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Soil moisture quantity prediction using optimized neural supported model for sustainable agricultural applications
Published in Elsevier Inc.
Volume: 28
Predicting soil moisture quantity could directly help the people engaged in sustainable agriculture and associated socio-economic structures. Recently researchers have engaged traditional and machine learning based models to predict soil moisture quantity. In the current study a modified Flower Pollination Algorithm (MFPA) has been employed to train Artificial Neural Network (ANN) to predict soil moisture quantity. The proposed method is compared with well known PSO (Particle Swarm optimization) supported ANN and Cuckoo Search (CS) supported ANN along with MLP-FFN classifier. The stability of the proposed model in presence of varying weather conditions has been established by performing a stability analysis using data level perturbation. Experimental results have indicated that NN-MFPA achieved an average RMSE of 0.0019 and outperformed other models. The ingenuity of the proposed model is further established by performing Wilcoxon rank test with 5% level of significance. © 2018 Elsevier Inc.
About the journal
JournalData powered by TypesetSustainable Computing: Informatics and Systems
PublisherData powered by TypesetElsevier Inc.
Open AccessNo