A Generalized Regression Neural Network Model for Accuracy Improvement of Global Precipitation Products: A Climate Zone-Based Local Optimization

Saeid Mohammadpouri, Mostafa Sadeghnejad, Hamid Rezaei, Ronak Ghanbari, Safiyeh Tayebi, Neda Mohammadzadeh, Naeim Mijani, Ahmad Raeisi, Solmaz Fathololoumi () and Asim Biswas
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Saeid Mohammadpouri: Department of Remote Sensing and GIS, University of Tabriz, Tabriz 5166616471, Iran
Mostafa Sadeghnejad: Department of Geography and Geospatial Sciences, Kansas State University, 920 N17th Street, Manhattan, KS 66506-2904, USA
Hamid Rezaei: Department of Civil and Environmental Engineering, Florida International University, Miami, FL 33174, USA
Ronak Ghanbari: Graduate Research in Remote Sensing, University of Iowa, Iowa City, IA 52242, USA
Safiyeh Tayebi: Faculty of Geography, University of Tehran, Tehran 1417853933, Iran
Neda Mohammadzadeh: Department of Geography and Geospatial Sciences, Kansas State University, 920 N17th Street, Manhattan, KS 66506-2904, USA
Naeim Mijani: Department of Remote Sensing and GIS, University of Tehran, Tehran 1417853933, Iran
Ahmad Raeisi: Department of Electrical and Computer Engineering, University of Tehran, Tehran 1439957131, Iran
Solmaz Fathololoumi: School of Environmental Sciences, University of Guelph, Guelph, ON N1G 2W1, Canada
Asim Biswas: School of Environmental Sciences, University of Guelph, Guelph, ON N1G 2W1, Canada

Sustainability, 2023, vol. 15, issue 11, 1-20

Abstract: The ability to obtain accurate precipitation data from various geographic locations is crucial for many applications. Various global products have been released in recent decades for estimating precipitation spatially and temporally. Nevertheless, it is extremely important to provide reliable and accurate products for estimating precipitation in a variety of environments. This is due to the complexity of topographic, climatic, and other factors. This study proposes a multi-product information combination for improving precipitation data accuracy based on a generalized regression neural network model using global and local optimization strategies. Firstly, the accuracy of ten global precipitation products from four different categories (satellite-based, gauge-corrected satellites, gauge-based, and reanalysis) was assessed using monthly precipitation data collected from 1896 gauge stations in Iran during 2003–2021. Secondly, to enhance the accuracy of the modeled precipitation products, the importance score of effective and auxiliary variables—such as elevation, the Enhanced Vegetation Index (EVI), the Land Surface Temperature (LST), the Soil Water Index (SWI), and interpolated precipitation maps—was assessed. Finally, a generalized regression neural network (GRNN) model with global and local optimization strategies was used to combine precipitation information from several products and auxiliary characteristics to produce precipitation data with high accuracy. Global precipitation products scored higher than interpolated precipitation products and surface characteristics. Furthermore, the importance score of the interpolated precipitation products was considerably higher than that of the surface characteristics. SWI, elevation, EVI, and LST scored 53%, 20%, 15%, and 12%, respectively, in terms of importance. The lowest RMSE values were associated with IMERGFinal, TRMM3B43, PERSIANN-CDR, ERA5, and GSMaP-Gauge. For precipitation estimation, these products had Kling–Gupta efficiency (KGE) values of 0.89, 0.86, 0.77, 0.78, and 0.60, respectively. The proposed GRNN-based precipitation product with a global (local) strategy showed RMSE and KGE values of 9.6 (8.5 mm/mo) and 0.92 (0.94), respectively, indicating higher accuracy. Generally, the accuracy of global precipitation products varies depending on climatic conditions. It was found that the proposed GRNN-derived precipitation product is more efficient under different climatic conditions than global precipitation products. Moreover, the local optimization strategy based on climatic classes outperformed the global optimization strategy.

Keywords: global precipitation products; surface properties; climate-based local optimization; generalized regression neural network (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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