研究業績: Papers (Peer-reviewed)

査読付き論文（学術誌）(Reviewed journal papers)

  1. *Nishisue K, Sugiura R, Nakano R, Shibuya K, Fukuda S., Measuring the flight trajectory of a free-flying moth on the basis of noise-reduced 3D point cloud time series data. Insects. 15(6), 373, 2024.  https://doi.org/10.3390/insects15060373
  2. *Jibiki, T., Fukuda, S., Temporal variables improve a spatiotemporal species distribution model for the non-native freshwater fish Candidia temminckii, iScience, 27(4), 109445, 2024. https://doi.org/10.1016/j.isci.2024.109445
  3. 上脇 優人・福田 信二，ランダムフォレストを用いたハツカダイコンの根色および根形状と重量のモデル化．植物環境工学，36(1), 12-22, 2024．https://doi.org/10.2525/shita.36.12
  4. *Kamiwaki Y, Fukuda, S., A Machine Learning-Assisted Three-Dimensional Image Analysis for Weight Estimation of Radish. Horticulturae, 10(2), 142, 2024. https://doi.org/10.3390/horticulturae10020142
  5. *Kamiwaki Y, Fukuda S. Effect of the light environment on image-based SPAD value prediction of radish leaves. Algorithms, 17(1), 16, 2024. https://doi.org/10.3390/a17010016
  6. Ohira, M., Fukuda, S., Exploring functional flow heterogeneity in regulated flow regime: Fish species turnover along hydraulic gradients in an artificial waterway network. River Research and Applications, 1–11, 2023. DOI: 10.1002/rra.4199
  7. *Matsuzawa, Y., Fukuda, S., Ohira, M., De Baets, B., Modelling fish co-occurrence patterns in a small spring-fed river using a machine learning approach. Ecological Indicators, 151, 110234, 2023. DOI: 10.1016/j.ecolind.2023.110234
  8. *笠原太一・福田信二・木村匡臣・浅田洋平・乃田啓吾，石垣島宮良川流域の物理環境および魚類相の流程分布と河川横断構造物の影響．土木学会論文集，79(1)，論文ID: G-0268，2023．DOI: 10.2208/jscejj.G-0268
  9. Yokota, K., Matsuzawa, Y., Fukuda, S., Takada, H., Mizukawa, K., Species-specific debromination of BDE99 in teleost fish: The relationship between debromination ability and bioaccumulation patterns of PBDEs, Science of The Total Environment, 806(3), 151265, 2022. DOI: 10.1016/j.scitotenv.2021.151265.
  10. Garcia-Vega, A., Fuentes-Perez, J.F., Fukuda, S., Kruusmaa, M., Sanz-Ronda, F.J., Tuhtan, J.A., Artificial lateral line for aquatic habitat modelling: An example for Lefua echigonia, Ecological Informatics, 65, 101388, 2021. DOI: 10.1016/j.ecoinf.2021.101388
  11. 松澤優樹・福田信二・大平　充，都市湧水河川におけるホトケドジョウ未成魚の生息環境評価．土木学会論文集B1（水工学），76(2), I_1321-I_1326，2020．
  12. 福田信二・青木興成・松澤優樹，ホトケドジョウの臨界遊泳速度と粗石による生息環境修復効果の定量評価．土木学会論文集B1（水工学），76(2), I_1327-I_1332，2020．
  13. *相原星哉・福田信二，群集解析と生息場モデルに基づく魚類の生息環境評価．農業農村工学会論文集，88(1)，I_93-I_103, 2020. DOI: 10.11408/jsidre.88.I_93
  14. 松澤優樹・福田信二，ランダムフォレストを用いたホトケドジョウの生息環境評価モデルの構築．土木学会論文集B1（水工学）, 75(2), I_541-I_546, 2019.
