Prediction of monthly precipitation using various artificial models and comparison with mathematical models

Kassem, Youssef; Gökçekuş, Hüseyin; Mosbah, Almonsef Alhadi Salem

doi:10.1007/s11356-022-24912-7

Cited by 12 publications

(1 citation statement)

References 168 publications

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“…The statistical comparison of the RMSE, MAE, and R 2 is illustrated in Table 4, respectively. Higher than 0.5 [71], R 2 scores are considered acceptable. Additionally, the obtained values of R 2 (Table 4) at most of the gauge stations for CHI, NCP, NCC, and NCD are closer to zero, which indicates unsatisfactory values for predictive models.…”

Section: Gauge-observed Data and Comparison Of Multi-precipitation Pr...mentioning

confidence: 99%

Gridded Precipitation Datasets and Gauge Precipitation Products for Driving Hydrological Models in the Dead Sea Region, Jordan

2023

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The consistency of hydrological process modeling depends on reliable parameters and available long-term gauge data, which are frequently restricted within the Dead Sea/Jordan regions. This paper proposes a novel method of utilizing six satellite-based and reanalysis precipitation datasets, which are assessed, evaluated, and corrected, particularly for the cases of ungauged basins and poorly monitored regions, for the first time. Due to natural processes, catchments fluctuate dramatically annually and seasonally, making this a challenge. This variability, which is significantly impacted by topo-geomorphological and climatic variables within the basins themselves, leads to increased uncertainty in models and significant restrictions in terms of runoff forecasting. However, quality evaluations and bias corrections should be conducted before the application of satellite data. Moreover, the hydrological HEC-HMS model was utilized to predict the runoff under different loss methods. Furthermore, this loss method was used with an integrated model that might be efficiently employed when designing hydraulic structures requiring high reliability in predicting peak flows. The models’ performance was evaluated using R-squared (R2), the root mean square error (RMSE), the mean absolute error (MAE), and Nash–Sutcliffe efficiency (NSE). In addition, these statistical metrics were implemented to quantitatively evaluate the data quality based on the observed data collected between 2015 and 2020. The results show that AgERA5 exhibited better agreement with the gauge precipitation data than other reanalysis precipitation and satellite-based datasets. The results demonstrate that the data quality of these products could be affected by observational bias, the spatial scale, and the retrieval method. Moreover, the SC loss method demonstrated satisfactory values for the R2, RMSE, NSE, and bias compared to the IC and GA loss, indicating its effectiveness in predicting peak flows and designing hydraulic structures that require high reliability. Overall, the study suggests that AgERA5 can provide better precipitation estimates for hydrological modeling in the Dead Sea region in Jordan. Moreover, integrating the SC, IC, and GA loss methods in hydraulic structure design can enhance prediction accuracy and reliability.

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Section: Gauge-observed Data and Comparison Of Multi-precipitation Pr...mentioning

confidence: 99%