提下(除了上游河段外的所有河段)，若採樣點位間的環境變異性較高，非線性模型SVM的預測能力顯著優於其它模型，但隨著環境變異性降低或資料間的雜訊減少，線性及非線性模型間的預測能力逐漸接近，甚至為無顯著差異，模型平均準確度亦越高。

In this study, linear models (Linear Discriminant Analysis and Multinomial Logistic Regression) and non-linear models (Quadratic Discriminant Analysis, Support Vector Machine, and Bayesian Networks) are used to compare their predictive abilities for two target species (Puntius semifasciolatus and Rhinogobius rubromaculatus). The comparison is also conducted under various data conditions (different spatial and temporal scales). Furthermore, the structures of the Bayesian Networks are explored using different structure learning algorithms, combined with expert knowledge and statistical tests to obtain the potential relationships between environmental variables and species.
The results of this study indicate that Puntius semifasciolatus prefers the habitat with relatively deep surface water (40cm ~ 60cm), low flow velocity (0m/s ~ 0.5m/s), high pH value (7.5 ~ 8.5), and low electrical conductivity (150μmhos/cm ~ 250μmhos/cm). The environmental variable “Water Depth” significantly influences its abundance. As the amount of the input dataset becomes larger, there is gradually no difference in the predictive ability between LDA and the radial kernel SVM models, and the average prediction accuracy of the models is higher. While discussing the small-scale sampling data, the reduction in data quantity diminishes the advantages of using linear models and lets the radial kernel SVM models perform better than other models. Meanwhile, the average prediction accuracy of the models becomes lower.
Rhinogobius rubromaculatus prefers the habitats with relatively shallow water (0cm ~ 40cm), and it is referred to be a kind of eurytopic species. In the precondition for sufficient training and validation data, if the variability and the noise among the sampling data increase, the predictive ability of radial kernel SVM models are significantly better than other models. As the environmental variability decreases and the data noise reduces, the predictive abilities of both linear and non-linear models gradually converge, showing no significant differences, and the average prediction accuracy of the models increases.

Abo Zidan, R., & Karraz, G. Gaussian Pyramid for Nonlinear Support Vector Machine. Applied Computational Intelligence and Soft Computing, 2022. (2022).
Alemi, F. Grow Shrink Algorithm for Discovering Markov Blankets. from http://openonlinecourses.com/causalanalysis/default.html (1996).
Almond, R. G., DiBello, L. V., Moulder, B., & Zapata‐Rivera, J. D. Modeling diagnostic assessments with Bayesian networks. Journal of Educational Measurement, 44(4), 341-359. (2007).
Alonso, J. I., de la Ossa, L., Gamez, J. A., & Puerta, J. M. On the use of local search heuristics to improve GES-based Bayesian network learning. Applied Soft Computing, 64, 366-376. (2018).
Asar, Y., & Erişoğlu, M. Liu Estimator in the Multinomial Logistic Regression Model. arXiv preprint arXiv:2111.03398. (2021).
Auria, L., & Moro, R. A. Support vector machines (SVM) as a technique for solvency analysis. (2008).
Böhning, D. Multinomial logistic regression algorithm. Annals of the institute of Statistical Mathematics, 44(1), 197-200. (1992).
Balakrishnama, S., & Ganapathiraju, A. Linear discriminant analysis-a brief tutorial. Institute for Signal and information Processing, 18(1998), 1-8. (1998).
Basant, N., Gupta, S., Malik, A., & Singh, K. P. Linear and nonlinear modeling for simultaneous prediction of dissolved oxygen and biochemical oxygen demand of the surface water—a case study. Chemometrics and Intelligent Laboratory Systems, 104(2), 172-180. (2010).
Beale, C. M., & Lennon, J. J. Incorporating uncertainty in predictive species distribution modelling. Philosophical Transactions of the Royal Society B: Biological Sciences, 367(1586), 247-258. (2012).
Behjati, S., & Beigy, H. Improved K2 algorithm for Bayesian network structure learning. Engineering Applications of Artificial Intelligence, 91, 103617. (2020).
Ben‐Gal, I. Bayesian networks. Encyclopedia of statistics in quality and reliability, 1. (2008).
