Optimization of neural networks for precipitation analysis in a humid region to detect drought and wet year alarms

Meteorological Applications

Volumn 23, Issue 1, 2016, Pages 91-100

  Valipour, Mohammad ^a  

^a ISLAMIC AZAD UNIVERSITY   (Iran)

Author keywords

Drought alarm; Forest cover; Meteorological conditions; Seasonal trend; Wet year alarm

Indexed keywords

ALARM SYSTEMS; DROUGHT; MATLAB; METEOROLOGICAL INSTRUMENTS;

ANNUAL PRECIPITATION; AUTOREGRESSIVE NEURAL NETWORKS; FOREST COVER; METEOROLOGICAL CONDITION; PRECIPITATION DATA; PRECIPITATION GAUGES; SEASONAL TRENDS; WET YEAR ALARM;

NEURAL NETWORKS;

ACCURACY ASSESSMENT; ARTIFICIAL NEURAL NETWORK; DROUGHT; FOREST COVER; HUMID ENVIRONMENT; METEOROLOGY; MODEL VALIDATION; OPTIMIZATION; PRECIPITATION (CLIMATOLOGY); SEASONAL VARIATION; SPATIOTEMPORAL ANALYSIS; DETECTION METHOD; HUMID TROPICS; PRECIPITATION ASSESSMENT; SOFTWARE; TESTING METHOD; TREND ANALYSIS;

GILAN; IRAN;

EID: 84965160300     PISSN: 13504827     EISSN: 14698080     Source Type: Journal    
DOI: 10.1002/met.1533     Document Type: Article

References (48)

