Advancements in daily precipitation forecasting: A deep dive into daily precipitation forecasting hybrid methods in the Tropical Climate of Thailand

MethodsX, Год журнала: 2024, Номер 12, С. 102757 - 102757

Опубликована: Май 31, 2024

Язык: Английский

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Time series trend analysis and forecasting of climate variability using deep learning in Thailand

Results in Engineering, Год журнала: 2024, Номер unknown, С. 102997 - 102997

Опубликована: Сен. 1, 2024

Язык: Английский

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Hybrid artificial intelligence models based on adaptive neuro fuzzy inference system and metaheuristic optimization algorithms for prediction of daily rainfall

Physics and Chemistry of the Earth Parts A/B/C, Год журнала: 2024, Номер 134, С. 103563 - 103563

Опубликована: Янв. 28, 2024

Язык: Английский

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A Study of Precipitation Forecasting for the Pre-Summer Rainy Season in South China Based on a Back-Propagation Neural Network

Water, Год журнала: 2024, Номер 16(10), С. 1423 - 1423

Опубликована: Май 16, 2024

In South China, the large quantity of rainfall in pre-summer rainy season can easily lead to natural disasters, which emphasizes importance improving accuracy precipitation forecasting during this period for social and economic development region. paper, back-propagation neural network (BPNN) is used establish model forecasting. Three schemes are applied improve performance: (1) predictors selected based on individual meteorological stations within region rather than as a whole; (2) triangular irregular (TIN) proposed preprocess observed data input BPNN model, while simulated/forecast expected output; (3) genetic algorithm hyperparameter optimization BPNN. The first scheme reduces mean absolute percentage error (MAPE) root square (RMSE) simulation by roughly 5% more 15 mm; second MAPE RMSE 15% mm, respectively, third improves inapparently. Obviously, raises upper limit capability greatly preprocessing data. During training validation periods, improved be controlled at approximately 35%. For hindcasting test period, anomaly rate less 50% only one season, highest 64.5%. According correlation coefficient Ps score hindcast precipitation, performance slightly better FGOALS-f2 model. Although global climate change makes variable, trend almost identical that values over whole suggesting able capture general characteristics change.

Язык: Английский

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Enhancing Water Quality Management: Predictive Insights Through Machine Learning Algorithms

Environmental earth sciences, Год журнала: 2025, Номер unknown, С. 171 - 180

Опубликована: Янв. 1, 2025

Язык: Английский

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Risk of groundwater depletion in Jaipur district, India: a prediction of groundwater for 2028 using artificial neural network

Rendiconti lincei. Scienze fisiche e naturali, Год журнала: 2024, Номер unknown

Опубликована: Окт. 18, 2024

Язык: Английский

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Future Estimation of Hydrometeorological Variables Using Machine Learning Techniques: A Comparative Approach

Revista de Gestão Social e Ambiental, Год журнала: 2024, Номер 18(6), С. e08267 - e08267

Опубликована: Авг. 1, 2024

Objective: The objective of the research was to analyze and compare different machine learning models identify which technique presents best performance in predicting hydrometeorological variables. Theoretical Framework: This section main concepts that underpin work. Machine techniques such as support vector machines, decision trees, random forests, artificial neural networks, gradient boosting are presented, providing a solid foundation for understanding context investigation. Method: study uses comparative methodology by applying predict variables based on data collected Petrolina-PE. Various were employed compared. Data normalization performed through logarithms, treatment included filling or excluding inconsistent records. effectiveness is evaluated using metrics Nash-Sutcliffe efficiency coefficient, Willmott index, Pearson correlation coefficient. Results Discussion: obtained results showed good predictability, ranging from 50 70% efficiency. analysis allowed identifying patterns relationships between initial configurations algorithms, contributing better processes their predictability. Research Implications: By more accurate reliable forecasts, presented can assist managers making decisions about sustainable use water mitigation natural disasters floods. Originality/Value: contributes literature advancing estimation variables, improving existing techniques, resource management. Its impact extends mitigating risks associated with extreme hydrological events promoting resources, sustainability resilience aquatic ecosystems, essential face climate change environmental challenges.

