Bridging physics and machine learning: AI-enhanced WRF ensemble approach to extreme rainfall prediction

Plants meet machines: Prospects in machine learning for plant biology

Physical-empirical models for prediction of seasonal rainfall extremes of Peninsular Malaysia

Objectives: To evaluate the efficiency of task prediction and resource allocation for load balancing (LB) in the cloud environment using the combined approach like random Forest(RF) for task prediction and Particle Swarm optimization for optimization and Convolutional Neural Networks (PSO-CNN) for resource prediction and allocation. Methods: The ensemble approach in the present study uses Random Forest (RF), a machine learning (ML) model for task prediction and Particle Swarm Optimization (PSO+CNN), a bio-inspired algorithm and Deep Learning (DL) model for optimization and resource allocation. The study employs PSO techniques to optimize CNN in order to address the investigation of algorithmic optimization in DL. The results show that the suggested model outperforms the other models like CNN-LSTM(Long Short-term memory), CNN-GRU(Gated Recurrent Unit), and PSO –SVM(Support Vector Machine) to increase the performance and efficacy of the cloud systems. The experiment is implemented using Python and assessed using Google Cluster dataset that is accessible to the public. Findings: The use of ML and DL techniques are found to be more efficient in cloud infrastructure than the conventional methods. The study examines the performance of the RF, PSO and CNN and the hybrid RF-PSO-CNN models. The accuracy, precision, and F1. Score metrics were used to assess the performance of the classification models. The recommended model RF-PSO-CNN outperforms them with an accuracy of 90% than the contrasted methods like CNN-LSTM, CNN- GRU and PSO-SVM. As a result, both the classification assessment metrics and resource consumption show that the proposed model performs effectively. Novelty: The novel ensemble approach suggests the combined RF-PSO-CNN for LB in cloud Computing. The task predicted by RF is assigned to the resource chosen by PSO and CNN, thereby improving the efficiency of task prediction and resource allocation. Most of the research uses any two ML or DL methods for either predicting the tasks to be scheduled or which resource to allocate. The study uses a combination of the ML (RF) method, bio-inspired algorithm (PSO) and a DL (CNN) model for both task and resource prediction concurrently and it examines the effectiveness of LB in the cloud context. Keywords: Load Balancing (LB), Task scheduling, Resource allocation, Random Forest (RF), Convolutional Neural Networks (CNN), Particle Swarm Optimization (PSO)

Exponential growth of electronic data requires advanced multi-label classification approaches for the development of natural language processing (NLP) applications such as recommendation systems, drug reaction detection, hate speech detection, and opinion recognition/mining. To date, several machine and deep learning–based multi-label classification methodologies have been proposed for English, French, German, Chinese, Arabic, and other developed languages. Urdu is the 11th largest language in the world and has no computer-aided multi-label textual news classification approach. Unlike other languages, Urdu is lacking multi-label text classification datasets that can be used to benchmark the performance of existing machine and deep learning methodologies. With an aim to accelerate and expedite research for the development of Urdu multi-label text classification–based applications, this article provides multiple contributions as follows: First, it provides a manually annotated multi-label textual news classification dataset for the Urdu language. Second, it benchmarks the performance of traditional machine learning approaches particularly by adapting three data transformation approaches along with three top-performing machine learning classifiers and four algorithm adaptation-based approaches. Third, it benchmarks performance of 16 existing deep learning approaches and the four most widely used language models. Finally, it provides an ensemble approach that reaps the benefits of three different deep learning architectures to precisely predict different classes associated with a particular Urdu textual document. Experimental results reveal that proposed ensemble approach performance values (87% accuracy, 92% F1-score, and 8% hamming loss) are significantly higher than adapted machine and deep learning–based approaches.

