A Comparison of Machine Learning Models for Predicting Rainfall in Urban Metropolitan Cities

Vijendra Kumar (), Naresh Kedam, Kul Vaibhav Sharma, Khaled Mohamed Khedher () and Ayed Eid Alluqmani
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Vijendra Kumar: Department of Civil Engineering, Dr. Vishwanath Karad MIT World Peace University, Pune 411038, Maharashtra, India
Naresh Kedam: Department of Thermal Engineering and Thermal Engines, Samara National Research University, 443086 Samara, Russia
Kul Vaibhav Sharma: Department of Civil Engineering, Dr. Vishwanath Karad MIT World Peace University, Pune 411038, Maharashtra, India
Khaled Mohamed Khedher: Department of Civil Engineering, College of Engineering, King Khalid University, Abha 61421, Saudi Arabia
Ayed Eid Alluqmani: Department of Civil Engineering, Faculty of Engineering, Islamic University of Madinah, Madinah 42351, Saudi Arabia

Sustainability, 2023, vol. 15, issue 18, 1-27

Abstract: Current research studies offer an investigation of machine learning methods used for forecasting rainfall in urban metropolitan cities. Time series data, distinguished by their temporal complexities, are exploited using a unique data segmentation approach, providing discrete training, validation, and testing sets. Two unique models are created: Model-1, which is based on daily data, and Model-2, which is based on weekly data. A variety of performance criteria are used to rigorously analyze these models. CatBoost, XGBoost, Lasso, Ridge, Linear Regression, and LGBM are among the algorithms under consideration. This research study provides insights into their predictive abilities, revealing significant trends across the training, validation, and testing phases. The results show that ensemble-based algorithms, particularly CatBoost and XGBoost, outperform in both models. CatBoost emerged as the model of choice throughout all assessment stages, including training, validation, and testing. The MAE was 0.00077, the RMSE was 0.0010, the RMSPE was 0.49, and the R 2 was 0.99, confirming CatBoost’s unrivaled ability to identify deep temporal intricacies within daily rainfall patterns. Both models had an R 2 of 0.99, indicating their remarkable ability to predict weekly rainfall trends. Significant results for XGBoost included an MAE of 0.02 and an RMSE of 0.10, indicating their ability to handle longer time intervals. The predictive performance of Lasso, Ridge, and Linear Regression varies. Scatter plots demonstrate the robustness of CatBoost and XGBoost by demonstrating their capacity to sustain consistently low prediction errors across the dataset. This study emphasizes the potential to transform urban meteorology and planning, improve decision-making through precise rainfall forecasts, and contribute to disaster preparedness measures.

Keywords: rainfall forecasting; machine learning; Catboost; Lasso; Ridge; LGBM; XGBoost (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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