Agronomic Disaster Management using Artificial Intelligence - A Case Study

M Sudha


Artificial Intelligence has become an essential tool in various hydrological data-driven forecast scenarios. The existing needs of farm management activities have witnessed the necessity of an intelligent decision support for strategic planning and implementation. This case study reports the benefits of application of neural network based short range precipitation prediction model in agronomic disaster management (ADM). This investigation describes the benefits of data-driven decision support systems for agronomic sustainability. Neural Network Architecture emerged recently have heterogeneous network design thereby suits for solving complex problems. The methodical evaluation conducted on agronomic disaster management framework designed using rough, genetic and neuro computing approach reported peak prediction accuracy of 97.21 % while the learning rate of the network was set to 0.7 for a fixed momentum of 0.5 producing a nominal error rate of 02.79%.


Artificial intelligence, Agronomic disaster management, Daily precipitation prediction, Data-driven computing

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