  15. De-Miguel-Gallo, M., Martinez-Capel, F., Munoz-Mas, R., Aihara, S., Matsuzawa, Y., Fukuda, S., Habitat evaluation for the endangered fish species Lefua echigonia in the Yagawa River, Japan. Journal of Ecohydraulics, 4(2), 147-157, 2019. DOI: 10.1080/24705357.2019.1614886
  16. Boets, P., Laverty, C., Fukuda, S., Verreycken, H., Green, K., Britton, R.J., Caffrey, J., Goethals, P.L.M., Pegg, J., Medoc., V., Dick, J.T.A., Intra- and intercontinental variation in the functional responses of a high impact alien invasive fish. Biological Invasions, 21(5), 1751-1762, 2019.
  17. Takeda, N., Lopez-Galvis, L., Pineda, D., Castilla, A., Takahashi, T., Fukuda, S., Okada, K., Estimating soil water contents from field water tables for potential rice irrigation criteria under contour-levee irrigation systems. Environmental Control in Biology, 57(2), 15–21, 2019.
  18. Takeda, N., Lopez-Galvis, L., Pineda, D., Castilla, A., Takahashi, T., Fukuda, S., Okada, K., Evaluation of water dynamics of contour-levee irrigation system in sloped rice fields in Colombia. Agricultural Water Management, 217, 107–118, 2019.
  19. *Ohira, M., Fukuda, S., Flow regime shapes seasonal patterns of fish species richness and abundance in main and branch channels of a rice irrigation system. Paddy and Water Environment, 16(4), 783–793, 2018. 
  20. Fukuda, S., Spreer, W., Wiriya-Alongkorn, W., Spohrer, K., Yasunaga, E., Tiyayon, C., Random Forests as a tool for analyzing partial drought stress based on CO2 concentrations in the rootzone of longan trees. Environmental Control in Biology, 56(2), 25–31, 2018．
  21. Yasunaga, E., Fukuda, S., Nagle, M., Spreer, W., Effect of storage conditions on the postharvest quality changes of fresh mango fruits for export during transportation. Environmental Control in Biology, 56(2), 39–44, 2018．
  22. Yasunaga, E., Fukuda, S., Takata, D., Spreer, W., Sardsud, V., Nakano, K., Quality changes in fresh mango fruits (Mangifera indica L. ‘Nam Dok Mai’) under actual distribution temperature profile from Thailand to Japan. Environmental Control in Biology, 56(2), 45–49, 2018．
  23. Tasmin, R., Shimasaki, Y., Tsuyama, M., Qiu, X., Khalil, F., Mukai, K., Khanam, M.R.M., Yamada, N., Fukuda, S., Kang, I.J., Oshima, Y., Effects of water temperature and light intensity on the acute toxicity of herbicide thiobencarb to a green alga, Raphidocelis subcapitata. Environmental Science and Pollution Research, 25, 25363–25370, 2018.
  24. Muñoz-Mas, R., Fukuda, S., Pórtoles, J., Martinez-Capel, F., Revisiting probabilistic neural networks: a comparative study with support vector machines and the microhabitat suitability for the Eastern Iberian chub (Squalius valentinus). Ecological Informatics, 43, 24–37, 2018.
  25. Muñoz-Mas, R., Fukuda, S., Vezza, P., Martinez-Capel, F., Comparing four methods for decision-tree induction: a case study on the invasive Iberian gudgeon (Gobio lozanoi; Doadrio & Madeira, 2004). Ecological Informatics, 34, 22–34, 2016.
  26. Fukuda, S., De Baets, B., Data prevalence matters when assessing species’ responses using data-driven species distribution models. Ecological Informatics, 32, 69–78, 2016.
  27. Fukuda, S., Tanakura, T., Hiramatsu, K., Harada, M., Assessment of spatial habitat heterogeneity by coupling data-driven habitat suitability models with a 2D hydrodynamic model in small-scale streams. Ecological Informatics, 29, 147–155, 2015.
  28. *Nguyen Viet Anh, Fukuda, S., Hiramatsu, K., Harada, M., Sensitivity-based calibration of the soil and water assessment tool for hydrologic cycle simulation in the Cong Watershed, Vietnam. Water Environment Research, 87(8), 735–750, 2015.
