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Hydrologic soil groups play an important role in the determination of surface runoff, which, in turn, is crucial for soil and water conservation efforts. Traditionally, placement of soil into appropriate hydrologic groups is based on the judgement of soil scientists, primarily relying on their interpretation of guidelines published by regional or national agencies. As a result, large-scale mapping of hydrologic soil groups results in widespread inconsistencies and inaccuracies. This paper presents an application of machine learning for classification of soil into hydrologic groups. Based on features such as percentages of sand, silt and clay, and the value of saturated hydraulic conductivity, machine learning models were trained to classify soil into four hydrologic groups. The results of the classification obtained using algorithms such as k-Nearest Neighbors, Support Vector Machine with Gaussian Kernel, Decision Trees, Classification Bagged Ensembles and TreeBagger (Random Forest) were compared to those obtained using estimation based on soil texture. The performance of these models was compared and evaluated using per-class metrics and micro- and macro-averages. Overall, performance metrics related to kNN, Decision Tree and TreeBagger exceeded those for SVM-Gaussian Kernel and Classification Bagged Ensemble. Among the four hydrologic groups, it was noticed that group B had the highest rate of false positives.
Shiny Abraham; Chau Huynh; Huy Vu. Classification of Soils into Hydrologic Groups Using Machine Learning. Data 2019, 5, 2 .
AMA StyleShiny Abraham, Chau Huynh, Huy Vu. Classification of Soils into Hydrologic Groups Using Machine Learning. Data. 2019; 5 (1):2.
Chicago/Turabian StyleShiny Abraham; Chau Huynh; Huy Vu. 2019. "Classification of Soils into Hydrologic Groups Using Machine Learning." Data 5, no. 1: 2.
The efficiency of a power system is reduced when voltage drops and losses occur along the distribution lines. While the voltage profile across the system buses can be improved by the injection of reactive power, increased line flows and line losses could result due to uncontrolled injections. Also, the determination of global optimal settings for all power-system components in large power grids is difficult to achieve. This paper presents a novel approach to the application of game theory as a method for the distributed control of reactive power and voltage in a power grid. The concept of non-cooperative, extensive = form games is used to model the interaction among power-system components that have the capacity to control reactive power flows in the system. A centralized method of control is formulated using an IEEE 6-bus test system, which is further translated to a method for distributed control using the New England 39-bus system. The determination of optimal generator settings leads to an improvement in load-voltage compliance. Finally, renewable-energy (reactive power) sources are integrated to further improve the voltage-compliance level.
Ikponmwosa Idehen; Shiny Abraham; Gregory V. Murphy. A Method for Distributed Control of Reactive Power and Voltage in a Power Grid: A Game-Theoretic Approach. Energies 2018, 11, 962 .
AMA StyleIkponmwosa Idehen, Shiny Abraham, Gregory V. Murphy. A Method for Distributed Control of Reactive Power and Voltage in a Power Grid: A Game-Theoretic Approach. Energies. 2018; 11 (4):962.
Chicago/Turabian StyleIkponmwosa Idehen; Shiny Abraham; Gregory V. Murphy. 2018. "A Method for Distributed Control of Reactive Power and Voltage in a Power Grid: A Game-Theoretic Approach." Energies 11, no. 4: 962.