Rainfall prediction plays a crucial role in agriculture, disaster management, and water resource planning. Traditional forecasting methods rely on statistical techniques, which may not effectively capture the complex patterns of weather data. This project utilizes machine learning (ML) techniques to enhance the accuracy of rainfall prediction. Various ML algorithms such as Decision Trees, Random Forest, Support Vector Machines, and Neural Networks are employed to analyse historical weather data, including temperature, humidity, wind speed, and atmospheric pressure. The proposed system preprocesses and trains the model on a large dataset, optimizing it for improved prediction accuracy. The trained model is then deployed as a web application using frameworks like Flask or Django, providing an interactive interface for users to input weather parameters and obtain rainfall predictions. The web deployment ensures accessibility and real-time predictions, making it useful for farmers, meteorologists, and policymakers. This project demonstrates the potential of machine learning in meteorological forecasting, offering a scalable and efficient approach to rainfall prediction. KEYWORDS Rainfall prediction plays a vital role in agriculture, water resource management, disaster preparedness, and overall climate monitoring. With the advancement of technology, machine learning techniques have become increasingly popular for accurately forecasting rainfall patterns based on historical and real-time meteorological data. This project focuses on building a rainfall prediction system using machine learning algorithms, such as Linear Regression, Random Forest, Support Vector Machine (SVM), and Neural Networks, to analyze weather parameters like temperature, humidity, wind speed, and pressure. The data is collected from reliable sources and undergoes data preprocessing, feature selection, and normalization to enhance the model's performance. Various supervised learning models are trained and evaluated using performance metrics like accuracy, precision, recall, and RMSE to identify the best-performing algorithm for rainfall prediction. Once the model is optimized, it is integrated into a web-based application using frameworks like Flask or Django, enabling real-time prediction and user interaction through a clean, responsive web interface. The application allows users to input weather parameters and get immediate rainfall predictions. Deployment is done using cloud platforms such as Heroku, AWS, or Google Cloud, ensuring accessibility and scalability. An interactive dashboard may be included to visualize rainfall trends, historical data, and predictive insights, helping users make informed decisions. The web app bridges the gap between complex machine learning models and end-users by offering an intuitive and functional interface. Overall, this project demonstrates the integration of predictive analytics, climate data analysis, and web development, showcasing how modern technology can contribute to effective environmental monitoring and disaster management through smart forecasting systems.

Hydroclimatic extremes, such as droughts, floods, and extreme rainfall have been increasing worldwide leading to severe impacts on society and ecosystems. For that reason, hydrological modelling research has advanced to improve flood and rainfall prediction and control.  This estimation has been traditionally carried out using physical and process-based hydroclimatic models, however, they have limitations due to their physical-based nature. They often require a large amount of different hydro-geomorphological monitoring datasets, as well as in-depth knowledge and expertise regarding hydrological parameters, which must be correctly selected, calibrated, and further interpreted to ensure the reliability of the model. In recent years, data-driven hydrological modelling, such as Machine Learning (ML), Artificial Intelligence (AI), and Deep Learning (DL) methods have demonstrated a great deal of promise for enhancing the forecasting of hydroclimatic extremes. In data-driven modelling, the models use a generalized relationship between input and output disregarding the physical mechanism behind the process, built based on historical data. ML methods have some advantages over physical-based models, such as not requiring an understanding of internal specific mechanisms, which can be highly complex to reproduce, as well as having a higher calculation efficiency which may provide a quicker response to extreme events of high-intensity and short duration such as urban flash floods. Although there have been significant advances from the scientific community toward understanding and testing different ML and AI models for various hydrological applications, there are still limitations in their applications. A huge challenge that remains in ML modelling for future extreme floods, is its ‘black-box’ nature where the interactions among various components are unknown, which hinders its further use in supporting important decision-making. Along with that, other challenges in the current hydroclimatic modelling approaches presented by the hydrological community are data availability and assimilation, uncertainty analysis, and model generalisation. Some studies have addressed these issues, showing satisfactory results, especially for hybrid models between ML and traditional process-based approaches and ensembles of multiple methods. However, in light of so many new methodologies and algorithms, we must address their benefits and drawbacks, through an interdisciplinary effort. Understanding the best way to select appropriate methodologies for different settings of data availability, climate variability, and uncertainty, generating rapid and interpretable responses to urgent hydrologic hazards.