Bentele, M., Lavrik, I., Ulrich, M., Stosser, S., Heermann, D., Kalthoff, H., et al. Mathematical modeling reveals threshold mechanism in CD95-induced apoptosis. The Journal of cell biology, 166(6), 839-851. (2004).
Bettany‐Saltikov, J., & Whittaker, V. J. Selecting the most appropriate inferential statistical test for your quantitative research study. Journal of Clinical Nursing, 23(11-12), 1520-1531. (2014).
Borcard, D., Gillet, F., & Legendre, P. Numerical ecology with R (Vol. 2): Springer. (2011).
Bose, S., Pal, A., SahaRay, R., & Nayak, J. Generalized quadratic discriminant analysis. Pattern Recognition, 48(8), 2676-2684. (2015).
Brown, J. H. On the relationship between abundance and distribution of species. The american naturalist, 124(2), 255-279. (1984).
Brown, J. H., Mehlman, D. W., & Stevens, G. C. Spatial variation in abundance. Ecology, 76(7), 2028-2043. (1995).
Buisson, L., Thuiller, W., Casajus, N., Lek, S., & Grenouillet, G. Uncertainty in ensemble forecasting of species distribution. Global Change Biology, 16(4), 1145-1157. (2010).
Cacuci, D. G. Sensitivity theory for nonlinear systems. I. Nonlinear functional analysis approach. Journal of Mathematical Physics, 22(12), 2794-2802. (1981).
Castaings, W., Dartus, D., Le Dimet, F.-X., & Saulnier, G.-M. Sensitivity analysis and parameter estimation for distributed hydrological modeling: potential of variational methods. Hydrology and Earth System Sciences, 13(4), 503-517. (2009).
Cervantes, J., Garcia-Lamont, F., Rodríguez-Mazahua, L., & Lopez, A. A comprehensive survey on support vector machine classification: Applications, challenges and trends. Neurocomputing, 408, 189-215. (2020).
Chan, T. U., Hart, B. T., Kennard, M. J., Pusey, B. J., Shenton, W., Douglas, M. M., et al. Bayesian network models for environmental flow decision making in the Daly River, Northern Territory, Australia. River Research and Applications, 28(3), 283-301. (2012).
Chang, F.-J., Wu, T.-C., Tsai, W.-P., & Herricks, E. E. Defining the ecological hydrology of Taiwan Rivers using multivariate statistical methods. Journal of hydrology, 376(1-2), 235-242. (2009).
Cherkassky, V., Shao, X., Mulier, F. M., & Vapnik, V. N. Model complexity control for regression using VC generalization bounds. IEEE transactions on Neural Networks, 10(5), 1075-1089. (1999).
Chickering, M., Geiger, D., & Heckerman, D. Learning Bayesian networks: Search methods and experimental results. Paper presented at the Proceedings of the fifth international workshop on artificial intelligence and statistics. (1995).
Colombo, D., & Maathuis, M. H. Order-independent constraint-based causal structure learning. J. Mach. Learn. Res., 15(1), 3741-3782. (2014).
El-Habil, A. M. An application on multinomial logistic regression model. Pakistan journal of statistics and operation research, 271-291. (2012).
Ershadi, M. M., & Seifi, A. An efficient Bayesian network for differential diagnosis using experts' knowledge. International Journal of Intelligent Computing and Cybernetics, 13(1), 103-126. (2020).
Friedman, N., & Goldszmidt, M. Discretizing continuous attributes while learning Bayesian networks. Paper presented at the ICML. (1996).
Friedman, N., & Koller, D. Being Bayesian about network structure. A Bayesian approach to structure discovery in Bayesian networks. Machine learning, 50, 95-125. (2003).
Friedman, N., Nachman, I., & Pe'er, D. Learning Bayesian network structure from massive datasets: The" sparse candidate" algorithm. arXiv preprint arXiv:1301.6696. (2013).
Gareth, J., Daniela, W., Trevor, H., & Robert, T. An introduction to statistical learning: with applications in R: Spinger. (2013).
Gaston, K. J. The structure and dynamics of geographic ranges: Oxford University Press on Demand. (2003).
Ghojogh, B., & Crowley, M. Linear and quadratic discriminant analysis: Tutorial. arXiv preprint arXiv:1906.02590. (2019).
