Abstract: Implementations set forth herein relate to determining causal relationships between covariates and value metrics for geographic regions for training one or more machine learning models. Causal relationships between different subsets of covariates and value metrics can be determined for various durations of time and for various geographic regions. For example, a value metric may exhibit a causal relationship to certain covariates for a first geographic region during a first duration of time, but may exhibit a different causal relationship to other covariates for a second geographic region for a second duration of time. Models can be trained and utilized to predict changes in value metrics for geographic regions, thereby enabling forecasting notifications to be provided to persons who may be negatively impacted by changes to those geographic regions.

Abstract: Implementations are described herein for predicting a future climate condition in an agricultural area. In various implementations, a teleconnection model may be applied to a dataset of remote climate conditions such as water surface temperatures to identify one or more of the most influential remote climate conditions on the future climate condition in the agricultural area. A trained machine learning model may be applied to the one or more most influential remote climate conditions and to historical climate data for the agricultural area to generate data indicative of the predicted future climate condition. Based on the data indicative of the predicted future climate condition, one or more output components may be caused to render output that conveys the predicted future climate condition.

Abstract: Implementations are described herein for leveraging teleconnections and location embeddings to predict geospatial measures for a geographic location of interest. In various implementations, a plurality of reference geographic locations may be identified that are disparate from a geographic location of interest and influence a geospatial measure in the geographic location of interest. One or more features may be extracted from each of the plurality of reference geographic locations. The extracted features and a location embedding generated for the geographic location of interest may be encoded into a joint embedding. A sequence encoder may be applied to the joint embedding to generate encoded data indicative of the predicted geospatial measure.

Abstract: Implementations are described herein for predicting a future climate condition in an agricultural area. In various implementations, a teleconnection model may be applied to a dataset of remote climate conditions such as water surface temperatures to identify one or more of the most influential remote climate conditions on the future climate condition in the agricultural area. A trained machine learning model may be applied to the one or more most influential remote climate conditions and to historical climate data for the agricultural area to generate data indicative of the predicted future climate condition. Based on the data indicative of the predicted future climate condition, one or more output components may be caused to render output that conveys the predicted future climate condition.

Abstract: Implementations are described herein for predicting a future climate condition in an agricultural area. In various implementations, a teleconnection model may be applied to a dataset of remote climate conditions such as water surface temperatures to identify one or more of the most influential remote climate conditions on the future climate condition in the agricultural area. A trained machine learning model may be applied to the one or more most influential remote climate conditions and to historical climate data for the agricultural area to generate data indicative of the predicted future climate condition. Based on the data indicative of the predicted future climate condition, one or more output components may be caused to render output that conveys the predicted future climate condition.

Abstract: Implementations are described herein for predicting a future climate condition in an agricultural area. In various implementations, a teleconnection model may be applied to a dataset of remote climate conditions such as water surface temperatures to identify one or more of the most influential remote climate conditions on the future climate condition in the agricultural area. A trained machine learning model may be applied to the one or more most influential remote climate conditions and to historical climate data for the agricultural area to generate data indicative of the predicted future climate condition. Based on the data indicative of the predicted future climate condition, one or more output components may be caused to render output that conveys the predicted future climate condition.