Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  describes the use of machine learning methods to build a decision support system for predicting the distribution of coastal ocean algal blooms based on remote sensing data in Monterey Bay. This system can help scientists obtain prior information in a large ocean region and formulate strategies for deploying robots in the coastal ocean for more detailed in situ exploration. The difficulty is that there are insufficient in situ data to create a direct statistical machine learning model with satellite data inputs. To solve this problem,  built a Random Forest model using MODIS and MERIS satellite data and applied a threshold filter to balance the training inputs and labels. To build this model, several features of remote sensing satellites re tested to obtain the most suitable features for the system. After building the model,  compared our random forest model with previous trials based on a Support Vector Machine (SVM) using satellite data from 221 days, and our approach performed significantly better. Finally,  used the latest in situ data from a September 2014 field experiment to validate our model.
['Weilong Song', 'John M. Dolan', 'Danelle E. Cline', 'Guangming Xiong']
I am writing a research paper on a idea Learning-Based Algal Bloom Event Recognition for Oceanographic Decision Support System Using Remote Sensing Data
[{"from": "human", "value": "I am writing a research paper on a idea Learning-Based Algal Bloom Event Recognition for Oceanographic Decision Support System Using Remote Sensing Data"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  describes the use of machine learning methods to build a decision support system for predicting the distribution of coastal ocean algal blooms based on remote sensing data in Monterey Bay. This system can help scientists obtain prior information in a large ocean region and formulate strategies for deploying robots in the coastal ocean for more detailed in situ exploration. The difficulty is that there are insufficient in situ data to create a direct statistical machine learning model with satellite data inputs. To solve this problem,  built a Random Forest model using MODIS and MERIS satellite data and applied a threshold filter to balance the training inputs and labels. To build this model, several features of remote sensing satellites re tested to obtain the most suitable features for the system. After building the model,  compared our random forest model with previous trials based on a Support Vector Machine (SVM) using satellite data from 221 days, and our approach performed significantly better. Finally,  used the latest in situ data from a September 2014 field experiment to validate our model."}]