machine learning algorithms

Enhanced Poverty Assessment through Advanced Analytical Models within the Malaysia MADANI Framework

This paper presents a research framework for an innovative poverty assessment methodology aligned with the Malaysia MADANI Framework's objectives of eradicating poverty and promoting inclusive economic growth.  Traditional approaches to household categorization often neglect critical demographic variables and the unequal distribution of income, leading to an incomplete understanding of poverty.  To address these limitations, the framework integrates mixture ordinal regression models with machine learning algorithms, leveraging the strengths of statistical modeling and a

A METHOD FOR FORECASTING THE ENERGY GENERATION OF A SOLAR POWER PLANT

The successful deployment of solar energy systems necessitates accurate forecasting of electricity production by photovoltaic power stations (PPS) to ensure the stable operation of power supply networks. This requirement stems from the need to maintain a real-time balance between electricity generation and consumption, which is achieved through the implementation of complex hierarchical control systems governing available energy sources.

Detection of geodynamic anomalies in GNSS time series using machine learning methods

One of the applied geodetic tasks in geodynamics is the detection of anomalous deviations in GNSS time series, which may indicate deformations of the Earth's surface caused by various geophysical phenomena. It is important to note that geodynamic anomalies may be of a local nature, manifesting at a single GNSS station, or of a regional nature, occurring simultaneously across a group of GNSS time series. The objective of this article is to develop a method for detecting geodynamic anomalies in GNSS time series using machine learning algorithms.