Basir Ullah
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View article: Precision Flood Forecasting in Dynamic Hydrological Systems: Integrating <scp>LP</scp>‐<scp>III</scp> Distributions, Multilayer Neural Networks, and <scp>CMIP6</scp> Projections for the Swat Basin
Precision Flood Forecasting in Dynamic Hydrological Systems: Integrating <span>LP</span>‐<span>III</span> Distributions, Multilayer Neural Networks, and <span>CMIP6</span> Projections for the Swat Basin Open
Floods are among the most destructive natural disasters, presenting significant challenges due to their unpredictability and complex behavior. This study develops a robust flood prediction framework for the Chakdara monitoring station on t…
View article: Prediction of particulate matter PM2.5 level in the air of Islamabad, Pakistan by using machine learning and deep learning approaches
Prediction of particulate matter PM2.5 level in the air of Islamabad, Pakistan by using machine learning and deep learning approaches Open
Introduction: Air pollution is a significant global health challenge, contributing to the deaths of millions of people annually. Among these pollutants, Particulate Matter (PM2.5) is the most harmful to the respiratory system causing serio…
View article: Future precipitation patterns: investigating the IDF curve shifts under CMIP6 pathways
Future precipitation patterns: investigating the IDF curve shifts under CMIP6 pathways Open
Climate change has altered rainfall patterns, leading to urban flooding in Peshawar City. This study develops intensity–duration–frequency (IDF) curves to assess rainfall intensities for various return periods and durations. The methodolog…
View article: Comparative analysis of ensemble learning algorithms in water quality prediction
Comparative analysis of ensemble learning algorithms in water quality prediction Open
Water is essential for all life forms but is increasingly at risk of contamination. Monitoring water quality is crucial to protect ecosystems and public health. This study evaluates ensemble learning techniques – AdaBoost, Gradient Boost, …
View article: Intercomparison of deep learning models in predicting streamflow patterns: insight from CMIP6
Intercomparison of deep learning models in predicting streamflow patterns: insight from CMIP6 Open
This research was carried out to predict daily streamflow for the Swat River Basin, Pakistan through four deep learning (DL) models: Feed Forward Artificial Neural Networks (FFANN), Seasonal Artificial Neural Networks (SANN), Time Lag Arti…
View article: Flood inundation mapping under climate change scenarios: insights from CMIP6
Flood inundation mapping under climate change scenarios: insights from CMIP6 Open
The present research endeavors to simulate daily stream flow by employing hydrologic and hydraulic modeling techniques to comprehensively assess the impact of climate change on flood risk. This investigation was conducted within the Shekha…
View article: Developing a machine learning-based flood risk prediction model for the Indus Basin in Pakistan
Developing a machine learning-based flood risk prediction model for the Indus Basin in Pakistan Open
Pakistan is highly prone to devastating floods, as seen in the June 2010 and September 2022 disasters. The 2010 floods affected 20 million people, causing 1,985 fatalities. In 2022, approximately 33 million individuals were impacted, with …
View article: Intercomparison of Swat and Lstm Models in Simulating Streamflow
Intercomparison of Swat and Lstm Models in Simulating Streamflow Open
View article: Futuristic Streamflow Prediction Based on Cmip6 Scenarios Using Machine Learning Models
Futuristic Streamflow Prediction Based on Cmip6 Scenarios Using Machine Learning Models Open
Accurate streamflow estimation is vital for effective water resources management, including flood mitigation, drought warning, and reservoir operation. This research assesses the predictive performance of popular machine learning algorithm…