Aiza Makhosheva
Biographic Data
| ID | 9424977 |
|---|---|
| NAME | Aiza Makhosheva |
| GIVEN NAMES | Aiza |
| FAMILY NAME | Makhosheva |
| SIGNATURE | MAKHOSHEVA A |
| AFFILIATIONS | North-Caucasian Mining and Metallurgical Institute |
| VERIFIED | No |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2024 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Predicting the characteristics of sele flows using machine learning
Mudflows are among the most hazardous natural phenomena in mountainous regions, causing significant damage to infrastructure and settlements. Their prediction is traditionally challenging due to the fragmented, incomplete, and heterogeneous nature of source data, which limits the applicability of classical statistical methods. Purpose of the research. To develop and test a set of machine learning models for predicting mudflow characteristics and …
Construction of a predictive model of pedestrian crossing traffic in a mountainous region taking into account weather and time factors
Pedestrian traffic in mountainous regions is significantly affected by weather conditions and time factors. Optimization of urban infrastructure and ensuring pedestrian safety require forecasting traffic changes depending on climate and time of day. The purpose of this study is to develop a predictive model that takes into account key factors affecting pedestrian traffic intensity. The study collected data on pedestrian traffic in the mountainous…
No prominent works on this page.
Construction of a predictive model of pedestrian crossing traffic in a mountainous region taking into account weather and time factors
Pedestrian traffic in mountainous regions is significantly affected by weather conditions and time factors. Optimization of urban infrastructure and ensuring pedestrian safety require forecasting traffic changes depending on climate and time of day. The purpose of this study is to develop a predictive model that takes into account key factors affecting pedestrian traffic intensity. The study collected data on pedestrian traffic in the mountainous…
Predicting the characteristics of sele flows using machine learning
Mudflows are among the most hazardous natural phenomena in mountainous regions, causing significant damage to infrastructure and settlements. Their prediction is traditionally challenging due to the fragmented, incomplete, and heterogeneous nature of source data, which limits the applicability of classical statistical methods. Purpose of the research. To develop and test a set of machine learning models for predicting mudflow characteristics and …
Climate change and permafrost (1 work) · Cluster analysis (1 work) · Computer Science (1 work) · Data set (1 work) · Debris flow (1 work) · Discretization (1 work) · Engineering (1 work) · Environmental Science (1 work) · Geography (1 work) · Karst Systems and Hydrogeology (1 work)