Agronomic Disaster Management using Artificial Intelligence - A Case Study

M Sudha

Abstract


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%.


Keywords


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

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References


Malik, A. and Kumar, A., 2015. Pan evaporation simulation based on daily meteorological data using soft computing techniques and multiple linear regression. Water resources management, 29(6), pp.1859-1872.

Sudha, M. and Subbu, K., 2017. Statistical Feature Ranking and Fuzzy Supervised Learning Approach in Modeling Regional Rainfall Prediction Systems. AGRIS On-line Papers in Economics and Informatics, 9(2), p.117.

Sudha, M., 2017. Intelligent decision support system based on rough set and fuzzy logic approach for efficacious precipitation forecast. Decision Science Letters, 6(1), pp.95-106.

Mohankumar, S. and Balasubramanian, V., 2016. Identifying Effective Features and Classifiers for Short Term Rainfall Forecast Using Rough Sets Maximum Frequency Weighted Feature Reduction Technique. CIT. Journal of Computing and Information Technology, 24(2), pp.181-194.

Sprague Jr, R.H. and Watson, H.J., 1996. Decision support for management. Prentice-Hall, Inc..

Jeong, C., Shin, J.Y., Kim, T. and Heo, J.H., 2012. Monthly precipitation forecasting with a neuro-fuzzy model. Water resources management, 26(15), pp.4467-4483.

Pant, L.M. and Ganju, A., 2004. Fuzzy rule-based system for prediction of direct action avalanches. Current science, 87(1), pp.99-104.

Kothari, M. and Gharde, K.D., 2015. Application of ANN and fuzzy logic algorithms for streamflow modelling of Savitri catchment. Journal of Earth System Science, 124(5), pp.933-943.

Khalili, N., Khodashenas, S.R., Davary, K., Baygi, M.M. and Karimaldini, F., 2016. Prediction of rainfall using artificial neural networks for synoptic station of Mashhad: a case study. Arabian Journal of Geosciences, 9(13), p.624.

Moustris, K.P., Larissi, I.K., Nastos, P.T. and Paliatsos, A.G., 2011. Precipitation forecast using artificial neural networks in specific regions of Greece. Water resources management, 25(8), pp.1979-1993.

Narvekar, M. and Fargose, P., 2015. Daily weather forecasting using artificial neural network. International Journal of Computer Applications, 121(22).

Olaiya, F. and Adeyemo, A.B., 2012. Application of data mining techniques in weather prediction and climate change studies. International Journal of Information Engineering and Electronic Business, 4(1), p.51.

Abraham, A., 2001. Neuro fuzzy systems: State-of-the-art modeling techniques. Connectionist models of neurons, learning processes, and artificial intelligence, pp.269-276.

Kumar, R., Goel, N.K., Chatterjee, C. and Nayak, P.C., 2015. Regional flood frequency analysis using soft computing techniques. Water resources management, 29(6), pp.1965-1978.

Akrami, S.A., El-Shafie, A. and Jaafar, O., 2013. Improving rainfall forecasting efficiency using modified adaptive Neuro-Fuzzy Inference System (MANFIS). Water resources management, 27(9), pp.3507-3523.

Al-Matarneh, L., Sheta, A., Bani-Ahmad, S., Alshaer, J. and Al-oqily, I., 2014. Development of temperature-based weather forecasting models using neural networks and fuzzy logic. International journal of multimedia and ubiquitous engineering, 9(12), pp.343-366.

Awan, J.A. and Bae, D.H., 2014. Improving ANFIS based model for long-term dam inflow prediction by incorporating monthly rainfall forecasts. Water resources management, 28(5), pp.1185-1199.

Bacanli, U.G., Firat, M. and Dikbas, F., 2009. Adaptive neuro-fuzzy inference system for drought forecasting. Stochastic Environmental Research and Risk Assessment, 23(8), pp.1143-1154.

Emamgholizadeh, S., Moslemi, K. and Karami, G., 2014. Prediction the groundwater level of Bastam plain (Iran) by artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). Water resources management, 28(15), pp.5433-5446.

Esmaeelzadeh, S.R., Adib, A. and Alahdin, S., 2015. Long-term streamflow forecasts by Adaptive Neuro-Fuzzy Inference System using satellite images and K-fold cross-validation (Case study: Dez, Iran). KSCE Journal of Civil Engineering, 19(7), p.2298.

Maiti, S. and Tiwari, R.K., 2014. A comparative study of artificial neural networks, Bayesian neural networks and adaptive neuro-fuzzy inference system in groundwater level prediction. Environmental earth sciences, 71(7), pp.3147-3160.

Han, J., Pei, J. and Kamber, M., 2011. Data mining: concepts and techniques. Elsevier.


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ISSN: 1694-2507 (Print)

ISSN: 1694-2108 (Online)