Prediction and Monitoring Hydrological Drought via Machine Learning Algorithms
Assoc. Prof. Dr. Burak KIZILÖZ
Special Issue Information
Dear colleagues,
In recent years, climate change caused by global warming has attracted the attention of many researchers in the literature. Countries are organizing national and international programs to discuss the effects of climate change and seek solutions. Particularly, the identification and monitoring of hydrological drought have gained priority. Within this scope, innovative approaches are still being researched. Approaches such as Mann Kendall, Sen slope, Standardized Precipitation Index (SPI), The Standardized Precipitation Evapotranspiration Index (SPEI), and Innovative Trend Analysis (ITA) are used to analyze hydrological drought changes. However, in recent years, approaches like machine learning are preferred for classification problems. Using the Machine Learning method will provide more realistic results for drought predictions. Water and sewerage authorities can assess and prioritize future water planning, investments, water resource conditions, and water quality by utilizing the model's prediction results.
You are welcome to submit your recent research studies or relevant state-of-the-art reviews on prediction and monitoring hydrolic dought. We look forward to your contribution.