Forecasting rainfall is crucial to the well-being of individuals and is significant everywhere in the world. It contributes to reducing the disastrous effects of floods on agriculture, human life, and socioeconomic systems. This study discusses the challenges of effectively forecasting rainfall and floods and the necessity of combining data with flood channel mathematical modelling to forecast floodwater levels and velocities. This research focuses on leveraging historical meteorological data to find trends using machine learning and deep learning approaches to estimate rainfall. The Bangladesh Meteorological Department provided the data for the study, which also uses eight machine learning algorithms. The performance of the machine learning models is examined using evaluation measures like the R² score, root mean squared error and validation loss. According to this research’s findings, polynomial regression, random forest regression, and long short-term memory (LSTM) had the highest performance levels. Random forest and polynomial regression have an R² value of 0.76, while LSTM has a loss value of 0.09, respectively.

This study brings about an innovative approach as rainfall forecast predominantly Boolean calculation is reviewed overall for 6 major cities of Pakistan for the time span of last quarter century. The study is aimed at improving the accuracy and the reliability of rainfall forecasting by making use of the capabilities of Artificial Intelligence (AI). This research investigates the efficacy of various machine learning models, including Naive Bayes, Logistic Regression, Support Vector Machines (SVM), K Nearest Neighbors (KNN), Gradient Boosting, and time series forecasting model ARIMA (Autoregressive Integrated Moving Average), for rainfall prediction. The model gets trained using 20 years of pakistan historical data performance where improving precision is the objective. With a highly scientific evaluation against real-world datasets, the new approach shows remarkable improvements in the accuracy of rainfall prediction compared to other conventional methods. Machine learning model KNN and time series forecasting model ARIM provide the good result in term of higher accuracy and lower RMSE. This results in the combination of environmental data science for innovation of the meteorological forecasting of this polarized area where the judgments are not so accurate.

All around the world, natural disasters have an impact on both human and animal life. In addition to causing major harm to property, animal life, etc. Natural disasters (including landslides, cloudbursts, heat waves, hurricanes, tsunamis, floods, tsunamis, earthquakes, and wildfires) affect thousands of lives each year throughout the world. Twitter, a social networking platform, users can share news, opinions, and personal stories. Due to the widespread availability of real-time data, numerous service agencies regularly analyze this data to spot crises, lower risk, and save lives. Humans, however, are unable to manually filter through the vast number of records and spot hazards in real-time to achieve this, it has been suggested in numerous studies to provide words in forms that computers can understand and on the word representations, use machine learning techniques to determine the meaning behind a post with accuracy. The community can monitor disasters by reporting hazards that are related to disaster occurrences on social media, which has been essential to emergency preparedness. This study examines how social media platform Twitter might be used in disaster-related research. It focuses on the most recent machine learning, deep learning, and disaster prediction techniques. Acquiring a thorough grasp of the numerous data kinds and their sources in relation to a variety of tasks and crisis management scenarios is another goal of the work. Additionally, The study also aims to offer a comprehensive analysis of the various data mining methods utilized for tackling various issues related to natural disasters as well as comprehensive directions on how to categorize tweets as "Related to Catastrophe" or "Not related with Catastrophe" using natural processing methods.

