Asia’s coastlines are choking in waste. The region is now home to many of the world’s most polluted beaches. The populous Indian Cities are growing economically but in an unsustainable manner. With Mumbai counted among topmost polluted beaches in the world, it is the need of the hour to take necessary steps for effective waste management by systematic data analysis for deriving useful information from waste generation patterns. The major objective of the study is pattern recognition and beach waste quantum prediction based on 5 years data, with a frequency of daily waste collection. The size of the training data set is 1,661 days and the validation data set is 335 days. The influence of population trend, waste generation during festivals, special days, weekends, and seasonal variations form the basis for the analysis. Using machine learning algorithms, the study identifies and investigates data patterns for the case study of Dadar-Mahim beach. Data frequency and weights are correlated with occurrence of events, festivals, weekends, and seasons. Exploratory Data Analysis (EDA) is employed for data preprocessing and wrangling, followed by a Random Forest algorithm-based model for the prediction of waste generated at Dadar-Mahim beach. The major challenges in data prediction are limited data availability and variation in the dates of festivals and holidays as well as lack of waste segregation information. Despite the above-mentioned challenges, the observations indicate the model’s average accuracy for making predictions of around 60%. The Graphic User Interface (GUI) developed based on the model provides a user-friendly application for predicting the total daily generation of beach waste with reasonable precision. On the basis of the model’s outcome and applicability, a schematic approach for efficient beach waste management is proposed. The recommendations would serve as guidelines for Urban Local Bodies (ULBs) to automate the collection, transport, and disposal of beach waste.

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Abstract-Road accidents are one of the major causes of death worldwide as per Global safety report more than 1.5 million people are killed in road accidents every year throughout the world[5] The probability of occurrence of accident depends on numerous factors like roadway condition, geometrics of road, vehicle, pavement condition and weather condition, etc[10] each factor contributes its own share towards occurrence of accidents and there can be many more factors which are situation specific. To ascertain the effect of various parameters an accident occurrence, data of road accidents for a stretch of 101 Kms of an Indian national highway was collected for past 3 years. The analysis of the data using regression technique enables to predict the occurrence of accidents for a certain situation and similar application can be carried out on any stretch to get regression equation of similar type.

Background: Recent developments in optical satellite remote sensing have led to a new era in the detection of surface water with its changing dynamics. This study presents the creation of surface water inventory for a part of Pune district (an administrative area), in India using the Landsat 8 Operational Land Imager (OLI) and a multi spectral water indices method. Methods: A total of 13 Landsat 8 OLI cloud free images were analyzed for surface water detection. Modified Normalized Difference Water Index (MNDWI) spectral index method was employed to enhance the water pixels in the image. Water and non-water areas in the map were discriminated using the threshold slicing method with a trial and error approach. The accuracy analysis based on kappa coefficient and percentage of the correctly classified pixels was presented by comparing MNDWI maps with corresponding Joint Research Centre (JRC) Global Surface Water Explorer (GSWE) images. The changes in the surface area of eight freshwater reservoirs within the study area (Bhama Askhed, Bhatghar, Chaskaman, Khadakwasala, Mulashi, Panshet, Shivrata, and Varasgaon) for the year 2016 were analyzed and compared to GSWE time series water databases for accuracy assessment. The annual water occurrence map with percentage water occurrence on a yearly basis was also prepared. Results: The kappa coefficient agreement between MNDWI images and GSWE images is in the range of 0.56 to 0.96 with an average agreement of 0.82 indicating a strong level of agreement. Conclusions: MNDWI is easy to implement and is a sufficiently accurate method to separate water bodies from satellite images. The accuracy of the result depends on the clarity of image and selection of an optimum threshold method. The resulting accuracy and performance of the proposed algorithm will improve with implementation of automatic threshold selection methods and comparative studies for other spectral indices methods.

Background: Road accidents claim around 1.35 million lives annually, with countries like India facing a significant impact. In 2019, India reported 449,002 road accidents, causing 151,113 deaths and 451,361 injuries. Accident severity modeling helps understand contributing factors and develop preventive strategies. AI models, such as random forest, offer adaptability and higher predictive accuracy compared to traditional statistical models. This study aims to develop a predictive model for traffic accident severity on Indian highways using the random forest algorithm. Methods: A multi-step methodology was employed, involving data collection and preparation, feature selection, training a random forest model, tuning parameters, and evaluating the model using accuracy and F1 score. Data sources included MoRTH and NHAI. Results: The classification model had hyperparameters ’max depth’:  10, ’max features’: ’sqrt’, and ’n estimators’: 100. The model achieved an overall accuracy of 67% and a weighted average F1-score of 0.64 on the training set, with a macro average F1-score of 0.53. Using grid search, a random forest Classifier was fitted with optimal parameters, resulting in 41.47% accuracy on test data. Conclusions: The random forest classifier model predicted traffic accident severity with 67% accuracy on the training set and 41.47% on the test set, suggesting possible bias or imbalance in the dataset. No clear patterns were found between the day of the week and accident occurrence or severity. Performance can be improved by addressing dataset imbalance and refining model hyperparameters. The model often underestimated accident severity, highlighting the influence of external factors. Adopting a sophisticated data recording system in line with MoRTH and IRC guidelines and integrating machine learning techniques can enhance road safety modeling, decision-making, and accident prevention efforts.