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• W1999461467 endingPage "81" @default.
• W1999461467 startingPage "65" @default.
• W1999461467 abstract "The forecasting of drought based on cumulative influence of rainfall, temperature and evaporation is greatly beneficial for mitigating adverse consequences on water-sensitive sectors such as agriculture, ecosystems, wildlife, tourism, recreation, crop health and hydrologic engineering. Predictive models of drought indices help in assessing water scarcity situations, drought identification and severity characterization. In this paper, we tested the feasibility of the Artificial Neural Network (ANN) as a data-driven model for predicting the monthly Standardized Precipitation and Evapotranspiration Index (SPEI) for eight candidate stations in eastern Australia using predictive variable data from 1915 to 2005 (training) and simulated data for the period 2006–2012. The predictive variables were: monthly rainfall totals, mean temperature, minimum temperature, maximum temperature and evapotranspiration, which were supplemented by large-scale climate indices (Southern Oscillation Index, Pacific Decadal Oscillation, Southern Annular Mode and Indian Ocean Dipole) and the Sea Surface Temperatures (Nino 3.0, 3.4 and 4.0). A total of 30 ANN models were developed with 3-layer ANN networks. To determine the best combination of learning algorithms, hidden transfer and output functions of the optimum model, the Levenberg–Marquardt and Broyden–Fletcher–Goldfarb–Shanno (BFGS) quasi-Newton backpropagation algorithms were utilized to train the network, tangent and logarithmic sigmoid equations used as the activation functions and the linear, logarithmic and tangent sigmoid equations used as the output function. The best ANN architecture had 18 input neurons, 43 hidden neurons and 1 output neuron, trained using the Levenberg–Marquardt learning algorithm using tangent sigmoid equation as the activation and output functions. An evaluation of the model performance based on statistical rules yielded time-averaged Coefficient of Determination, Root Mean Squared Error and the Mean Absolute Error ranging from 0.9945–0.9990, 0.0466–0.1117, and 0.0013–0.0130, respectively for individual stations. Also, the Willmott's Index of Agreement and the Nash–Sutcliffe Coefficient of Efficiency were between 0.932–0.959 and 0.977–0.998, respectively. When checked for the severity (S), duration (D) and peak intensity (I) of drought events determined from the simulated and observed SPEI, differences in drought parameters ranged from − 1.41–0.64%, − 2.17–1.92% and − 3.21–1.21%, respectively. Based on performance evaluation measures, we aver that the Artificial Neural Network model is a useful data-driven tool for forecasting monthly SPEI and its drought-related properties in the region of study." @default.
• W1999461467 created "2016-06-24" @default.
• W1999461467 creator A5029315623 @default.
• W1999461467 creator A5065141057 @default.
• W1999461467 date "2015-07-01" @default.
• W1999461467 modified "2023-10-12" @default.
• W1999461467 title "Application of the Artificial Neural Network model for prediction of monthly Standardized Precipitation and Evapotranspiration Index using hydrometeorological parameters and climate indices in eastern Australia" @default.
• W1999461467 cites W1598241312 @default.
• W1999461467 cites W1671644826 @default.
• W1999461467 cites W174598607 @default.
• W1999461467 cites W1854912902 @default.
• W1999461467 cites W1964425467 @default.
• W1999461467 cites W1965867673 @default.
• W1999461467 cites W1966145422 @default.
• W1999461467 cites W1966919913 @default.
• W1999461467 cites W1967334197 @default.
• W1999461467 cites W1969621693 @default.
• W1999461467 cites W1973078804 @default.
• W1999461467 cites W1977539011 @default.
• W1999461467 cites W1981159242 @default.
• W1999461467 cites W1987557628 @default.
• W1999461467 cites W1988243170 @default.
• W1999461467 cites W1991811454 @default.
• W1999461467 cites W1994169669 @default.
• W1999461467 cites W1994638005 @default.
• W1999461467 cites W1994650774 @default.
• W1999461467 cites W1995341919 @default.
• W1999461467 cites W1999580027 @default.
• W1999461467 cites W1999687643 @default.
• W1999461467 cites W2001675020 @default.
• W1999461467 cites W2004571187 @default.
• W1999461467 cites W2008720804 @default.
• W1999461467 cites W2012272234 @default.
• W1999461467 cites W2014202046 @default.
• W1999461467 cites W2014367122 @default.
• W1999461467 cites W2017214666 @default.
• W1999461467 cites W2021640400 @default.
• W1999461467 cites W2022280600 @default.
• W1999461467 cites W2022389497 @default.
• W1999461467 cites W2023692298 @default.
• W1999461467 cites W2026950817 @default.
• W1999461467 cites W2026977685 @default.
• W1999461467 cites W2031346902 @default.
• W1999461467 cites W2031946334 @default.
• W1999461467 cites W2032276178 @default.
• W1999461467 cites W2033245860 @default.
• W1999461467 cites W2033904036 @default.
• W1999461467 cites W2037460094 @default.
• W1999461467 cites W2038224183 @default.
• W1999461467 cites W2039049978 @default.
• W1999461467 cites W2040916393 @default.
• W1999461467 cites W2046794862 @default.
• W1999461467 cites W2047884674 @default.
• W1999461467 cites W2048344816 @default.
• W1999461467 cites W2050985317 @default.
• W1999461467 cites W2053035314 @default.
• W1999461467 cites W2064671841 @default.
• W1999461467 cites W2065133690 @default.
• W1999461467 cites W2069475686 @default.
• W1999461467 cites W2070427882 @default.
• W1999461467 cites W2071223456 @default.
• W1999461467 cites W2074197685 @default.
• W1999461467 cites W2077910286 @default.
• W1999461467 cites W2077968790 @default.
• W1999461467 cites W2078174366 @default.
• W1999461467 cites W2079269412 @default.
• W1999461467 cites W2082001988 @default.
• W1999461467 cites W2087070363 @default.
• W1999461467 cites W2087255756 @default.
• W1999461467 cites W2087414266 @default.
• W1999461467 cites W2092556113 @default.
• W1999461467 cites W2093273670 @default.
• W1999461467 cites W2093401535 @default.
• W1999461467 cites W2093947774 @default.
• W1999461467 cites W2097403132 @default.
• W1999461467 cites W2097668422 @default.
• W1999461467 cites W2101927907 @default.
• W1999461467 cites W2107878489 @default.
• W1999461467 cites W2108706807 @default.
• W1999461467 cites W2108886061 @default.
• W1999461467 cites W2111286455 @default.
• W1999461467 cites W2121534406 @default.
• W1999461467 cites W2124037634 @default.
• W1999461467 cites W2124076118 @default.
• W1999461467 cites W2134206969 @default.
• W1999461467 cites W2142029992 @default.
• W1999461467 cites W2147746661 @default.
• W1999461467 cites W2152208495 @default.
• W1999461467 cites W2153133730 @default.
• W1999461467 cites W2155482699 @default.
• W1999461467 cites W2158952405 @default.
• W1999461467 cites W2165772416 @default.
• W1999461467 cites W2167182224 @default.
• W1999461467 cites W2179095720 @default.
• W1999461467 cites W2395814628 @default.
• W1999461467 cites W3150714801 @default.
• W1999461467 doi "https://doi.org/10.1016/j.atmosres.2015.03.018" @default.
• W1999461467 hasPublicationYear "2015" @default.

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