Rainfall prediction is critical in agriculture, water resource management, transportation, and disaster planning. As a critical component of the hydrological cycle, precisely forecasting daily rainfall helps to lessen the effects of floods, droughts, and other extreme weather events. However, due to the nonlinear and complicated interaction of meteorological factors, daily rainfall forecast remains a grim problem. The proposed work adopts a systematic deep learning-based framework, integrating Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) models and Transformers to predict the rainfall of the next day. Besides deep models, a few machine learning classifiers, such as the Logistic Regression (LR), Support Vector Machines (SVM), and the K-Nearest Neighbor (KNN) will be applied to benchmark the performance.One of the main insights in this study is the systematic comparison of a wide variety of activation functions, such as Sigmoid, ReLU, Tanh, Swish, Leaky ReLU, and ELU to measure their effects on model accuracy, convergence, and generalization performance. An experiment was employed on real-life rainfall of the USA dataset, which proved the efficiency of using the BiLSTM model and ReLU and Leaky ReLU activation functions to achieve up to 99% accuracy, whereas Transformer model was also efficient as it obtain up to 98%, but it was not stable and showed a tendency towards occasional instability during the training process. On the contrary, traditional machine learning models displayed an intermediate predictive accuracy, with average accuracies of approximately 87%. Significantly, the results were validated through multi-seed experiments with confidence intervals and baseline benchmarks, demonstrating that the near-perfect scores were not incidental but consistently stable across different initializations. This study provides a new theoretical standpoint in terms of activation functions in deep sequence models as it studies beneficiaries and shortcomings through a mathematical point of view. The results highlight that activation function selection can have an important impact on the accuracy of forecasts and can be significant in building more accurate, scalable, and general learning subroutines in weather predictions.

Purchasing a used vehicle might be difficult. As with many other consumer items, the cost of a used automobile has increased dramatically in recent years. The unpleasant experience of being a car owner has also been exacerbated by growing interest rates and petrol prices. One of the major and fascinating fields of investigation is used automobile price prediction. As the number of automobiles on the road has increased significantly, more and more people are unable to purchase the newly manufactured vehicles due to a variety of factors, including excessive costs, limited supply, insufficient funds, and so on. As a result, the used automobile industry is expanding globally, although it is still in its infancy and is primarily controlled by the unorganized sector in Pakistan. Along with the outcomes of the experiments in this study. In order to predict the resale price of used cars given a number of parameters, such as the car's model, year of production, mark, selling type, fuel type, and current pricing, a variety of regression algorithms based on supervised machine learning were utilized in this study. Two key areas of the automotive domain vehicle price prediction and automobile recognition—are addressed by the Car Price prediction and recognition initiatives, the blends supervised machine learning with computer vision methods. The goal of this work is to equip users with the knowledge and skills necessary for making intelligent choices when buying cars and to identify cars through picture capture. In this study the main source of information for the forecasts is the Kaggle website. Random forest demonstrated a high degree of accuracy and a low root mean square error in all other investigated models. Our suggested model's performance demonstrates the usefulness and efficiency of the suggested investigation.

Natural disasters have an impact on the lives of people and animals worldwide. Twitter, which is a social networking tool that enables individuals to share news, opinions, and even their experiences, has emerged as a valuable source of real-time information. Since real-time data is easily accessed, many service providers make an effort to analyze it on a daily basis for purposes of detecting emergencies and lowering risk factors. As for people, a person cannot sit and analyze thousands of records and, at the same time, recognize threats in real time. To address this challenge, machine learning (ML) and deep learning (DL) techniques have become essential tools for automating disaster detection and classification. The article examines the disaster prediction capabilities of Twitter text data and evaluates the power of diverse supervised machine learning models (logistic regression, naive Bayes, support vector machines (SVM), k-nearest neighbors (KNN) and XGBoost). Also, a deep learning model based on Long Short-Term Memory (LSTM) is tested to see how it performs on sequential data in tweets. The main goal of the work is investigating the impact of the usage of different data modes, including text, visual data (including pictures and satellite data), audio data (including sounds of emergency calls or acoustic patterns), and blog or news posts, on effective disaster prediction and classification. Although the nature of Twitter text data (real-time, short, and publicly available) has been prioritized in this research, the property of multimodal data sources to improve situational awareness is also recognized as their increasing role. Through the analysis of these varied streams of information with both a traditional machine learning approach and an advanced deep learning approach, the comprehensive study will attempt to create a more holistic and responsive disaster detection framework that could serve as an aid to decision-makers and emergency responders in their attempt to mitigate losses and improve the response strategy.