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Quantitative Endosurgery Process Analysis by Machine Learning Method Bojan Nokovic , Andrew Lambe (pages: 1-7) Modelling Student Performance in a Structural Steel Graduate-Based Module: A Comparative Analysis Between K-Nearest Neighbor and Dummy Classifiers Masengo Ilunga , Omphemetse Zimbili , Phahlani Mampilo , Agarwal Abhishek (pages: 8-15) Interoperable Digital Skills for Foreign Languages Education in the COVID-19 Paradigm Rusudan Makhachashvili , Ivan Semenist , Iryna Vorotnykova (pages: 16-20) Education, Training and Informatics Go Hand in Hand in (Foreign) Higher Education Institutions (HEIs) – Case Studies From Live and Online Classrooms Ekaterini Nikolarea (pages: 21-29) Enhancing Pedagogical and Digital Competencies Through Digital Tools: A Proposal for Semi-schooled Language Teaching Programs in Oaxaca, Mexico José de Jesús Bautista Hernández , Eduardo Bustos Farías , Norma Patricia Maldonado Reynoso (pages: 30-35) Railway Track Degradation Modelling Using Finite Element Analysis: A Case Study in South Africa Ntombela Lunga , Masengo Ilunga (pages: 36-50) Continuum of Academic Collaboration: Issues of Inconsistent Terminology in Multilingual Context Cristo Leon , James Lipuma , Marcos O. Cabobianco , Maria B. Daizo (pages: 51-62) Peat Resource Management and Climate Change Mitigation Issues – Case of Latvia Anita Titova , Natalja Lace (pages: 63-70) Using Geospatial Computation Intelligence for Mapping Temporal Evolution of Urban Built-up in Selected Areas of the Ekurhuleni Municipality, South Africa Jo-Anne Correia , Masengo Ilunga (pages: 71-80) Cybernetics and Informatics of Generative AI for Transdisciplinary Communication in Education Rusudan Makhachashvili , Ivan Semenist (pages: 81-88) Navigating Psychological Riptides: How Seafarers Cope and Seek Help for Mental Health Needs Coleen Abadicio , Stella Louise Arenas , Rosette Renee Hahn , Angel Berry Maleriado , Ramon Miguel Mariano , Rodolfo Antonio Ma. Zabella , Genejane Adarlo (pages: 89-98)
ABSTRACT
Water Quantity Prediction Using Least Squares Support Vector Machines (LS-SVM) Method
The impact of reliable estimation of stream flows at highly urbanized areas and the associated receiving waters is very important for water resources analysis and design. We used the least squares support vector machine (LS-SVM) based algorithm to forecast the future streamflow discharge. A Gaussian Radial Basis Function (RBF) kernel framework was built on the data set to optimize the tuning parameters and to obtain the moderated output. The training process of LS-SVM was designed to select both kernel parameters and regularization constants. The USGS real-time water data were used as time series input. 50% of the data were used for training, and 50% were used for testing. The experimental results showed that the LS-SVM algorithm is a reliable and efficient method for streamflow prediction, which has an important impact to the water resource management field.
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