Язык: Английский

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Improving Rainfall Prediction Accuracy in the USA Using Advanced Machine Learning Techniques

Journal of Environmental and Agricultural Studies, Год журнала: 2024, Номер 5(3), С. 23 - 34

Опубликована: Ноя. 9, 2024

The key aim of this research project is to design and evaluate advanced machine learning models for increasing accuracy in rainfall forecasting over the USA. We intended investigate nonlinear relationships typical atmospheric variables using state-of-the-art ML methods more accurate predictions. For on USA, we utilized an extensive dataset that comprises historical data collected from National Oceanic Atmospheric Administration (NOAA) other meteorological agencies. main use paper consists daily measurements across various geographical locations thus capturing wide-ranging necessary both training validation model. Besides measuring rainfall, included sources such as NOAA's Global Historical Climatology Network NASA's Modern-Era Retrospective Analysis Research Applications. These datasets further provided are known affect rain, including temperature, humidity, wind speed, pressure. performance metrics used work considered include accuracy, precision, recall, F1 score. above table shows Random Forest Classifier outperformed models, achieving perfect accuracy. That indicated it rightly classified all instances test set. Logistic Regression Support Vector Machine gave a quite good by giving average but had lower precision recall prediction. Accurate has direct consequences agriculture, greatly empowering farmers agricultural planners make effective decisions regarding planting, harvesting, crop management. forecasts also critical importance disaster management planning flood emergencies. Moreover, precise particularly sustainable water resources management, presents most important conserving these resources.

Язык: Английский

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Spatial Variation of Rainfall Between Nineveh and Basra Governorates due to Terrain Elevation Using Digital Elevation Model–Geographic Information System

Ecological Engineering & Environmental Technology, Год журнала: 2024, Номер 25(7), С. 1 - 10

Опубликована: Май 20, 2024

The study discussed the change in amounts of rainfall falling on two governorates Iraq, one north and other south, differing topographic elevation.The descriptive analytical approach, drawing inferential maps, adopting a digital elevation model were used to prove results.The aimed identify effect terrain factor increasing precipitation.Rainfall its decrease with sea level areas emerges importance using (DEM) as an analysis tool building three-dimensional models phenomena give comprehensive survey Earth's surface, this turn enhances accuracy extracted results well demonstrating capabilities inherent geographic information system (GIS) program dealing input analysis.And processing outputting quantitative data.The most important are that highest rainfall, rain reaching more than 360 mm, correspond terrain, which reaches height 1800 meters above level, represented Aqra Mountains Al-Sheikhan Sinjar Makhmour, within Nineveh Governorate.In second area, Basra Governorate, we find located desert range Western Plateau Hafr Al-Batin Valley approximately 290 m, it is land lime, gravel, sand.Thus, originality scientific fact becomes clear us, values these Its averages do not exceed 182 anomaly precipitation has become clear, low-lying exposed crossed by high contour line, due rocky limestone formation, tongue Iraqi region, adjacent both states Kuwait Saudi Arabia.The value comes from obtained showing eroding, difference (two governorates) Mosul, mountainous nature, eroding surface features low land, role modern technologies highlighting rates both.

Язык: Английский

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Optimizing the extreme gradient boosting algorithm through the use of metaheuristic algorithms in sales forecasting

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Июнь 20, 2024

Accurate forecasting of future demand is essential for decision-makers and institutions in order to utilize the sources effectively gain competitive advantages. Machine learning algorithms play a significant role this mission. In machine algorithms, tuning hyperparameters could dramatically enhance performance algorithm. This paper presents novel methodology optimizing Extreme Gradient Boosting (XGBoost), prominent algorithm, by leveraging Artificial Rabbits Optimization (ARO), recent metaheuristic construct robust generalizable model. Additionally, study conducts an experimental comparison ARO with two widely utilized Genetic Algorithm (GA) Bee Colony (ABC), eight different XGBoost. For experiment, 68,949 samples were collected. Furthermore, variables that have effect on sales investigated reliability Ten independent variables, comprising mixture internal external features including display size, financial indicators, weather conditions, identified. The findings showcased implemented ARO-XGBoost model surpassed other models, XGBoost model, optimized XGBoost, (ABC) across various evaluation metrics such as mean absolute percentage error. summary, use artificial rabbits optimization, yielded satisfactory results hyperparameter optimization our proposed comprehensive holds potential serving valuable studies.

Язык: Английский

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