Artificial Intelligence by Priyanka Garsole
This paper presents the study of Support Vector Regression (SVR) to forecast the future streamflo... more This paper presents the study of Support Vector Regression (SVR) to forecast the future streamflow discharge
using past streamflow and rainfall data, which is closely related to regularization network and Gaussian
processes. A Gaussian Radial Basis Function (RBF) kernel framework was built on the dataset to optimize the
tuning parameters and to obtain the moderated output. It has been observed from various studies that the
prediction ability of RBF kernel function is better for regression problems. The training process of Support
Vector Machine (SVM) involves the selection of both kernel parameters and regularization constants. The
constants such as γ, ε, C and σ are the parameters of SVR which were optimized to obtain the desired outputs.
Where parameter C determines trade-off between model complexity and degree to which deviations are larger
than ε are tolerated in optimization formulation, and the parameter ε controls the width of ε –sensitive zone. The
study area include the upstream part of Jayakwadi dam. The prediction is based on flow at two Gauge and
Discharge Sites (GDS) i.e. Kopargaon and Nagamthan and rainfall data at three Standard Rain gauge Stations
(SRG) i.e. Rahata, Khirdisathe, and Wadala Mahadev in Maharashtra, India. The daily data values of these
stations for 18 years were collected from Hydrological Data User Group (HDUG), Nashik. About 70% of data
were used for training purpose and remaining 30% of data were used for testing purpose. The experimental
results showed that the SVR algorithm is reliable and efficient method for streamflow prediction, which has an
important impact on the water resources management of region.
Keywords: Support Vector Machine (SVM), Support Vector Regression (SVR), Radial Basis Function (RBF)
Kernel, ε-sensitive zone, regularization parameters.
Papers by Priyanka Garsole
AQUA — Water Infrastructure, Ecosystems and Society
Seepage is the phenomenon of water infiltrating through a gravity dam's foundation, causing e... more Seepage is the phenomenon of water infiltrating through a gravity dam's foundation, causing erosion and weakening the dam's construction over time. If not properly managed, this can eventually lead to the dam's catastrophic failure, posing a significant danger to public safety and the environment. As a result, precise seepage prediction in gravity dams is essential for ensuring their safety and stability. This review paper looks at the use of artificial intelligence (AI) techniques for predicting seepage in gravity dams, as well as the challenges and possible solutions. The paper identifies and suggests potential solutions to the challenges connected with using AI for seepage prediction, such as data quality and model interpretability. The paper also covers future research paths, such as the creation of advanced machine learning algorithms and the improvement of data collection and processing. Overall, this review gives insight on the current state of the art in using AI...
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Artificial Intelligence by Priyanka Garsole
using past streamflow and rainfall data, which is closely related to regularization network and Gaussian
processes. A Gaussian Radial Basis Function (RBF) kernel framework was built on the dataset to optimize the
tuning parameters and to obtain the moderated output. It has been observed from various studies that the
prediction ability of RBF kernel function is better for regression problems. The training process of Support
Vector Machine (SVM) involves the selection of both kernel parameters and regularization constants. The
constants such as γ, ε, C and σ are the parameters of SVR which were optimized to obtain the desired outputs.
Where parameter C determines trade-off between model complexity and degree to which deviations are larger
than ε are tolerated in optimization formulation, and the parameter ε controls the width of ε –sensitive zone. The
study area include the upstream part of Jayakwadi dam. The prediction is based on flow at two Gauge and
Discharge Sites (GDS) i.e. Kopargaon and Nagamthan and rainfall data at three Standard Rain gauge Stations
(SRG) i.e. Rahata, Khirdisathe, and Wadala Mahadev in Maharashtra, India. The daily data values of these
stations for 18 years were collected from Hydrological Data User Group (HDUG), Nashik. About 70% of data
were used for training purpose and remaining 30% of data were used for testing purpose. The experimental
results showed that the SVR algorithm is reliable and efficient method for streamflow prediction, which has an
important impact on the water resources management of region.
Keywords: Support Vector Machine (SVM), Support Vector Regression (SVR), Radial Basis Function (RBF)
Kernel, ε-sensitive zone, regularization parameters.
Papers by Priyanka Garsole
using past streamflow and rainfall data, which is closely related to regularization network and Gaussian
processes. A Gaussian Radial Basis Function (RBF) kernel framework was built on the dataset to optimize the
tuning parameters and to obtain the moderated output. It has been observed from various studies that the
prediction ability of RBF kernel function is better for regression problems. The training process of Support
Vector Machine (SVM) involves the selection of both kernel parameters and regularization constants. The
constants such as γ, ε, C and σ are the parameters of SVR which were optimized to obtain the desired outputs.
Where parameter C determines trade-off between model complexity and degree to which deviations are larger
than ε are tolerated in optimization formulation, and the parameter ε controls the width of ε –sensitive zone. The
study area include the upstream part of Jayakwadi dam. The prediction is based on flow at two Gauge and
Discharge Sites (GDS) i.e. Kopargaon and Nagamthan and rainfall data at three Standard Rain gauge Stations
(SRG) i.e. Rahata, Khirdisathe, and Wadala Mahadev in Maharashtra, India. The daily data values of these
stations for 18 years were collected from Hydrological Data User Group (HDUG), Nashik. About 70% of data
were used for training purpose and remaining 30% of data were used for testing purpose. The experimental
results showed that the SVR algorithm is reliable and efficient method for streamflow prediction, which has an
important impact on the water resources management of region.
Keywords: Support Vector Machine (SVM), Support Vector Regression (SVR), Radial Basis Function (RBF)
Kernel, ε-sensitive zone, regularization parameters.