Abstract: The present invention introduces an innovative data mining technique to identify precursory signals associated with earthquakes. It involves a multistrategy approach that employs one-dimensional wavelet transformations to identify singularities in data, and analyzes the continuity of wavelet maxima in time and space to determine the singularities that could be precursory signals. Surface Latent Heat Flux (SLHF) data may be used. A single prominent SLHF anomaly may be found to be associated some days prior to a main earthquake event.

Abstract: Disclosed is a geographic information system which comprises a multithreading client and a multithreading server cluster. The multithreading client includes at least one user interface, at least one client coordinator, map data, at least one map manager, at least one client-side spatial analyzer, at least one cache manager, at least one data requester, and at least one information communicator. The multithreading server cluster includes: at least one servlet, at least one image accessor, at least one map configuration mechanism; at least one data storage access mechanism, at least one data source manager, and at least one server-side spatial manager.

Abstract: Crop yield may be assessed and predicted using a piecewise linear regression method with break point and various weather and agricultural parameters, such as NDVI, surface parameters (soil moisture and surface temperature) and rainfall data. These parameters may help aid in estimating and predicting crop conditions. The overall crop production environment can include inherent sources of heterogeneity and their nonlinear behavior. A non-linear multivariate optimization method may be used to derive an empirical crop yield prediction equation. Quasi-Newton method may be used in optimization for minimizing inconsistencies and errors in yield prediction. Minimization of least square loss function through iterative convergence of pre-defined empirical equation can be based on piecewise linear regression method with break point. This non-linear method can achieve acceptable lower residual values with predicted values very close to the observed values.

Abstract: The present invention introduces an innovative data mining technique to identify precursory signals associated with earthquakes. It involves a multistrategy approach that employs one-dimensional wavelet transformations to identify singularities in data, and analyzes the continuity of wavelet maxima in time and space to determine the singularities that could be precursory signals. Surface Latent Heat Flux (SLHF) data may be used. A single prominent SLHF anomaly may be found to be associated some days prior to a main earthquake event.

Abstract: The present invention introduces an innovative data mining technique to identify precursory signals associated with earthquakes. It involves a multistrategy approach that employs one-dimensional wavelet transformations to identify singularities in data, and analyzes the continuity of wavelet maxima in time and space to determine the singularities that could be precursory signals. Surface Latent Heat Flux (SLHF) data may be used. A single prominent SLHF anomaly may be found to be associated some days prior to a main earthquake event.

Abstract: The present invention introduces an innovative data mining technique to identify precursory signals associated with earthquakes. It involves a multistrategy approach that employs one-dimensional wavelet transformations to identify singularities in data, and analyzes the continuity of wavelet maxima in time and space to determine the singularities that could be precursory signals. Surface Latent Heat Flux (SLHF) data may be used. A single prominent SLHF anomaly may be found to be associated some days prior to a main earthquake event.

Abstract: Crop yield may be assessed and predicted using a piecewise linear regression method with break point and various weather and agricultural parameters, such as NDVI, surface parameters (soil moisture and surface temperature) and rainfall data. These parameters may help aid in estimating and predicting crop conditions. The overall crop production environment can include inherent sources of heterogeneity and their nonlinear behavior. A non-linear multivariate optimization method may be used to derive an empirical crop yield prediction equation. Quasi-Newton method may be used in optimization for minimizing inconsistencies and errors in yield prediction. Minimization of least square loss function through iterative convergence of pre-defined empirical equation can be based on piecewise linear regression method with break point. This non-linear method can achieve acceptable lower residual values with predicted values very close to the observed values.

Abstract: Disclosed is a geographic information system which comprises a multithreading client and a multithreading server cluster. The multithreading client includes at least one user interface, at least one client coordinator, map data, at least one map manager, at least one client-side spatial analyzer, at least one cache manager, at least one data requester, and at least one information communicator. The multithreading server cluster includes: at least one servlet, at least one image accessor, at least one map configuration mechanism; at least one data storage access mechanism, at least one data source manager, and at least one server-side spatial manager.