Precipitation Forecasting Assessment via Nonlinear Autoregressive Neural Network and Vector Autoregressive Models



Precipitation prediction by hidden markov models
Batmaz, İnci; Yazıcı, Ceyda; Yozgatlıgil, Ceylan (null; 2014-06-18)
Precipitation Modeling by Polyhedral RCMARS and Comparison with MARS and CMARS
Ozmen, Ayse; Batmaz, İnci; Weber, Gerhard-Wilhelm (2014-10-01)
Climate change is becoming an ever important issue due to the possibility that it may result in extreme weather events such as floods or droughts. Consequently, precipitation forecasting has similarly gained in significance as it is a useful tool in meeting the increasing need for the efficient management of water resources as well as in preventing disasters before they happen. In the literature, there are various statistical and computational methods used for this purpose, including linear and nonlinear re...
Drought Forecasting with Time Series and Machine Learning Approaches
Evkaya, Ömer Ozan; Yozgatlıgil, Ceylan; Kestel, Sevtap Ayşe (null; 2017-12-08)
As a main reason of undesired agricultural, economic and environmental damages, drought is one of the most important stochastic natural hazard having certain features. In order to manage the impacts of drought, more than 100 drought indices have been proposed for both monitoring and forecasting purposes [1], [3]. For different types of droughts, these indices have been used to understand the effects of dry periods including meteorological, agricultural and hydrological droughts in many distinct locations. I...
Rainfall runoff modelling with green-ampt infiltration process.
San, Ovul; Sevük, Süha; Department of Civil Engineering (1993)
Rainfall network design approaches and applications.
Balkan, Guven; Department of Civil Engineering (1979)
Citation Formats
S. Aslan, C. İyigün, C. Yozgatlıgil, and İ. Batmaz, “Precipitation Forecasting Assessment via Nonlinear Autoregressive Neural Network and Vector Autoregressive Models,” 2013, Accessed: 00, 2021. [Online]. Available: