Accurate rainfall forecasting is crucial for various sectors across diverse geographical regions, including Uttarakhand, Uttar Pradesh, Haryana, Punjab, Himachal Pradesh, Madhya Pradesh, Rajasthan, and the Union Territory of Delhi. This study addresses the need for precise rainfall predictions by bridging the gap between localized meteorological data and broader regional influences. It explores how rainfall patterns in neighboring states affect Delhi's precipitation, aiming to improve forecasting accuracy. Historical rainfall data from neighboring states over four decades (1980–2021) were collected and analyzed. The study employs a dual-model approach: a daily model for immediate rainfall triggers and a weekly model for longer-term trends. Several machine learning algorithms, including CatBoost, XGBoost, ElasticNet, Lasso, LGBM, Random Forest, Multilayer Perceptron, Ridge, Stochastic Gradient Descent, and Linear Regression, were used in the modeling process. These models were rigorously assessed based on performance metrics from training, validation, and testing datasets. For daily rainfall forecasting, CatBoost, XGBoost, and Random Forest emerged as top performers, showcasing exceptional accuracy and pattern-capturing capabilities. In weekly rainfall forecasting, XGBoost consistently achieved near-perfect accuracy with an R² value of 0.99, with Random Forest and CatBoost also demonstrating strong performance. The study provides valuable insights into how climate patterns in neighboring states influence Delhi's weather, leading to more reliable and timely rainfall predictions.

Scouring around bridge abutments is a crucial and complex process that sometimes may lead to the failure of the bridge abutment. Therefore, in the present research, scouring around bridge abutments under clear water conditions was examined without and with countermeasures for providing an economical solution. A total of forty-five experiments were performed under clear water conditions to find the maximum scour depth around the bridge abutment. Experiments were performed in two different phases. In the first phase, scour depth was investigated without any countermeasures. In the second phase, scour depth was investigated using marble and brick waste as a countermeasure. The results showed that the maximum scour depth around the bridge abutment (at a distance of 10 cm on the upstream side and 15 cm on the downstream side of the abutment) for the Froude’s number of 0.22 was 0.137 m without any countermeasure. The scouring depth increased up to 40% with an increase in the Froude’s number from 0.13 to 0.22. The maximum reduction of scour depth was observed to be 40% and 55% when brick and marble waste were used as a countermeasure, respectively, compared to without a countermeasure case. It was concluded that marble and brick waste not only reduced scour depth to a significant level but also provided an economical solution.