  29. Miyazawa, Y., Manythong Chanhsom, Fukuda, S., Ogata, K., Comparison of the growth traits of a commercial pioneer tree species, paper mulberry (Broussonetia papyrifera L. Vent.), with those of shade-tolerant tree species: investigation of the ecophysiological mechanisms underlying shade-intolerance. Agroforestry Systems, 88(5), 907–919, 2014.
  30. 原田昌佳・平松和昭・福田信二，有機汚濁が進む閉鎖性水域の嫌気的・還元的条件下での水質動態．日本雨水資源化システム学会誌，20(1), 49–55, 2014．
  31. Fukuda, S., Yasunaga, E., Nagle, M., Yuge, K., Sardsud, V., Spreer, W., Muller, J., Modelling the relationship between peel colour and the quality of fresh mango fruit using Random Forests. Journal of Food Engineering. 131, 7–17, 2014.
  32. Tasmin, R., Shimasaki, Y., Qiu, X., Honda, M., Tsuyama, M., Yamada, N., Fukuda, S., Oshima, Y., Elevated temperatures and low nutrients decrease the toxicity of diuron for growth of the green alga Pseudokirchneriella subcapitata. Japanese Journal of Environmental Toxicology, 17(1), 1–10, 2014.
  33. Tasmin, R., Shimasaki, Y., Tsuyama, M., Qiu, X., Khalil, F., Okino, N., Yamada, N., Fukuda, S., Kang, I.J., Oshima, Y., Elevated water temperature reduces the acute toxicity of the widely used herbicide diuron to a green alga, Pseudokirchneriella subcapitata. Environmental Science and Pollution Research, 21(2), 1064–1070, 2014.
  34. Do Thuy Nguyen, Harada, M., Hiramatsu, K., Fukuda, S., Application of a simple genetic algorithm for the calibration of aquatic ecosystem model of an agricultural pond. Paddy and Water Environment, 12(1), 1–15, 2014
  35. Fukuda, S., De Baets, B., Waegeman, W., Verwaeren, J., Mouton, A.M., Habitat prediction and knowledge extraction for spawning European grayling (Thymallus thymallus L.) using a broad range of species distribution models. Environmental Modelling & Software, 47, 1–6, 2013.
  36. Fukuda, S., De Baets, B., Onikura, N., Nakajima, J., Mukai, T., Mouton, A.M., Modelling the distribution of the pan-continental invasive fish Pseudorasbora parva based on landscape features in the northern Kyushu Island, Japan. Aquatic Conservation: Marine and Freshwater Ecosystems, 23(6), 901–910, 2013.
  37. Onikura, N., Miyake, T., Nakajima, J., Fukuda, S., Kawamato, T., Kawamura, K., Predicting potential hybridization between native and non-native Rhodeus ocellatus subspecies: the implications for conservation of a pure native population in northern Kyushu, Japan. Aquatic Invasions, 8(2), 219–229, 2013.
  38. Yuge, K., Yasunaga, E., Fukuda, S., Spreer, W., Sardsud, V., Pattanopo, W., Evaluation of soil water management difference in mango orchards between Thailand and Japan. American Journal of Plant Sciences, 4(1), 182–187, 2013.
  39. Fukuda, S., Spreer, W., Yasunaga, E., Yuge, K., Sardsud, V., Muller, J., Random Forests modelling for the estimation of mango (Mangifera indica L. cv. Chok Anan) fruit yields under different irrigation regimes. Agricultural Water Management, 116 , 142–150, 2013.
  40. Fukuda, S., De Baets, B., Do absence data matter when modelling fish habitat preference using a genetic Takagi-Sugeno fuzzy model? International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 20, Suppl. 2, 233–245, 2012.
  41. Fukuda, S., Mouton, A.M., De Baets, B., Abundance versus presence/absence data for modelling fish habitat preference with a genetic Takagi-Sugeno fuzzy system. Environmental Monitoring and Assessment, 184(10), 6159–6171, 2012.
