Harmful algae indexing (HaiDex) method

Harmful algal bloom (HAB, also termed red tide) has increasingly caused tremendous damage to fisheries worldwide. Since the formation process of HAB is still to be uncovered and the causes of HAB occurrence are largely unknown, it is impossible to take effective measures of prevention. At the present, the only viable measure against HAB is to forewarn and predict the occurrence of large scale HAB, which relies on a viable and efficient indexing method. Unfortunately, there is currently no reliable method to forewarn the occurrence of HAB. The HaiDex method is of a diffusion-characterized water pollution indexing technology, which is invented to effectively forecast HAB, independent of water regions around the world. To ensure forecast accuracy, the HaiDex method is invented (and claimed) to: 1) Characterize statistically a continuous formation process with imperfect panel data of water quality (e.g., missing and censored measures on factors such as water temperature and pollutant concentration, etc,); 2) Develop computationally monitoring multi-dimensional measures of water quality with adaptive filtering and updating (e.g., identifying insensitive measures); 3) Assess dynamically the likelihood of occurrence of harmful algal bloom, in the presence of discrete chaotic events, regime switching and contingent reactions. Key invention items of HaiDex method include: 1) MCMC Diffusion Simulator to computationally characterize the formation process of HAB by applying MCMC simulation (i.e., Markov chain, Monte Carlo simulation), which can be programmed on mainframe computing facility or PC with statistics software supports such as SAS and STATA. 2) Adaptive Bayesian Validation and Discrete-Choice Modeling to statistically assess the likelihood of chaotic events and regime changes, which can be developed with general econometrics and statistics software, such as STATA.