Support vector machine applications in the field of hydrology: a review
PC Deka - Applied soft computing, 2014 - Elsevier
In the recent few decades there has been very significant developments in the theoretical
understanding of Support vector machines (SVMs) as well as algorithmic strategies for …
understanding of Support vector machines (SVMs) as well as algorithmic strategies for …
A multi-verse optimizer approach for feature selection and optimizing SVM parameters based on a robust system architecture
Support vector machine (SVM) is a well-regarded machine learning algorithm widely
applied to classification tasks and regression problems. SVM was founded based on the …
applied to classification tasks and regression problems. SVM was founded based on the …
A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series
Develo** a hydrological forecasting model based on past records is crucial to effective
hydropower reservoir management and scheduling. Traditionally, time series analysis and …
hydropower reservoir management and scheduling. Traditionally, time series analysis and …
Support vector regression for real-time flood stage forecasting
PS Yu, ST Chen, IF Chang - Journal of hydrology, 2006 - Elsevier
Flood forecasting is an important non-structural approach for flood mitigation. The flood
stage is chosen as the variable to be forecasted because it is practically useful in flood …
stage is chosen as the variable to be forecasted because it is practically useful in flood …
Using support vector machines for long-term discharge prediction
Accurate time-and site-specific forecasts of streamflow and reservoir inflow are important in
effective hydropower reservoir management and scheduling. Traditionally, autoregressive …
effective hydropower reservoir management and scheduling. Traditionally, autoregressive …
Predicting monthly streamflow using data‐driven models coupled with data‐preprocessing techniques
CL Wu, KW Chau, YS Li - Water Resources Research, 2009 - Wiley Online Library
In this paper, the accuracy performance of monthly streamflow forecasts is discussed when
using data‐driven modeling techniques on the streamflow series. A crisp distributed support …
using data‐driven modeling techniques on the streamflow series. A crisp distributed support …
Adaptive neuro-fuzzy inference system coupled with shuffled frog lea** algorithm for predicting river streamflow time series
Accurate runoff forecasting plays a key role in catchment water management and water
resources system planning. To improve the prediction accuracy, one needs to strive to …
resources system planning. To improve the prediction accuracy, one needs to strive to …
Comparison of stochastic and machine learning methods for multi-step ahead forecasting of hydrological processes
Research within the field of hydrology often focuses on the statistical problem of comparing
stochastic to machine learning (ML) forecasting methods. The performed comparisons are …
stochastic to machine learning (ML) forecasting methods. The performed comparisons are …
Urban land-use classification using machine learning classifiers: comparative evaluation and post-classification multi-feature fusion approach
Accurate spatial-temporal map** of urban land-use and land-cover (LULC) provides
critical information for planning and management of urban environments. While several …
critical information for planning and management of urban environments. While several …
Hydrologically informed machine learning for rainfall‐runoff modeling: A genetic programming‐based toolkit for automatic model induction
Abstract Models of water resources systems are conceived to capture the underlying
environmental dynamics occurring within watersheds. All such models can be regarded as …
environmental dynamics occurring within watersheds. All such models can be regarded as …