Kun Shan is an Associate Professor at the Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences. His research primarily centres on monitoring and predicting harmful algal blooms in reservoir and lake ecosystems using multi-sensors and AI approaches. He has authored over 50 peer-reviewed papers in prestigious international journals (e.g., Water Research, etc.), with one top-cited paper in Harmful Algae from 2019 to 2021. He is currently leading over ten research projects in ecological informatics, supported by national and local natural science foundations. He is serving as an editorial board member of Ecological Informatics, Water, IJERPH, etc.

harmful algae blooms; statistical machine learning; deep neural network; real-time monitoring

[1]. Achieved the First Prize for Technological Progress in Geographic Information Science and Technology in China in 2022.

[2]. Received the Special Prize for Science and Technology in Ecological Environment in Chongqing in 2022.

[3]. Recognized as an Outstanding Reviewer for the Journal "Lake Sciences" in the years 2020 and 2022.

[4]. Honored with the Young Outstanding Presentation Award at the 15th Ecology Conference in 2016.

[5]. Granted the Student Award by the American Society of Limnology and Oceanography (ALSO) in 2012.

[1].Kun Shan#*, Ouyang Tina, Xiaoxiao Wang, Hong Yang, Botian Zhou, Zhongxing Wu, Mingsheng Shang. Temporal prediction of algal parameters in Three Gorges Reservoir based on highly time-resolved monitoring and long short-term memory network. Journal of Hydrology, 2022, 605, 127304.  

[2].Qichao Zhou#*, Yun Zhang, Juan Tao, Lin Ye, Haijun Wang, Kun Shan*, Erik Jeppesen, Lirong Song. Water depth and land-use intensity indirectly determine phytoplankton functional diversity and further regulate resource use efficiency at a multi-lake scale. Science of the Total Environment, 2022, 155303.  

[3].XiaoXiao Wang#, Lan Wang, MingSheng Shang, Lirong Song, Kun Shan*. Revealing physiochemical factors and Zooplankton influencing Microcystis bloom toxicity in a large-shallow lake using Bayesian machine. Toxins. 2022, 14, 530.  

[4].Lili Hu#, Kun Shan*, Licheng Huang, Yuanrui Li, Lei Zhao, Qichao Zhou*, Lirong Song. Environmental factors associated with cyanobacterial assemblages in a mesotrophic subtropical plateau lake: A focus on bloom toxicity. Science of the Total Environment, 2021,778,146052.  

[5].Kun Shan#*, Xiaoxiao Wang, Hong Yang, Botian Zhou, Lirong Song, Mingsheng Shang. Use statistical machine learning to detect nutrient thresholds in Microcystis blooms and microcystin management. Harmful Algae, 2020, 94, 101807.  

[6].Kun Shan#*, Lirong Song*, Wei Chen, Lin Li, Liming Liu, Yanlong Wu, Yunlu Jia, Qichao Zhou, Liang Peng. Analysis of environmental drivers influencing interspecific variations and associations among bloom-forming cyanobacteria in large, shallow eutrophic lakes. Harmful Algae, 2019, 84, 84-94.  

[7].Kun Shan#*, Mingsheng Shang, Botian Zhou, Lin Li, Xiaoxiao Wang, Hong Yang, Lirong Song*. Application of Bayesian network including Microcystis morphospecies for microcystin risk assessment in three cyanobacterial bloom-plagued lakes. Harmful Algae, 2019, 83, 14-24.

[8].Botian Zhou#, Mingsheng Shang, Sheng Zhang, Li Feng, Xiangnan Liu, Ling Wu, Lei Feng, Kun Shan*. Remote examination of the seasonal succession of phytoplankton assemblages from time-varying trends. Journal of Environmental Management. 2019, 246:687-694.  

[9].Botian Zhou#, Mingsheng Shang, Guoyin Wang, Sheng Zhang, Li Feng, Xiangnan Liu, Ling Wu, Kun Shan*. Distinguishing two phenotypes of blooms using the normalised difference peak-valley index (NDPI) and Cyano-Chlorophyta index (CCI). Science of the Total Environment, 2018, 628, 848-857.  

[10]. Kun Shan#, Lin Li#, Xiaoxiao Wang, Yanlong Wu, Lili Hu, Gongliang Yu, Lirong Song*.  Modelling ecosystem structure and trophic interactions in a typical cyanobacterial bloom-dominated shallow Lake Dianchi, China. Ecological modelling, 2014, 291, 82-95.