Accurate prediction of catchment flow has been recognized as an important measures for effective flood-risk management strategy. A neural network modeling approach was used to construct a real time catchment flow prediction model for a river basin. Two types of neural network architectures i.e. feed forward and recurrent neural networks, and three types of training algorithm i.e. Levenberg-Marquardt, Bayesian regularization, and Gradient descent with momentum and adaptive learning rate backpropagation algorithms were examined in this study. A total of six different neural network configurations were developed and examined in terms of optimum results for 1 to 5-h ahead prediction. The methods were used to predict flow in the Cilalawi River in Indonesia, and their performances were evaluated using various statistical indices. The modeling results indicate that reasonable prediction accuracy was achieved for most of models for 1-h ahead forecast with correlation >0.91. However, the model accuracy deteriorates as the lead-time increases. When compared, a 4-10-1 recurrent network and 4-4-1 feed forward network, both trained with the Levenberg-Marquardt algorithm has produced a better performances on indicators related to average goodness of prediction for the 1 to 5-h ahead river flow forecasts compared to other models. Feed forward network trained with gradient descent with momentum and adaptive learning rate backpropagation algorithm model appears to be the worst of the adaptive techniques investigated in terms of modeling performances. Thus, the results of the study suggest that recurrent and feed forward network trained with Levenberg-Marquardt are able to forecast the catchment flow up to 5 h in advance with reasonable prediction accuracy.
The adaptation level among maize genotypes under drought stress is strongly affected by morphology and physiology aspects. To assess the adaptation level of maize hybrids to drought weight, an experiment was conducted in the dry season of 2016 (June to September) at Maros Experimental Station. A total of 70 maize hybrids candidates were evaluated under drought stress at generative (flowering stage) until physiological maturity. The results indicated that leaf rolling scores were negatively correlated with grain yield under drought stress conditions. The hybrid 26/B11209 and P 31 that experienced early leaf moving and a higher leaf rolling score ranged from 4.3, and 3.9 had grain yield of only 1.3 t/ha and 1.2 t/ha respectively, lower compared to the hybrid 34/Mal 03 and Bisi 18 that experienced a delayed leaf rolling and lower leaf rolling score (<2.5) with yields of 4.3 t/ha and 3.9 t/ha respectively. The hybrids 34/Mal 03 and Bisi18 had mechanisms to reduce the area of leaves affected by radiation and maintain relatively higher leaf moisture content compared to hybrids 26 / B11209 and P 31. Leaf relative moisture content of hybrid maize 34/Mal 03 and Bisi 18 were higher viz., 79.9% and 78.7% respectively and lower leaf temperatures (39.4-39.8 °C) as compared to hybrids 26 / B11209 and P 31. The effective score assessment time of leaf rolling of the hybrid genotypes was when the whole hybrid genotypes experienced leaf rolling with ±50% genotype had leaf rolling scored 2 and ±50% of other genotypes scored 3.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.