JACIII Vol.20 No.5 pp. 730-734
doi: 10.20965/jaciii.2016.p0730

Short Paper:

Neural Approach to Predict Flow Discharge in River Chenab Pakistan

Tanzila Saba

College of Computer and Information Sciences, Prince Sultan University
Rafha Street, Riyadh 11586, Kingdom of Saudi Arabia

March 29, 2016
June 7, 2016
Online released:
September 20, 2016
September 20, 2016
computational intelligence, flood forecasting, risk management

River water flow forecast in general and particularly in floods is of worth importance for monitoring operations of floods in canals and rivers. Floods in rivers bring destructions to road, houses, crops and causes human dislocation. The River Chenab is one of the largest rivers in Pakistan and has a historical recording of heavy floods. Prior to heavy floods, in time warning is mandatory to save lives and property. Accordingly, this paper presents an intelligent model to predict an advance alarming water flow from Chenab River. Standard learning algorithm is applied to train the ANN for this task. Inputs to the neural network are taken from the daily discharge values and the output layer composed of four neurons to represent number of predicted days. Moreover, trial and error approach is adopted to select appropriate number of inputs for time-series data. Two different architecture (single and double hidden layers) of neural network are evaluated and compared to find the most suitable one. Additionally, two activation functions are tested. The results thus achieved reveal well in time warning to the surroundings to secure flood victims. However, during low discharge, neural network miscalculated.

  1. [1] T. Saba and A. Rehman, “Effects of Artificially Intelligent Tools on Pattern Recognition,” Int. J. of Machine Learning and Cybernetics. Vol.4, No.2, pp. 155-162, doi: 10.1007/s13042-012-0082-z, 2012.
  2. [2] A. Rehman and T. Saba, “Neural Network for Document Image Preprocessing,” Artificial Intelligence Review, Vol.42, No.2, pp. 253-273, doi: 10.1007/s10462-012-9337-z, 2014.
  3. [3] A. Joorabchi, H. Zhang, and M. Blumenstein, “Application of artificial neural networks in flow discharge prediction for the Fitzroy River, Australia,” J. of Coastal Research, Special Issue Vol.50, pp. 287-291, 2007.
  4. [4] S. Chavoshi, Wan Nor A. Sulaiman, B. Saghafian, Md Nasir Bin Sulaiman, and L. Abd Manaf, “Flood prediction in southern strip of Caspian Sea watershed,” Vol.40, No.6, pp. 593-605, 2013.
  5. [5] S. H. Elsafi, “Artificial Neural Networks (ANNs) for flood forecasting at Dongola Station in the River Nile, Sudan,” Alexandria Engineering J., Vol.53, No.3, pp. 655-662, doi:10.1016/j.aej.2014.06.010, 2014.
  6. [6] T. Saba, A. Rehman, and G. Sulong, “Cursive Script Segmentation with Neural Confidence,” Int. J. of Innovative Computing, Information and Control (IJICIC), Vol.7, No.7, pp. 1-10, 2011.
  7. [7] A. Rehman and T. Saba, “Features Extraction for Soccer Video Semantic Analysis: Current Achievements and Remaining Issues,” Artificial Intelligence Review, Vol.41, No.3, pp. 451-461, doi 10.1007/s10462-012-9319-1, 2012.
  8. [8] C. Phetchanchai, A. Selamat, A. Rehman, and T. Saba, “Index Financial Time Series Based on Zigzag-Perceptually Important Points,” J. of Computer Science, Vol.6, No.12, pp. 1389-1395, 2010.
  9. [9] T. Saba, A. Rehman, and G. Sulong, “Improved Statistical Features for Cursive Character Recognition,” Int. J. of Innovative Computing, Information and Control (IJICIC), Vol.7, No.9, pp. 5211-5224, 2011.
  10. [10] T. Saba, A. Rehman, A. Altameem, and M. Uddin, “Annotated Comparisons of Proposed Preprocessing Techniques for Script Recognition,” Neural Computing and Applications, Vol.25, No.6, pp. 1337-1347, doi: 10.1007/s00521-014-1618-9, 2014.
  11. [11] K. Neamah, D. Mohamad, T. Saba, and A. Rehman, “Discriminative features mining for offline handwritten signature verification,” 3D Research, Vol.5, No.3, doi: 10.1007/s13319-013-0002-3, 2014.
  12. [12] A. Rehman and T. Saba, “Evaluation of artificial intelligent techniques to secure information in enterprises,” Artificial Intelligence Review, Vol.42, No.4, pp. 1029-1044, doi: 10.1007/s10462-012-9372-9, 2014.
  13. [13] T. Saba and A. Rehman, “Machine Learning and Script Recognition,” Lambert Academic publisher, pp. 79-83, 2012.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, IE9,10,11, Opera.

Last updated on Mar. 28, 2017