Ghosh, S., Dasgupta, A., & Swetapadma, A. A study on support vector machine based linear and non-linear pattern classification. Paper presented at the 2019 International Conference on Intelligent Sustainable Systems (ICISS). (2019).
Giles, H. Using Bayesian networks to examine consistent trends in fish farm benthic impact studies. Aquaculture, 274(2-4), 181-195. (2008).
Gámez, J. A., Mateo, J. L., & Puerta, J. M. Learning Bayesian networks by hill climbing: efficient methods based on progressive restriction of the neighborhood. Data Mining and Knowledge Discovery, 22, 106-148. (2011).
Guisan, A., & Zimmermann, N. E. Predictive habitat distribution models in ecology. Ecological modelling, 135(2-3), 147-186. (2000).
Guo, H., & Li, H. An efficient Bayesian network structure learning algorithm using the strategy of two-stage searches. Intelligent Data Analysis, 24(5), 1087-1106. (2020).
Guo, H., & Li, H. A decomposition hybrid structure learning algorithm for Bayesian network using expert knowledge. Knowledge and Information Systems, 1-22. (2023).
Hamby, D. A comparison of sensitivity analysis techniques. Health physics, 68(2), 195-204. (1995).
Hastie, T., Tibshirani, R., Friedman, J., Hastie, T., Tibshirani, R., & Friedman, J. Linear methods for classification. The elements of statistical learning: data mining, inference, and prediction, 101-137. (2009).
Hauser-Davis, R. A., de Oliveira, T. F., Da Silveira, A. M., Protázio, J. M. B., & Ziolli, R. L. Logistic regression and fuzzy logic as a classification method for feral fish sampling sites. Environmental and ecological statistics, 19, 473-483. (2012).
Heckerman, D. A tutorial on learning with Bayesian networks: Springer. (1998).
Heckerman, D., Geiger, D., & Chickering, D. M. Learning Bayesian networks: The combination of knowledge and statistical data. Machine learning, 20, 197-243. (1995).
James, G., Witten, D., Hastie, T., & Tibshirani, R. An Introduction to Statistical Learning: with Applications in R: Springer New York. (2013).
Jensen, F. Bayesian Networks and Decision Graphs Springer-Verlag New York Inc. (2001).
Jensen, F. V. Bayesian networks. Wiley Interdisciplinary Reviews: Computational Statistics, 1(3), 307-315. (2009).
Jiang, B., Wang, X., & Leng, C. A direct approach for sparse quadratic discriminant analysis. The Journal of Machine Learning Research, 19(1), 1098-1134. (2018).
Kecman, V. Support vector machines–an introduction Support vector machines: theory and applications (pp. 1-47): Springer. (2005).
Kennedy, T. B., & Haag, W. R. Using morphometrics to identify glochidia from a diverse freshwater mussel community. Journal of the North American Benthological Society, 24(4), 880-889. (2005).
Knudby, A., Brenning, A., & LeDrew, E. New approaches to modelling fish–habitat relationships. Ecological Modelling, 221(3), 503-511. (2010).
Kwak, C., & Clayton-Matthews, A. Multinomial logistic regression. Nursing research, 51(6), 404-410. (2002).
Lee, S., & Kim, S. B. Parallel simulated annealing with a greedy algorithm for Bayesian network structure learning. IEEE Transactions on Knowledge and Data Engineering, 32(6), 1157-1166. (2019).
Legendre, P., & Gallagher, E. D. Ecologically meaningful transformations for ordination of species data. Oecologia, 129, 271-280. (2001).
Lepš, J., & Šmilauer, P. Multivariate analysis of ecological data using CANOCO: Cambridge university press. (2003).
Li, J.-P., Mirza, N., Rahat, B., & Xiong, D. Machine learning and credit ratings prediction in the age of fourth industrial revolution. Technological Forecasting and Social Change, 161, 120309. (2020).
Lin, Y., Deng, X., Li, X., & Ma, E. Comparison of multinomial logistic regression and logistic regression: which is more efficient in allocating land use? Frontiers of Earth Science, 8, 512-523. (2014).
Livingstone, D. M., & Imboden, D. M. The prediction of hypolimnetic oxygen profiles: a plea for a deductive approach. Canadian Journal of Fisheries and Aquatic Sciences, 53(4), 924-932. (1996).