Rainfall plays a prominent role in managing of water resources. The accurate prediction of rainfall is the greatest challenge in the field of hydrologic studies. The prediction of rainfall is necessary to overcome natural disasters like flood and drought. The inaccurate prediction of rainfall causes either dry or overflow in water storage structures. In this study different types of Machine Learning (ML) and deep learning techniques are adopted to predict rainfall pattern of Aiyar river basin, in Tiruchirappalli district. The comparative study of these ML models is done to identify the best ML model for the study area. The comparison was done for different scenarios and time intervals. The rainfall data from years 1987 to 2023 is used for predicting the daily rainfall in the basin. The rainfall data from years 1987 to 2007 is used for testing and the remaining years data is used for training the data set. The Theisen polygon method is used to average and weighted the rainfall data in the basin. The ML models and deep learning techniques used in this study are Linear model, Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) models. The rainfall was predicted for different time scenario by using different ML algorithms like Autocorrelation method. The accuracy of the predicted model results was tested with RMSE, MASE and R square values. The result shows coefficient between 0.5 to 0.9 within the limit from the daily rainfall values. From the overall model comparison, it is observed that the SVM model accuracy is high compared to the other models involved in this study. It is concluded that two different methods ML and deep learning methods have been applied with same data in which SVM ML techniques gives better results in this study area. In future the predicted rainfall data of this study can be used for accurate flood forecasting and modelling of Aiyar basin.

In this work we describe a machine learning pipeline for facies classification based on wireline logging measurements. The algorithm has been designed to work even with a relatively small training set and amount of features. The method is based on a gradient boosting classifier which demonstrated to be effective in such a circumstance. A key aspect of the algorithm is feature augmentation, which resulted in a significant boost in accuracy. The algorithm has been tested also through participation to the SEG machine learning contest. Presentation Date: Wednesday, September 27, 2017 Start Time: 3:05 PM Location: 350D Presentation Type: ORAL

Extreme weather events have been one of the biggest challenges in the mining sector. One alternative to mitigate these risks is to improve the prediction of extreme weather events occur. One of the common extreme weather events faced by the mining sector is extreme rainfall. Continuous extreme rainfall can give rise to flooding events, which might disrupt the supply chain and operation of the mining sector. Previously, extreme rainfall prediction is conducted by employing the traditional statistical methods such as linear regression or autoregressive integrated moving average (ARIMA). Those methods result in good accuracy; however, they do not cover some of the assumptions of the data. Along with the development of information technology, advanced method, namely machine learning, is conducted. Thus, this study employed machine learning to predict the rainfall duration in open-pit mining. The predictive models constructed are a feed-forward neural network and an ARIMA model. This study also compared the performance of the neural network model and the ARIMA model by measuring its root mean square (RMSE). Based on the result, the neural network model outperforms the ARIMA model.

ObjectiveAsthma, a chronic respiratory condition characterized by airway inflammation and constriction, affects millions of individuals worldwide, resulting in high healthcare expenses and a lower quality of life. Early prediction and control of asthma risk are critical for avoiding exacerbations and improving outcomes.MethodsIn this study, we describe a comprehensive asthma prediction model that uses machine learning and deep learning techniques to estimate asthma risk based on a variety of health and environmental parameters. Recursive feature elimination and Extra Trees Classifier were used to choose features, and the synthetic minority over-sampling approach was used to balance the dataset to overcome class imbalance. Hyperparameter tuning was used to optimize performance for 12 machine learning models such as extreme gradient boosting, Random Forest, and support vector machine as well as deep learning models, including multilayer perceptrons, convolutional neural networks, recurrent neural network, and artificial neural network.ResultsAfter hyperparameter adjustment, ensemble approaches that used both hard and soft voting were evaluated. When hyperparameter adjustment was used, the soft voting ensemble that combined XGBoost and CatBoost achieved the highest accuracy (93.61%). Shapley additive explanations and local interpretable model-agnostic explanations were employed to make predictions interpretable, providing information on feature contributions and boosting clinician confidence. A Flask server and web interface were also deployed, enabling real-time user interaction where patients and medical professionals could enter data and obtain asthma risk estimations immediately.ConclusionsThis study presents an accurate and explainable asthma risk prediction framework using ensemble machine and deep learning models, achieving 93.61% accuracy with real-time clinical applicability.

Rainfall Prediction is a tough undertaking due to erratic patterns of rainfall and climate fluctuations all throughout the world. Rainfall forecasts helps to prevent floods and even assists in agriculture for producing crops. Rainfall prediction is aided by machine learning, which uses previously recorded weather data to identify unknown trends. This paper predicts rainfall using a machine learning classification technique. Of the total data, 80% is the training data and 20% is the tested data. Analysis is done on the number of actual and expected forecasts. The accuracy of the employed classification model is found to be 85%. The accuracy of numerous different machine learning techniques is also compared.

