Journal of Hydrology, Год журнала: 2024, Номер unknown, С. 132556 - 132556
Опубликована: Дек. 1, 2024
Язык: Английский
Natural Hazards, Год журнала: 2024, Номер unknown
Опубликована: Июль 19, 2024
Язык: Английский
Процитировано
3
Natural Hazards Research, Год журнала: 2025, Номер unknown
Опубликована: Фев. 1, 2025
Язык: Английский
Процитировано
0
Journal of Pipeline Systems Engineering and Practice, Год журнала: 2025, Номер 16(2)
Опубликована: Фев. 18, 2025
Язык: Английский
Процитировано
0
Lecture notes in electrical engineering, Год журнала: 2025, Номер unknown, С. 574 - 585
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
0
Опубликована: Янв. 1, 2025
Процитировано
0
Journal of Flood Risk Management, Год журнала: 2025, Номер 18(2)
Опубликована: Апрель 13, 2025
ABSTRACT This study aimed to develop a proof‐of‐concept prototype of machine learning system forecast and mitigate the effect floods in Kasese District. The researchers used participatory design science approach. conducted document reviews brainstorming obtain past climate data from representatives affected communities, Makerere University Department Meteorology, Uganda National Meteorological Authority. Qualitative were transcribed recordings sessions notes literature. then summarized tables analyzed using Visual Network Analysis (VNA) with Word Clouds Gephi Open Source Software. We employed combination C++ programming, sensors wired Arduino 2 3 Integrated Development Environment System build prototype. Two algorithms, including linear regression K‐nearest neighbours (KNN) learn collected hydrological make necessary predictions. Using sensors, we able read water levels, temperature, humidity. successfully demonstrated ability send early‐warning alerts users, contributing both theoretical advancements disaster risk reduction practical tools for mitigating flood‐related losses Uganda. recommend further validate use this evaluate its efficacy predictive accuracy averting areas.
Язык: Английский
Процитировано
0
Meteorology, Год журнала: 2025, Номер 4(2), С. 10 - 10
Опубликована: Апрель 7, 2025
Accurate weather forecasting is essential for various industries, particularly in sectors like energy, agriculture, and disaster management. In Slovenia, predictions are crucial estimating electrical current transmission efficiency through power lines ensuring the reliable supply of electricity to consumers. This study focuses on quantifying measurement uncertainty meteorological forecasts generated by CALMET model, specifically addressing its impact energy reliability. The research highlights those local factors, such as topography, that contribute significantly uncertainty, which affects accuracy forecasts. examines parameters temperature, wind speed, solar radiation, identifying how environmental variations lead fluctuations forecast Understanding these uncertainties critical improving precision forecasts, especially transmission, where even small errors can have substantial consequences. primary goal this enhance reliability uncertainty. By interpretation data, refining methods, integrating advanced models, proposes ways reduce These improvements could support better decision-making other rely accurate predictions. Ultimately, findings suggest key more dependable industries.
Язык: Английский
Процитировано
0
Human-Centric Intelligent Systems, Год журнала: 2025, Номер unknown
Опубликована: Апрель 25, 2025
Abstract Natural disasters can be fatal and necessitate critical thinking in decision-making processes; thus, simulations of multiple scenarios must evaluated to prepare for such imminent events. An Open World Machine Learning (OWML) framework has been proposed as a focal tool improve natural disaster management. To better understand formulate comprehensive foundation, systematic review different cases was conducted. In this study, four types disasters-earthquakes, floods, wildfires, hurricanes-were analyzed using machine learning techniques within the OWML framework. The essence approach lies its ability accumulate knowledge from various sources adapt new, unknown event types. Moreover, by explicitly incorporating both qualitative quantitative characteristics, enhances predictive accuracy adaptability. results demonstrate that models effectively process large geospatial datasets respond looming threats with greater precision. Albeit some limitations, data quality model complexity, findings suggest serve foundational governments reduce expenditure evacuation strategies. evolutionary nature allows continuous adaptation, which is crucial escalate frequency intensity due climate change. essence, study provides significant contribution management demonstrating potential frameworks enhancing processes.
Язык: Английский
Процитировано
0
Advances in Science, Technology & Innovation/Advances in science, technology & innovation, Год журнала: 2025, Номер unknown, С. 1 - 17
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
0
Опубликована: Май 16, 2025
Cloudbursts present significant risks to urban infrastructure and public safety due their abrupt localized characteristics, frequently leading flash floods landslides. This study introduces the Advanced Cloudburst Prediction System, a hybrid AI-driven framework aimed at providing real-time assessments of cloudburst specific cities. The system combines Random Forest classifier with an LSTM neural network, utilizing both historical simulations current weather data sourced from OpenWeatherMap API. Its outputs feature dynamic risk probabilities, visual analytics, regional maps, emergency notifications through Gradio web interface. By delivering timely warnings practical insights, this enables authorities citizens improve disaster preparedness response strategies.
Язык: Английский
Процитировано
0