  42. Onikura, N., Nakajima, J., Miyake, T., Kawamura, K., Fukuda, S., Predicting distributions of seven bitterling fishes in northern Kyushu, Japan. Ichthyological Research, 59, 124–133, 2012.
  43. Onikura, N., Nakajima, J., Inui, R., Mizutani, H., Kobayakawa, M., Fukuda, S., Mukai T., Evaluating the potential for invasion by alien freshwater fishes in northern Kyushu Island, Japan, using the Fish Invasiveness Scoring Kit. Ichthyological Research, 58(4), 382–387, 2011.
  44. Fukuda, S., Assessing the applicability of fuzzy neural networks for habitat preference evaluation of Japanese medaka (Oryzias latipes). Ecological Informatics, 6, 286-295, 2011.
  45. 福田信二・増田慎也・平松和昭・原田昌佳，HSIモデルを用いたメダカの生息場選好性評価におけるデータ形式およびカテゴリー化手法の影響. 農業農村工学会論文集, 272, 79(2), 11-19, 2011.
  46. Fukuda, S., De Baets, B., Mouton, A.M., Waegeman, W., Nakajima, J., Mukai, T., Hiramatsu, K., Onikura, N., Effect of model formulation on the optimization of a genetic Takagi-Sugeno fuzzy system for fish habitat suitability evaluation. Ecological Modelling, 222, 1401-1413, 2011.
  47. Fukuda, S., Kang, I.J., Moroishi, J., Nakamura, A., The application of entropy for detecting behavioural responses in Japanese medaka (Oryzias latipes) exposed to different toxicants. Environmental Toxicology, 25, 446-455, 2010.
  48. Fukuda, S., Consideration of fuzziness: Is it necessary in modelling fish habitat preference of Japanese medaka (Oryzias latipes)? Ecological Modelling, 220, 2877-2884, 2009.
  49. Aye, S.S., Fukuda, S., Matsumoto M., Chemotaxonomic characterization of rice seedling blight complex using fatty acid methyl ester (FAME) profiles. MYCOSCIENCE, 49(6), 373-378, 2008.
  50. Fukuda, S., Hiramatsu, K., Prediction ability and sensitivity of artificial intelligence-based habitat preference models for predicting spatial distribution of Japanese medaka (Oryzias latipes). Ecological Modelling, 215(4), 301-313, 2008.
  51. Fukuda, S., Okushima, S., Assessing nonlinearity in fish habitat preference of Japanese medaka (Oryzias latipes) using genetic algorithm-optimized habitat prediction models. JARQ-Japan Agricultural Research Quarterly, 42(2), 97-107, 2008.
  52. Fukuda, S., Hiramatsu, K., Prediction ability of resource selection functions for the estimation of fish habitat preference, Trans. JSIDRE, 247, 113-118, 2007.
  53. Fukuda, S., Hiramatsu, K. Mori, M., Fuzzy neural network model for habitat prediction and HEP for habitat quality estimation focusing on Japanese medaka (Oryzias latipes) in agricultural canals. Paddy and Water Environment, 4, 119-124, 2006.
  54. Fukuda, S., Hiramatsu, K., Mori, M., Shikasho, S., Numerical quantification of the significance of aquatic vegetation affecting spatial distribution of Japanese medaka (Oryzias latipes) in an agricultural canal. Landscape and Ecological Engineering, 2(1), 65-80, 2006.
  55. 福田信二・平松和昭・森牧人・四ヶ所四男美，農業用水路におけるメダカの生息場選好性のあいまいさに関する数理表現，農業土木学会論文集. 239, 43-49, 2005.
  56. 平松和昭・福田信二・四ヶ所四男美，ファジィ推論によるメダカの環境応答モデルの開発. 農業土木学会論文集, 228, 65-72, 2003.

