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--- en:pub:bibtex:kuells [2024/04/14 08:07] – ckuells
+++ en:pub:bibtex:kuells [2024/04/14 08:15] – ckuells
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+@article{Eliades2022,
+title = {Quantifying Evapotranspiration and Drainage Losses in a Semi-Arid Nectarine (Prunus persica var. nucipersica) Field with a Dynamic Crop Coefficient (Kc) Derived from Leaf Area Index Measurements},
+ISSN = {2073-4441},
+DOI = {10.3390/w14050734},
+volume = {14},
+number = {5},
+year = {2022},
+journal = {Water},
+author = {Eliades, M. and Bruggeman, A. and Djuma, H. and Christofi, C. and Kuells, C.},
+abstract = {Quantifying evapotranspiration and drainage losses is essential for improving irrigation efficiency. The FAO-56 is the most popular method for computing crop evapotranspiration. There is, however, a need for locally derived crop coefficients (Kc) with a high temporal resolution to reduce errors in the water balance. The aim of this paper is to introduce a dynamic Kc approach, based on Leaf Area Index (LAI) observations, for improving water balance computations. Soil moisture and meteorological data were collected in a terraced nectarine (Prunus persica var. nucipersica) orchard in Cyprus, from 22 March 2019 to 18 November 2021. The Kc was derived as a function of the canopy cover fraction (c), from biweekly in situ LAI measurements. The use of a dynamic Kc resulted in Kc estimates with a bias of 17 mm and a mean absolute error of 0.8 mm. Evapotranspiration (ET) ranged from 41% of the rainfall (P) and irrigation (I) in the wet year (2019) to 57% of P + I in the dry year (2021). Drainage losses from irrigation (DR_I) were 44% of the total irrigation. The irrigation efficiency in the nectarine field could be improved by reducing irrigation amounts and increasing the irrigation frequency. Future studies should focus on improving the dynamic Kc approach by linking LAI field observations with remote sensing observations and by adding ground cover observations.}
+}
+@article{KISI2024102017,
+title = {Enhancing river flow predictions: Comparative analysis of machine learning approaches in modeling stage-discharge relationship},
+journal = {Results in Engineering},
+volume = {22},
+pages = {102017},
+year = {2024},
+issn = {2590-1230},
+doi = {https://doi.org/10.1016/j.rineng.2024.102017},
+url = {https://www.sciencedirect.com/science/article/pii/S2590123024002706},
+author = {Ozgur Kisi and Hazi {Mohammad Azamathulla} and Fatih Cevat and Christoph Külls and Mehdi Kuhdaragh and Mehdi Fuladipanah},
+keywords = {Stage, Discharge, Artificial neural networks, Neuro-fuzzy, Rating curve, Estimation},
+abstract = {Streamflow, a pivotal variable in water resources management, holds profound significance in shaping the decision-making processes of hydrologic projects. This paper tries to delve into the exploration of the stage-discharge relationship using three machine learning methods (MLMs) namely multi-layer neural networks (MLNN), radial basis neural networks (RBNN), and neuro-fuzzy systems (ANFIS) to predict and simulate mean daily stage-discharge data derived from two monitoring stations, Bulakbasi and Karaozü, Kizilirmak River, Turkey. Root mean square error (RMSE), Mean absolute percentage error (MAPE), coefficient of determination (R2), and the Developed Discrepancy Ratio (DDR) metrics were utilized to MLMs' performance assessment. The performance evaluation indices (RMSE, MAEP, R2, DDR) for the preeminent MLNN model applied to Bulakhbashi and Karasu stations were determined as (0.29, 1.57, 0.9998, 17.62) and (1.71, 6.56, 0.9980, 6.65), respectively. The MLNN model contributed to a notable enhancement in the RMSE performance index for the aforementioned stations, exhibiting improvements of 87% and 56%, respectively. These results affirm the MLNN's proficiency in accurately capturing the stage-discharge at both monitoring stations.}
+}
 @article{OLA2024101405,
 title = {Remediating Oil Contamination in the Niger Delta Region of Nigeria: Technical Options and Monitoring Strategies},