Institute: North Dakota
Year Established: 2018 Start Date: 2018-03-01 End Date: 2019-02-28
Total Federal Funds: $2,250 Total Non-Federal Funds: $4,500
Principal Investigators: Halis Simsek
Abstract: Predicting the occurrence and expansion of harmful algal blooms (HABs) using mathematical modeling tools will help to control external nutrient loadings into the aquatic system. Therefore, predictive tools such as artificial intelligence techniques will be used in this study to forecast the timing and magnitude of HAB events in North Dakota freshwaters. Artiﬁcial intelligence techniques including artiﬁcial neural network (ANN) and adaptive network-based fuzzy inference systems (ANFIS) has been used extensively in a variety of context to predict and classify environmental systems. Moreover, these techniques are useful to elucidate the environmental factors, such as light intensity, temperature, wind direction and buoyancy, phosphorus and nitrogen dominant cyanobacteria, total nitrogen and phosphorous ratio that controlling toxin production and decay. All these parameters will be useful to predict the harmful cyanobacteria specious in freshwater ecosystems.