Hanyang University · 環境科学
Professor Muhammad Jehanzaib's research lab specializes in hydrological modeling, drought and flood forecasting, and climate change adaptation, with a strong focus on applying advanced machine learning techniques to improve water resources management. The lab emphasizes data-driven modeling using algorithms such as SVM, ANFIS, ELM, and neural networks for accurate prediction of runoff, evapotranspiration, and drought indices (e.g., SPI, SRI). Research spans regional studies in arid and semi-arid zones, particularly in North Africa and South Asia, addressing challenges related to climate variability and agricultural sustainability. The lab also contributes to systematic reviews on climate adaptation strategies, especially in vulnerable regions like Pakistan.
Figures are computed from collected data and may differ slightly.
Runoff plays an essential part in the hydrological cycle, as it regulates the quantity of water which flows into streams and returns surplus water into the oceans. Runoff modelling may assist in understanding, controlling, and monitoring the quality and amount of water resources. The aim of this article is to discuss various categories of rainfall–runoff models, recent developments, and challenges of rainfall–runoff models in flood prediction in the modern era. Rainfall–runoff models are classif
Forecasting meteorological and hydrological drought using standardized metrics of rainfall and runoff (SPI/SRI) is critical for the long-term planning and management of water resources at the global and regional levels. In this study, various machine learning (ML) techniques including four methods (i.e., ANN, ANFIS, SVM, and DT) were utilized to construct hydrological drought forecasting models in the Wadi Ouahrane basin in the northern part of Algeria. The performance of ML models was assessed
Drought is among the most hazardous climatic disasters that significantly influence various aspects of the environment and human life. Qualitative and reliable drought forecasting is important worldwide for effective planning and decision-making in disaster-prone regions. Data-driven models have been extensively used for drought forecasting, but due to the inadequacy of information on model performance, the selection of an appropriate forecasting model remains a challenge. This study concerns a
Evapotranspiration (ET) is a significant aspect of the hydrologic cycle, notably in irrigated agriculture. Direct approaches for estimating reference evapotranspiration (ET0) are either difficult or need a large number of inputs that are not always available from meteorological stations. Over a 6-year period (2006–2011), this study compares Feed Forward Neural Network (FFNN), Radial Basis Function Neural Network (RBFNN), and Gene Expression Programming (GEP) machine learning approaches for estim
Pakistan is among the countries that are highly vulnerable to climate change. The country has experienced severe floods and droughts during recent decades. The agricultural sector in Pakistan is adversely affected by climate change. This systematic review paper set out to analyze the existing literature on adaptation measures at the farm level toward climate change in Pakistan. Adopting a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method, a total of 62 articles w
Drought is one of the most severe climatic calamities, affecting many aspects of the environment and human existence. Effective planning and decision making in disaster-prone areas require accurate and reliable drought predictions globally. The selection of an effective forecasting model is still challenging due to the lack of information on model performance, even though data-driven models have been widely employed to anticipate droughts. Therefore, this study investigated the application of si
Natural events and anthropogenic activities are the two major driving forces that alter hydrological cycle processes, leading to hydrological extreme events such as floods and droughts. Streamflows are directly influenced by these forces, however, their cumulative effects over time are considered as indirect effects. This study investigated the distinct effects of precipitation, temperature, and human activities on hydrological droughts in five major river basins in South Korea. Change points in
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