Tropical Cyclones (TCs) are counted among the most destructive phenomena that can be found in nature. Every year, globally an average of 90 TCs occur over tropical waters, and global warming is making them stronger and more destructive. The accurate localization and tracking of such phenomena have become a relevant and interesting area of research in weather and climate science. Traditionally, TCs have been identified in large climate data sets through the use of deterministic tracking schemes that rely on subjective thresholds. This study presents a Machine Learning (ML) ensemble approach for locating TCs center coordinates. The ensemble combines TCs center estimates of different ML models that agree about the presence of a TC in input data. ERA5 reanalysis data was used for model training and testing jointly with the International Best Track Archive for Climate Stewardship (IBTrACS) records. Compared to single models estimates, the ML ensemble approach was able to improve TCs localization in terms of Euclidean Distance with respect to the observed TCs locations from IBTrACS. Moreover, a hybrid tracking scheme was defined: starting from the individual TC center locations detected by the ML ensemble approach, a deterministic tracking algorithm was used for reconstructing TC trajectories. The hybrid tracking scheme was then compared with four deterministic trackers reported in literature, achieving a Probability of Detection and a False Alarm Rate of 71.49% and 23%, respectively, over 40 years of reanalysis data.

Sentiment classification plays a crucial role in analyzing customer feedback to identify market trends, enhance product recommendations, and improve customer satisfaction. This study focuses on sentiment analysis of Amazon reviews using two major datasets—Fine Food Reviews and Unlocked Mobile Reviews—which exhibit label imbalance. To address this challenge, both oversampling and undersampling techniques were applied to balance the datasets. Various machine learning (ML) algorithms, including Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), Naïve Bayes (NB), and Gradient Boosting Machine (GBM), as well as deep learning (DL) models such as Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and transformer-based models like RoBERTa, were implemented. After data cleaning and preprocessing, models were trained, and performance was evaluated. The results indicate that oversampling significantly enhances classification accuracy, particularly for the Fine Food dataset. Among ML models, Random Forest achieved the highest accuracy due to its ensemble approach and robustness in handling high-dimensional data. DL models, particularly RoBERTa, also demonstrated superior performance owing to their capacity to capture contextual dependencies. The findings emphasize the importance of data balancing for optimal sentiment analysis and contribute valuable insights toward advancing automated opinion classification in e-commerce applications.

Efficient-VGG16: A Novel Ensemble Method for the Classification of COVID-19 X-ray Images in Contrast to Machine and Transfer Learning

Farmers in India suffer gruesome fate at the mercy of rain gods since primary source of agriculture in India is rainfall. Agriculture is a major source of living but contributes only about 18% of total gross domestic product, its reason being lack of adequate crop planning by farmers. Although India has surplus fertile land, inefficient agricultural practices due to deficiency of rainfall and crop prediction techniques, in turn, leads to uncountable farmer suicides. Currently, the invasion of Machine Learning (ML) has abetted in finding promising solutions to address the problems of predicting rainfall, soil assessment, crop management, yield prediction, crop quality and disease detection and classification. Despite the technology, hitherto, there is no platform nor system in place to inform farmers of the rainfall predicted and advise what crops to grow. In this paper, rainfall prediction using diverse ML and statistical algorithms is encapsulated, accordingly best suitable crops to grow are recommended keeping soil as a parameter. The raw real-time rainfall data acquired was pertained to three regions of Karnataka North, South and Coastal. Data was cleaned and structured and its features extracted. Statistical tests—ADF, KPSS, ACF, PACF executed on the feature extracted data revealed its trend and seasonality insightful for modelling. Using effective ML and statistical algorithms such as ARIMA, ANN, random forest, TBATS, Holt-Winters, simple, double, triple exponential smoothening et al., rainfall for the next six years was predicted. All three regions were distinctly modelled. Time series forecasting using ARIMA proved to be the best performer. All models performances are validated using standard error measures to have authenticity of accuracy. The generalized accuracy of ARIMA model averaging on all three regions is 92.91%, ANN 88.26%, TBATS 61%, simple exponential smoothening had 71.1%, double exponential smoothening is 68.63%, triple exponential smoothening accuracy is 57.42%, and random forest gave 42%.