Data for Cognitive Digital Twin Framework in "The Spine", Madinaty

Shimaa Elgingihy

This dataset contains the research data, code, and validation files associated with the paper titled: "A Cognitive Digital Twin Framework for Sustainable Urban Water Management and Carbon Sequestration: A Case Study of 'The Spine', Madinaty, Cairo, Egypt" Authors Shimaa M. Elgingihy, Asmaa A. Tohamy, Abelrahman E. Mohamed, Ramy M. Wahba, Hamdey O. Zain Al-Abdeen, Abdallah A. Elsayed Dataset Contents (The_Spine_Data.rar) The compressed repository contains the complete dataset, data cleaning files, machine learning prediction models, and output results used for evaluating the Cognitive Digital Twin framework for "The Spine" project: Input / Raw Data: Cleaned_Climate_Data.csv: Preprocessed historical weather and climate datasets used as environmental inputs for urban water management and microclimate modeling. Simulation & Machine Learning Outputs: rf_predictions.xlsx: Predicted output data and validation results generated from the Random Forest model. Code Scripts & Jupyter Notebooks: LMST.ipynb: Jupyter notebook containing data processing and spatial/environmental modeling analyses. LSTM_Weather_Prediction_The_Spine.ipynb: Python implementation of the Long Short-Term Memory (LSTM) deep learning network for weather and climate time-series forecasting. The_Spine_Randomforest.ipynb: Python code for training, evaluating, and running predictions using the Random Forest regressor/classifier algorithm. Usage & License This data is made publicly available to support open science and allow complete reproducibility of the analysis, simulation, and predictive models presented in the accompanying manuscript.