Precipitation pattern modeling using cross-station perception: regional investigation
- 92 Downloads
Establishing robust models for predicting precipitation processes can yield a significant aspect for many applications in water resource engineering and environmental prospective. In particular, understanding precipitation phenomena is crucial for managing the effects of flooding in watersheds. In this research, a regional precipitation pattern modeling was undertaken using three intelligent predictive models incorporating artificial neural network (ANN), support vector machine (SVM) and random forest (RF) methods. The modeling was carried out using monthly time scale precipitation information in a semi-arid environment located in Iraq. Twenty weather stations covering the entire region were used to construct the predictive models. At the initial stage, the region was divided into three climatic districts based on documented research. Initially, modeling was carried out for each district using historical information from regionally distributed meteorological stations for calibration. Subsequently, cross-station modeling was undertaken for each district using precipitation data from other districts. The study demonstrated that cross-station modeling was an effective means of predicting the spatial distribution of precipitation in watersheds with limited meteorological data.
KeywordsPrecipitation forecasting Intelligence models Cross-station modeling Semi-arid environment
Authors would like to acknowledge their gratitude and appreciate for the Meteorological Organization of Seismology (IMOS), Ministry of Agriculture and Water Resources of Kurdistan, for providing the precipitation data set of the studied case study and their admirable cooperation. Also, we thank all reviewers and editors for their insightful comments that improved the clarity of the final paper.
Compliance with ethical standards
Conflict of interest
The authors have no conflict of interest to declare for publishing this article.
- Haykin S (1999) Neural networks: a comprehensive foundation, Prentic-Hall, Upper Saddle River, New Jersey, p 842Google Scholar
- Kumar M, Raghuwanshi N, Singh R et al (2002) Estimating evapotranspiration using artificial neural network. J Irrig Drain Eng 128:224–233. https://doi.org/10.1061/(ASCE)0733-9437(2002)128:4(224) CrossRefGoogle Scholar
- Osman Y, Abdellatif M, Al-Ansari N et al (2017) Climate change and future precipitation in arid environment of middle east: case study of Iraq. J Environ Hydrol 25:1–18Google Scholar
- Ruiz-Gazeb A, Villa N (2007) Storms prediction: Logistic regression vs random forest for unbalanced data. Case Stud Business Ind Gov Stat 1:91–101Google Scholar
- Segal MR (2004) Machine learning benchmarks and random forest regression. Biostatistics 1–14Google Scholar
- Sulaiman J, Darwis H, Hirose H (2013) Forecasting monthly maximum 5-day precipitation using artificial neural networks with initial lags. In: Proceedings—6th International Symposium on Computational Intelligence and Design, ISCID 2013. pp 3–7Google Scholar