1. Banihabib ME, Valipour M, Behbahani SMR. 2012. Comparison of autoregressive static and artificial dynamic neural network for the forecasting of monthly inflow of Dez reservoir. J. Environ. Sci. Technol. 13(4): 1-14.
2. Burlando P, Montanari A, Ranzi R. 1996. Forecasting of storm rainfall by combined use of radar, rain gages and linear models. Atmos. Res. 42(1-4): 199-216.
3. Burlando P, Rosso R, Cadavid LG, Salas JD. 1993. Forecasting of short-term rainfall using ARMA models. J. Hydrol. 144(1-4): 193-211.
4. Cigizoglu HK. 2005. Generalized regression neural network in monthly flow forecasting. Civil Eng. Environ. Syst. 22(2): 71-81.
5. Dasari HP, Salgado R. 2015. Numerical modelling of heavy rainfall event over Madeira Island in Portugal: sensitivity to different micro physical processes. Meteorol. Appl. 22: 113-127.
6. Foresti L, Seed A. 2015. On the spatial distribution of rainfall nowcasting errors due to orographic forcing. Meteorol. Appl. 22: 60-74.
7. Hasan MM, Dunn PK. 2010. A simple Poisson-gamma model for modelling rainfall occurrence and amount simultaneously. Agric. For. Meteorol. 150(10): 1319-1330.
8. Hasan M, Tsegaye T, Shi X, Schaefer G, Taylor G. 2008. Model for predicting rainfall by fuzzy set theory using USDA scan data. Agric. Water Manage. 95(12): 1350-1360.
9. Ju Q, Yu Z, Hao Z, Ou G, Zhao J, Liu D. 2009. Division-based rainfall-runoff simulations with BP neural networks and Xinanjiang model. Neurocomputing 72(13): 2873-2883.
10. Kan G, Yao C, Li Q, Li Z, Yu Z, Liu Z, et al. 2015. Improving event-based rainfall-runoff simulation using an ensemble artificial neural network based hybrid data-driven model. Stoch. Environ. Res. Risk Assess., 29(5): 1345-1370.
11. Kim DK, Lee D. 2015. Atmospheric thickness and vertical structure properties in wintertime precipitation events from microwave radiometer, radiosonde and wind profiler observations. Meteorol. Appl. 22: 599-609, DOI: 10.1002/met.1494.
12. Kisi O, Cigizoglu HK. 2007. Comparison of different ANN techniques in river flow prediction. Civil Eng. Environ Syst. 24(3): 211-231.
13. Kisi O, Shiri J. 2011. Precipitation forecasting using wavelet-genetic programming and wavelet-neuro-fuzzy conjunction models. Water Resour. Manage. 25(13): 3135-3152.
14. Kisi O, Shiri J. 2012. Reply to discussion of "Precipitation forecasting using wavelet-genetic programming and wavelet-neuro-fuzzy conjunction models". Water Resour. Manage. 26(12): 3663-3665.
15. Korsholm US, Petersen C, Sass BH, Nielsen NW, Jensen DG, Olsen BT, et al. 2015. A new approach for assimilation of 2D radar precipitation in a high-resolution NWP model. Meteorol. Appl. 22: 48-59.
16. Mahdizadeh Khasraghi M, Gholami Sefidkouhi MA, Valipour M. 2015. Simulation of open- and closed-end border irrigation systems using SIRMOD. Arch. Agron. Soil Sci. 61(7): 929-941.
17. Maier HR, Jain A, Dandy GC, Sudheer KP. 2010. Methods used for the development of neural networks for the prediction of water resource variables in river systems: current status and future directions. Environ. Model Softw. 25(8): 891-909.
18. MATLAB Toolboxes. 2010. MATLAB/Toolboxes/Neural Network. Version 7.11.0.584 (R2010b). http://www.mathworks.com (accessed 25 June 2010).
19. Pombo S, de Oliveira RP, Mendes A. 2014. Validation of remote-sensing precipitation products for Angola. Meteorol. Appl. 22: 395-409, DOI:10.1002/met.1467.
20. Rahimi S, Gholami Sefidkouhi MA, Raeini-Sarjaz M, Valipour M. 2015. Estimation of actual evapotranspiration by using MODIS images (a case study: Tajan catchment). Arch. Agron. Soil Sci. 61(5): 695-709.
21. Rossi PJ, Hasu V, Koistinen J, Moisseev D, Makela A, Saltikoff E. 2014. Analysis of a statistically initialized fuzzy logic scheme for classifying the severity of convective storms in Finland. Meteorol. Appl. 21(3): 656-674.
22. Schneider U, Becker A, Meyer-Christoffer A, Ziese M, Rudolf B. 2011. Global precipitation analysis products of the GPCC. Global Precipitation Climatology Centre (GPCC), Deutscher Wetterdienst, Offenbach a. M., Germany.
23. Valipour M. 2012. Critical areas of iran for agriculture water management according to the annual rainfall. Eur. J. Sci. Res. 84(4): 600-608.
24. Valipour M. 2014a. Drainage, waterlogging, and salinity. Arch. Agron. Soil Sci. 60(12): 1625-1640.
25. Valipour M. 2014b. Future of the area equipped for irrigation. Arch. Agron. Soil Sci. 60(12): 1641-1660.
26. Valipour M. 2014c. Application of new mass transfer formulae for computation of evapotranspiration. J. Appl. Water Eng. Res. 2(1): 33-46.
27. Valipour M. 2014d. Use of average data of 181 synoptic stations for estimation of reference crop evapotranspiration by temperature-based methods. Water Resour. Manage. 28(12): 4237-4255.
28. Valipour M. 2014e. Temperature analysis of reference evapotranspiration models. Meteorol. Appl. 22: 385-394, DOI: 10.1002/met.1465.
29. Valipour M. 2015a. Comparative evaluation of radiation-based methods for estimation of potential evapotranspiration. J. Hydrol. Eng. 20(5): 04014068.
30. Valipour M. 2015b. Importance of solar radiation, temperature, relative humidity, and wind speed for calculation of reference evapotranspiration. Arch. Agron. Soil Sci. 61(2): 239-255.
31. Valipour M. 2015c. Long-term runoff study using SARIMA and ARIMA models in the United States. Meteorol. Appl. 22: 592-598, DOI:10.1002/met.1491.
32. Valipour M. 2015d. Evaluation of radiation methods to study potential evapotranspiration of 31 provinces. Meteorol. Atmos. Phys. 127(3): 289-303.
33. Valipour M. 2015e. A comprehensive study on irrigation management in Asia and Oceania. Arch. Agron. Soil Sci. 61(9): 1247-1271.
34. Valipour M. 2015f. Future of agricultural water management in Africa. Arch. Agron. Soil Sci. 61(7): 907-927.
35. Valipour M. 2015g. Study of different climatic conditions to assess the role of solar radiation in reference crop evapotranspiration equations. Arch. Agron. Soil Sci. 61(5): 679-694.
36. Valipour M. 2015h. Investigation of Valiantzas' evapotranspiration equation in Iran. Theor. Appl. Climatol., 121(1-2): 267-278.
37. Valipour M, Banihabib ME, Behbahani SMR. 2012a. Monthly inflow forecasting using autoregressive artificial neural network. J. Appl. Sci. 12(20): 2139-2147.
38. Valipour M, Banihabib ME, Behbahani SMR. 2012b. Parameters estimate of autoregressive moving average and autoregressive integrated moving average models and compare their ability for inflow forecasting. J. Math. Stat. 8(3): 330-338.
39. Valipour M, Banihabib ME, Behbahani SMR. 2013. Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir. J. Hydrol. 476: 433-441.
40. Valipour M, Eslamian S. 2014. Analysis of potential evapotranspiration using 11 modified temperature-based models. Int. J. Hydrol. Sci. Technol. 4(3): 192-207.
41. Valipour M, Gholami Sefidkouhi MA, Eslamian S. 2015a. Surface irrigation simulation models: a review. Int. J. Hydrol. Sci. Technol. 5(1): 51-70.
42. Valipour M, Montazar AA. 2012a. Optimize of all effective infiltration parameters in furrow irrigation using visual basic and genetic algorithm programming. Aust. J. Basic Appl. Sci. 6(6): 132-137.
43. Valipour M, Montazar AA. 2012b. Sensitive analysis of optimized infiltration parameters in SWDC model. Adv. Environ. Biol. 6(9): 2574-2581.
44. Valipour M, Ziatabar Ahmadi M, Raeini-Sarjaz M, Gholami Sefidkouhi MA, Shahnazari A, Fazlola R, et al. 2015b. Agricultural water management in the world during past half century. Arch. Agron. Soil Sci. 61(5): 657-678.
45. Wagesho N, Goel NK, Jain MK. 2013. Temporal and spatial variability of annual and seasonal rainfall over Ethiopia. Hydrol. Sci. J. 58(2): 354-373.
46. Wu M, Lam H, Li K. 2015. Characterization and indexing of heavy rainstorms in Hong Kong. Meteorol. Appl. 22: 25-36.
47. Yu PS, Chen ST, Wu CC, Lin SC. 2004. Comparison of grey and phase-space rainfall forecasting models using a fuzzy decision method. Hydrol. Sci. J. 49(4): 655-672.
48. Yu PS, Yang TC, Kuo CC. 2006. Evaluating long-term trends in annual and seasonal precipitation in Taiwan. Water Resour. Manage. 20(6): 1007-1023.