System and method of multi-modal multi-task environmental quality forecasting
AssigneeAmbient Ridge, Inc.
InventorDylan LEWIS
Life of this patent
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Abstract
In a method of multi-modal multi-task environmental quality forecasting, multi-modal data associated with a plurality of environmental input features is received. A neural sub-network of a plurality of neural sub-networks is applied to each modality of the multi-mode data associated with each environmental input feature of the plurality of environmental input features. A neural network is applied to outputs of each of the plurality of neural sub-networks, wherein the neural network includes a trained model for forecasting the plurality of environmental qualities. An environmental quality forecast is output for the plurality of environmental qualities.
Claims19
A method of multi-modal multi-task environmental quality forecasting, the method comprising:
- receiving multi-modal data associated with a plurality of environmental input features;
- applying a neural sub-network of a plurality of neural sub-networks to each modality of the multi-mode data associated with each environmental input feature of the plurality of environmental input features;
- applying a neural network to outputs of each of the plurality of neural sub-networks, the neural network comprising a trained model for forecasting a plurality of environmental qualities;
- and
- outputting an environmental quality forecast for the plurality of environmental qualities.
A non-transitory computer readable storage medium having computer readable program code stored thereon for causing a computer system to perform a method of multi-modal multi-task environmental quality forecasting, the method comprising:
- receiving multi-modal data associated with a plurality of environmental input features, the multi-modal data comprising tabular data, time-series data, and satellite image data, wherein the plurality of environmental input features are related to air quality, soil quality, and water quality;
- applying a neural sub-network of a plurality of neural sub-networks to each modality of the multi-mode data associated with each environmental input feature of the plurality of environmental input features, the plurality of neural sub-networks comprising a recurrent neural sub-network, a fully-connected neural sub-network, and a convolutional neural sub-network;
- applying a neural network to outputs of each of the plurality of neural sub-networks, the neural network comprising a trained model for forecasting a plurality of environmental qualities;
- and
- outputting an environmental quality forecast for the air quality, the soil quality, and the water quality.
A method of multi-modal multi-task environmental quality forecasting, the method comprising:
- receiving multi-modal data associated with a plurality of environmental input features the multi-modal data comprising tabular data, time-series data, and satellite image data;
- determining whether local data of at least one of the tabular data and the time-series data is unavailable for at least one environmental input feature of the plurality of environmental input features;
- applying a masking feature to a portion of the multi-modal data that is unavailable for at least one of the tabular data and the time-series data, the masking feature indicating an absence of the local data of the multi-modal data;
- applying a neural sub-network of a plurality of neural sub-networks to each modality of the multi-mode data associated with each environmental input feature of the plurality of environmental input features;
- applying a neural network to outputs of each of the plurality of neural sub-networks, the neural network comprising a trained model for forecasting a plurality of environmental qualities;
- and
- outputting an environmental quality forecast for the plurality of environmental qualities.
Dependencies are inferred from the claim text.