Luce, R. D. Individual choice behavior: A theoretical analysis, new york, ny: John willey and sons: Inc. (1959).
Müller, K.-R., Anderson, C. W., & Birch, G. E. Linear and nonlinear methods for brain-computer interfaces. IEEE transactions on neural systems and rehabilitation engineering, 11(2), 165-169. (2003).
Madsen, A. L., Jensen, F., Salmerón, A., Langseth, H., & Nielsen, T. D. A parallel algorithm for Bayesian network structure learning from large data sets. Knowledge-Based Systems, 117, 46-55. (2017).
Manel, S., Dias, J.-M., & Ormerod, S. J. Comparing discriminant analysis, neural networks and logistic regression for predicting species distributions: a case study with a Himalayan river bird. Ecological modelling, 120(2-3), 337-347. (1999).
Marcos-Pasero, H., Colmenarejo, G., Aguilar-Aguilar, E., Ramírez de Molina, A., Reglero, G., & Loria-Kohen, V. Ranking of a wide multidomain set of predictor variables of children obesity by machine learning variable importance techniques. Scientific Reports, 11(1), 1910. (2021).
Marcot, B. G., Holthausen, R. S., Raphael, M. G., Rowland, M. M., & Wisdom, M. J. Using Bayesian belief networks to evaluate fish and wildlife population viability under land management alternatives from an environmental impact statement. Forest ecology and management, 153(1-3), 29-42. (2001).
Margaritis, D. Learning Bayesian network model structure from data: Carnegie-Mellon Univ Pittsburgh Pa School of Computer Science. (2003).
Margaritis, D., & Thrun, S. Bayesian network induction via local neighborhoods. Advances in neural information processing systems, 12. (1999).
Martín, B., González–Arias, J., & Vicente–Vírseda, J. Machine learning as a successful approach for predicting complex spatio–temporal patterns in animal species abundance. Machine learning, 44, 289-301. (2021).
McFadden, D. Conditional logit analysis of qualitative choice behavior. (1973).
McKelvey, D. R., & Wilson, C. D. Discriminant classification of fish and zooplankton backscattering at 38 and 120 kHz. Transactions of the American Fisheries Society, 135(2), 488-499. (2006).
Mitchell, T. Generative and Discriminative Classifiers: Naive Bayes and Logistic Regression. Draft Version. (2005).
Muñoz-Mas, R., Vezza, P., Alcaraz-Hernández, J. D., & Martínez-Capel, F. Risk of invasion predicted with support vector machines: A case study on northern pike (Esox Lucius, L.) and bleak (Alburnus alburnus, L.). Ecological Modelling, 342, 123-134. (2016).
Nagarajan, R., Scutari, M., & Lèbre, S. Bayesian networks in r. Springer, 122, 125-127. (2013).
Nate, N. A., Bozek, M. A., Hansen, M. J., Ramm, C. W., Bremigan, M. T., & Hewett, S. W. Predicting the occurrence and success of walleye populations from physical and biological features of northern Wisconsin lakes. North American Journal of Fisheries Management, 23(4), 1207-1214. (2003).
Ng, A., & Jordan, M. On discriminative vs. generative classifiers: A comparison of logistic regression and naive bayes. Advances in neural information processing systems, 14. (2001).
Nicholson, A., Woodberry, O., & Twardy, C. The “native fish” bayesian networks: Citeseer. (2010).
Niedermayer, D. An introduction to Bayesian networks and their contemporary applications. Innovations in Bayesian networks: Theory and applications, 117-130. (2008).
Olden, J. D., & Jackson, D. A. A comparison of statistical approaches for modelling fish species distributions. Freshwater biology, 47(10), 1976-1995. (2002).
Olden, J. D., Joy, M. K., & Death, R. G. An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data. Ecological modelling, 178(3-4), 389-397. (2004).
Orians, G. H. Micro and macro in ecological theory. Bioscience, 30(2), 79-79. (1980).
Osisanwo, F., Akinsola, J., Awodele, O., Hinmikaiye, J., Olakanmi, O., & Akinjobi, J. Supervised machine learning algorithms: classification and comparison. International Journal of Computer Trends and Technology (IJCTT), 48(3), 128-138. (2017).
