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Improving Drought Forecasting in Iran’s Arid Regions Using Machine Learning and Soft Computing Approaches

Ronny

CMES Researcher Ronny Berndtsson has co-authored an article for PLOS One.

"Enhancement of standardized precipitation evapotranspiration index predictions by machine learning based on regression and soft computing for Iran’s arid and hyper–arid region"

Introduction

Drought is a climate risk that affects access to safe water, crop development, ecological stability, and food production. Therefore, developing drought prediction methods can lead to better management of surface and groundwater resources. Similarly, machine learning can be used to find improved relationships between nonlinear variables in complex systems. 

Read the full article here.

About Ronny Berndtsson

Ronny Berndtsson is co-coordinator of the Lund University Strategic Research Area “Middle East in the Contemporary World (MECW)”, Swedish coordinator of Horizon 2020 project FASTER (Farmers’ adaptation sustainability in Tunisia through excellence in research), and project leader of various other international research projects.

His major fields of research are Hydroclimatological processes by dynamical systems, Rainfall space-time variability and modeling, Soil water and solute trans­port in heterogeneous soils, Urban drainage and related pollutant transport, and Hydropolitics and Hydrosolidarity in the Middle East.