Point estimation of soil moisture characteristic curve using artificial neural networks and its optimizing by genetic algorithm in Agro-Industries of Khouzestan | ||
| پژوهش های آبخیزداری | ||
| Article 10, Volume 29, Issue 3 - Serial Number 112, October 2016, Pages 101-112 PDF (577.13 K) | ||
| Document Type: Research | ||
| DOI: 10.22092/wmej.2016.112501 | ||
| Authors | ||
| Nabi Junadeleh1; Habiballah Nadian2; Bijan Khalilimoghadam* 3; Shoja Ghorbanidashtaki4 | ||
| 1Master's student in soil science, Ramin Khuzestan University of Agriculture and Natural Resources | ||
| 2Associate Professor, Department of Soil Science, Ramin Khuzestan University of Agriculture and Natural Resources | ||
| 3Assistant Professor, Department of Soil Science, Ramin Khuzestan University of Agriculture and Natural Resources | ||
| 4Assistant Professor of Soil Science Department of Shahrekord University | ||
| Abstract | ||
| Soil hydraulic properties have key role in sugar cane cultivation management. The purpose of this study is to estimate soil moisture characteristic curve using an artificial neural network and its optimization with genetic algorithm. Therefore, based on the cultivation operations management and soil properties included: organic matter content, soil texture, electrical conductivity, sodium adsorption ratio, 4 land unit tracts in Debel-Khozaii, Amir-Kabir, Karoon and Haft-Tapeh agro-industries were selected. A total of 310 soil samples from both 0-40 and 40-80 cm of soil profiles were collected. In this study, five models were arranged in hierarchy to estimate soil hydraulic properties with ANNs. The performances of the models were evaluated using Spearman's correlation coefficient (r) between the observed and the estimated values, normalized mean square error (NMSE), and mean absolute error (MAE). Owing to the fact that the selection of each of the variable parameters of neural network necessitated recurring trails and errors, and consequently teaching a large number of networks with various topologies, genetic algorithm method was utilized for finding the optimization of these parameters and the efficiency of this method was examined in terms of the optimization of neural network. Results showed that the neural network has a high degree of accuracy in modeling and estimating soil moisture characteristic curve (R =0.943, MAE=0.019, NMSE=0.054). Also, combining artificial neural networks with genetic algorithm for optimizing the conditions of the artificial neural networks implementation was positive and combining approach indicated its superiority over non-optimized implementation of artificial neural networks in all cases (R=0.985, MAE=0.01, NMSE =0.0151). | ||
| Keywords | ||
| Genetic algorithms; Artificial Neural Networks; Soil moisture characteristic curve | ||
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