 

査読付き論文（国際会議） (Reviewed conference papers)

  1. Ishikawa, W., Fukuda, S., Application of YOLOv5 and MASK R-CNN for diurnal activity monitoring of freshwater fish. Proceedings of the 2022 Joint 12th International Conference on Soft Computing and Intelligent Systems and 23rd International Symposium on Advanced Intelligent Systems (SCIS&ISIS), 2022. DOI: 10.1109/SCISISIS55246.2022.10001948
  2. Ogami, T., Fukuda, S., Kakudo, H., Modelling the relationships between local landscape features and habitat suitability of Genji-firefly Luciola cruciata using Random Forests. Proceedings of the 2022 Joint 12th International Conference on Soft Computing and Intelligent Systems and 23rd International Symposium on Advanced Intelligent Systems (SCIS&ISIS), 2022. DOI: 10.1109/SCISISIS55246.2022.10001898
  3. Kamiwaki, Y., Fukuda, S., Evolutionary optimization of stereo matching parameters for plant shape estimation. Proceedings of the 2022 Joint 12th International Conference on Soft Computing and Intelligent Systems and 23rd International Symposium on Advanced Intelligent Systems (SCIS&ISIS), 2022. DOI: 10.1109/SCISISIS55246.2022.10002113  
  4. Fukuda, S., Data aggregation for ecohydraulic monitoring. Proceedings of the 2018 Joint 10th International Conference on Soft Computing and Intelligent Systems and 19th International Symposium on Advanced Intelligent Systems, 1239-1242, 2018.
  5. Fukuda, S., Ohira, M., Data-driven habitat modelling using high resolution ecohydraulic data in an agricultural canal.? Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems. Paper ID 271, 2017.
  6. Matsuzawa, Y., Ohira, M., Fukuda, S., Microhabitat modelling for an endangered freshwater fish, Lefua echigonia, in a spring-fed urban stream. Proceedings of the 37th IAHR World Congress, 2781-2785, 2017.
  7. Fukuda, S., Tuhtan, J.A., Fuentes-Perez, J.F., Schletterer, M., Kruusmaa, M., Random forests hydrodynamic flow classification in a vertical slot fishway using a bioinspired artificial lateral line probe. Lecture Notes in Computer Science 9835, 297-307. Intelligent Robotics and Applications. Springer International Publishing, 2016. DOI: 10.1007/978-3-319-43518-3_29
  8. Fukuda, S., Hiramatsu, K., Harada, M., Assessment of depth measurement using an acoustic Doppler current profiler and a CTD profiler in a small river in Japan. Lecture Notes in Computer Science 9835, 308-316. Intelligent Robotics and Applications. Springer International Publishing, 2016. DOI: 10.1007/978-3-319-43518-3_30
  9. Yasunaga, E., Fukuda, S., Spreer, W., Takata, D., Online monitoring system on controlled irrigation experiment for export quality mango in Thailand. Lecture Notes in Computer Science 9835, 328-334. Intelligent Robotics and Applications. Springer International Publishing, 2016. DOI: 10.1007/978-3-319-43518-3_32
  10. Charoenkit, N., Naphrom, D., Sruamsiri, P., Sringarm, K., Fukuda, S., Modeling the relationship between hormone dynamics and off-season flowering of litchi by using Random Forests. Agriculture and Agricultural Science Procedia, 5, 9-16, 2015.
  11. Fukuda, S., Tanaka, K., Hanada, M., Harada, M., Hiramatsu, K., Application of ultrasonic telemetry for habitat suitability assessment of freshwater fish: A case study in a small river in Japan. Proceedings of the ISE 2014, Paper ID: 230, 2014.
  12. Fukuda, S., Effects of data prevalence on species distribution modelling using a genetic Takagi-Sugeno fuzzy system. Proceedings of the SSCI 2013 GEFS, 2013.
  13. Yasunaga, E., Fukuda, S., Yuge, K., Sardsud, V., Spreer, W., Pattanapo, W., Comparison of postharvest quality changes of export mango fruit from different harvest sites in Thailand. Acta Horticulturae, 1006, 423–428, 2013.
  14. Yasunaga, E., Fukuda, S., Yuge, K., Sardsud, V., Spreer, W., Pattanapo, W., Comparison of changes in post-harvest quality deterioration of mango fruits between Thailand-Fukuoka and Okinawa-Fukuoka transportations. Acta Horticulturae, 989, 221–224, 2013.