Pearl, J. Probabilistic reasoning in intelligent systems: networks of plausible inference: Morgan kaufmann. (1988).
Pearl, J. Decision making under uncertainty. ACM Computing Surveys (CSUR), 28(1), 89-92. (1996).
Pellet, J.-P., & Elisseeff, A. Using markov blankets for causal structure learning. Journal of Machine Learning Research, 9(7). (2008).
Pickens, B. A., Carroll, R., Schirripa, M. J., Forrestal, F., Friedland, K. D., & Taylor, J. C. A systematic review of spatial habitat associations and modeling of marine fish distribution: a guide to predictors, methods, and knowledge gaps. Plos one, 16(5), e0251818. (2021).
Pollino, C. A., Woodberry, O., Nicholson, A., Korb, K., & Hart, B. T. Parameterisation and evaluation of a Bayesian network for use in an ecological risk assessment. Environmental Modelling & Software, 22(8), 1140-1152. (2007).
Qi, X., Fan, X., Gao, Y., & Liu, Y. Learning Bayesian network structures using weakest mutual-information-first strategy. International Journal of Approximate Reasoning, 114, 84-98. (2019).
Rahier, T. Bayesian networks for static and temporal data fusion. Université Grenoble Alpes. (2018).
Raue, A., Kreutz, C., Maiwald, T., Klingmüller, U., & Timmer, J. Addressing parameter identifiability by model-based experimentation. IET systems biology, 5(2), 120-130. (2011).
Recknagel, F. Applications of machine learning to ecological modelling. Ecological modelling, 146(1-3), 303-310. (2001).
Saltelli, A., & Chan, K. Scott. EM, editors (2000a). Sensitivity analysis: Wiley Series in Probability and Statistics. Wiley. (2000).
Samad, M. D., Ulloa, A., Wehner, G. J., Jing, L., Hartzel, D., Good, C. W., et al. Predicting survival from large echocardiography and electronic health record datasets: optimization with machine learning. JACC: Cardiovascular Imaging, 12(4), 681-689. (2019).
Scutari, M. Understanding Bayesian Networks. (2017).
Scutari, M., Graafland, C. E., & Gutiérrez, J. M. Who learns better bayesian network structures: Constraint-based, score-based or hybrid algorithms? Paper presented at the International Conference on Probabilistic Graphical Models. (2018).
Scutari, M., Graafland, C. E., & Gutiérrez, J. M. Who learns better Bayesian network structures: Accuracy and speed of structure learning algorithms. International Journal of Approximate Reasoning, 115, 235-253. (2019).
Segurado, P., & Araujo, M. B. An evaluation of methods for modelling species distributions. Journal of biogeography, 31(10), 1555-1568. (2004).
Shideler, G. S., Araújo, R. J., Walker, B. K., Blondeau, J., & Serafy, J. E. Non‐linear thresholds characterize the relationship between reef fishes and mangrove habitat. Ecosphere, 8(9), e01943. (2017).
Shmilovici, A. Support vector machines. Data mining and knowledge discovery handbook, 231-247. (2010).
Timothy Jason Shepard, P. Decision fusion using a multi-linear classifier. Paper presented at the Proceedings of the international conference on multisource-multisensor information fusion. (1998).
Tsai, W.-P., Huang, S.-P., Cheng, S.-T., Shao, K.-T., & Chang, F.-J. A data-mining framework for exploring the multi-relation between fish species and water quality through self-organizing map. Science of the Total Environment, 579, 474-483. (2017).
Tsamardinos, I., Aliferis, C. F., Statnikov, A. R., & Statnikov, E. Algorithms for large scale Markov blanket discovery. Paper presented at the FLAIRS conference. (2003).
Tsamardinos, I., Brown, L. E., & Aliferis, C. F. The max-min hill-climbing Bayesian network structure learning algorithm. Machine learning, 65, 31-78. (2006).
Uusitalo, L. Advantages and challenges of Bayesian networks in environmental modelling. Ecological modelling, 203(3-4), 312-318. (2007).
Verma, T., & Pearl, J. An algorithm for deciding if a set of observed independencies has a causal explanation. Paper presented at the Uncertainty in artificial intelligence. (1992).
Vidal-Naquet, M., & Ullman, S. Object Recognition with Informative Features and Linear Classification. Paper presented at the ICCV. (2003).