  15. Fukuda, S., De Baets, B., A short review on the application of computational intelligence and machine learning in the bioenvironmental sciences. Proceedings of SCIS&ISIS2012, 106-110, 2012.
  16. Fukuda, S., Effect of aggregation functions on the habitat preference modelling using a genetic Takagi-Sugeno fuzzy system. Proceedings of the FUZZ-IEEE2012, 1-8, 2012.
  17. Yasunaga, E., Yuge, K., Fukuda, S., Sardsud, V., Spreer, W., Pattanapo, W., Effect of post-harvest distribution environment on quality deterioration of mango fruits. Acta Horticulturae, 934, 921-927, 2012.
  18. Yuge, K., Yasunaga, E., Fukuda, S., Sardsud, V., Spreer, W., Pattanapo, W., Evaluation of the effect of the soil water environment in mango fields on the fruit quality. Acta Horticulturae,938, 445-451, 2012.
  19. Fukuda, S., De Baets, B., Nojima, Y., Comparing predictive accuracy of a genetic Takagi-Sugeno fuzzy model and random forests for fish habitat modelling. Proceedings of the IWACIII2011, paper ID: GS2-3, 2011.
  20. Fukuda, S., Application of a fuzzy neural network model to evaluate habitat preference of Japanese medaka (Oryzias latipes). Proceeding of the 2011 IFSA World Congress and the 2011 AFSS, HF001, 1-4, 2011.
  21. Fukuda, S., Assessing the effects of zero abundance data on habitat preference modelling using a genetic Takagi-Sugeno fuzzy model. Proceeding of the FUZZ-IEEE 2011, 272-277, 2011.
  22. Fukuda, S., De Baets, B., Waegeman, W., Mouton, A.M., Nakajima, J., Mukai, T., Onikura, N., A discussion on the accuracy-complexity relationship in modelling fish habitat preference using a genetic Takagi-Sugeno fuzzy system. Proceedings of the SSCI 2011 GEFS, 81-86, 2011.
  23. Fukuda, S., Onikura, N., De Baets, B., Waegeman, W., Mouton, AM., Nakajima, J., Mukai, T., A genetic Takagi-Sugeno fuzzy system for fish habitat preference modelling. Proceeding of the NaBIC2010, 281-286, 2010.
  24. Fukuda, S., Effect of data quality on habitat preference evaluation for Japanese medaka (Oryzias latipes) using a simple genetic fuzzy system. Proceedings of FUZZ-IEEE2010, 1231-1237, 2010.
  25. Fukuda, S., Assessing transferability of genetic algorithm-optimized fuzzy habitat preference models for Japanese medaka (Oryzias latipes). Proceedings of GEFS2010, 57-62, 2010.
  26. Fukuda, S., Trinh Quang Huy, Pham Van Cuong, Ho Thi Lam Tra, Shimasaki, Y., Araki, T., Mori, Y., Matsumoto, M., Ha Viet Cuong, Do Nguyen Hai, Mathematical modelling on nitrogen dynamics of paddy field waters in Red River Delta, Vietnam. Proceedings of the PAWEES2009, GI.8, 1-10, 2009.
  27. Fukuda, S., A preliminary analysis for improving model structure of fuzzy habitat preference model for Japanese medaka (Oryzias latipes). Proceedings: IFSA/EUSFLAT2009, 1258-1263, 2009.
  28. Fukuda, S., Uncertainty analysis on fuzzy neural network model for evaluating fish habitat preference of Japanese medaka (Oryzias latipes) in agricultural canals in Japan. Proceedings of the 7th International Symposium on Ecohydraulics, Paper ID: conf187a145, 2009.
  29. Hiramatsu, K., Shikasho, S., Fukuda, S., Mathematical modeling of preference intensity of Japanese medaka fish for instream water environment using fuzzy reasoning. 2004 ASAE/CSAE Annual Meeting Paper, Paper Number: 043069, 2004.