Wang, H., Ling, Z., Yu, K., & Wu, X. Towards efficient and effective discovery of Markov blankets for feature selection. Information Sciences, 509, 227-242. (2020).
Wolkenhauer, O., Wellstead, P., Cho, K.-H., & Ingalls, B. Sensitivity analysis: from model parameters to system behaviour. Essays in biochemistry, 45, 177-194. (2008).
Xanthopoulos, P., Pardalos, P. M., Trafalis, T. B., Xanthopoulos, P., Pardalos, P. M., & Trafalis, T. B. Linear discriminant analysis. Robust data mining, 27-33. (2013).
Yaramakala, S., & Margaritis, D. Speculative Markov blanket discovery for optimal feature selection. Paper presented at the Fifth IEEE International Conference on Data Mining (ICDM'05). (2005).
Yuan, G.-X., Ho, C.-H., & Lin, C.-J. Recent advances of large-scale linear classification. Proceedings of the IEEE, 100(9), 2584-2603. (2012).
Zarkami, R., Darizin, Z., Pasvisheh, R. S., Bani, A., & Ghane, A. Use of data-driven model to analyse the occurrence patterns of an indicator fish species in river: A case study for Alburnoides eichwaldii (De Filippi, 1863) in Shafaroud River, north of Iran. Ecological Engineering, 133, 10-19. (2019).
Zi, Z. Sensitivity analysis approaches applied to systems biology models. IET systems biology, 5(6), 336-346. (2011).
Zsebők, S., Estók, P., & Görföl, T. Acoustic discrimination of Pipistrellus kuhlii and Pipistrellus nathusii (Chiroptera: Vespertilionidae) and its application to assess changes in species distribution. Acta Zoologica Academiae Scientiarum Hungaricae, 58(2), 199-209. (2012).
行政院環境保護署. 鹽水溪. Retrieved 0524, 2023, from https://water.epa.gov.tw/Public/CHT/River/yanshui.aspx (2023, 0510).
林育仕. 利用基因網路分析變數間之因果關係. 國立交通大學統計學研究所碩士論文, 新竹市.取自https://hdl.handle.net/11296/bqt8gp . (2005).
林馳源. 伏流水對地表逕流水質與魚類影響之研究. 國立成功大學水利及海洋工程學系碩士論文, 台南市. 取自https://hdl.handle.net/11296/w4f9g6 . (2013).
邱映軒. 魚類群聚與環境關聯性及伏流水與地表逕流交換分析. 國立成功大學水利及海洋工程學系碩士論文, 台南市. 取自https://hdl.handle.net/11296/6njkfh . (2021).
邵廣昭. 臺灣魚類資料庫 網路電子版 Retrieved 5-24, 2023, from http://fishdb.sinica.edu.tw (2023).
柯柏隆. 淡水蝦於水生植物棲息地利用與環境因子之關係. 國立成功大學水利及海洋工程學系碩士論文, 台南市. 取自https://hdl.handle.net/11296/5e77m9 . (2022).
郭志榮. 五溝水──挽救五溝水濕地. 我們的島 Retrieved 0321, 2023, from https://ourisland.pts.org.tw/%E9%97%9C%E9%8D%B5%E5%AD%97/%E4%BA%94%E6%BA%9D%E6%B0%B4 (2012).
陳品任. 魚類群落水生植物棲地使用及地表逕流與伏流水交換的水質空間變異. 國立成功大學水利及海洋工程學系碩士論文, 台南市. 取自https://hdl.handle.net/11296/8rmf2j . (2022).
葉柏緯. 伏流水對魚類棲地之影響─ 以五溝水湧泉濕地為例. 國立成功大學水利及海洋工程學系碩士論文, 台南市. 取自https://hdl.handle.net/11296/24e24e . (2014).
鄭順林. Ch4_classification. (2022a).
鄭順林. Ch6_model_selection. (2022b).
鄭順林. Ch9_Support_Vector_Machines. (2022c).
鮑興國. Bayesian networks: National Taiwan University of Science and Technology. (2023).
鍾國芳、邵廣昭. 臺灣物種名錄 網路電子版. from https://taibnet.sinica.edu.tw/chi/taibnet_species_detail.php?name_code=381845 (2022).