Research Article The Comparison of Grey System and the Verhulst Model for Rainfall and Water in Dam Prediction Chalermchai Puripat and Sukuman Sarikavanij Department of Mathematics, Faculty of Science, King Mongkut’s University of Technology onburi (KMUTT), Bangkok 10140, ailand Correspondence should be addressed to Sukuman Sarikavanij; sukuman@hotmail.com Received 6 November 2017; Revised 12 April 2018; Accepted 10 May 2018; Published 4 September 2018 Academic Editor: Yanbo Huang Copyright © 2018 Chalermchai Puripat and Sukuman Sarikavanij. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Atimeseriesofdataofrainfallinailandbetweentheyears2005and2015wasemployedtopredictpossiblefuturerainfallbasedon JulongDeng’sgreysystemstheoryandthegreyVerhulstmodeltoseewhichmodelcanpredictmoreaccuratelywithuncertainand limiteddata.Firstly,therainfalldatawerearrangedtodisplaytheoverallpatternsofrainfallvolumealongwithitsfrequencyaswellas the temperature during ailand’s rainy seasons. is makes it possible to see the cycle of rainfall, which is too long for people to intuitively understand the nature of precipitation. One puzzling phenomenon that has made rainfall forecast elusive is the un- predictabilityofthehaphazardnatureofrainfallinailand.Amoreprecisepredictionwouldcertainlyresultinabettercontrolof water volume in rivers and dams for fruitful agricultural business and adequate human consumption. is can also prevent the flooding that can devastate the economy and transportation of the whole country and also tremendously improve the future water managementpolicyinmanyways.iseffectivepredictioncouldalsobeemployedelsewherearoundtheglobeforsimilarbenefits. Hence, the grey systems theory and the grey Verhulst model are juxtaposed to determine a better prediction possible. 1. Introduction Waterisextremelyimportanttoailand’sagricultureaswell as people largely because the country’s physical terrain is rather flat and consequently appropriate for agriculture while thedenselypopulatedcitiesrequireamplesupplyofwaterfor daily use and consumption. Such needs for water would not have been a serious problem if there had been adequate reservoirs and means to conserve rain water for later uses. Whathasexacerbatedtheproblemsisthehaphazardnatureof precipitation patterns during the rainy seasons. Such un- certainty results in prolonged droughts and severe floods. Fortunately, to relieve his people’s suffering from having too much or too little water, the late King Bhumibol Adulyadej initiatedvariousRoyalprojectsofwaterresourcedevelopment and management. Some examples are water resources de- velopment at Huay Hong Krai National Park, Doi Saket, and Huay Jo Reservoir, Chiang Mai; drainage projects from lowland and swamp areas of Bacho, Bacho, Narathiwat; building water reservoirs in strategic regions of ailand, for instancePaSakRiverDam;andfloodreliefprojectsinLopburi and Saraburi. e king had also invented a method to resolve water shortage problems in other ways such as the cloud seeding procedure for artificial rains [1]. With technological and theoretical advances in science and mathematics during this decade, however, the outlook of rainfall prediction has been improved. Beginning in 1982, Professor Julong Deng’s grey systems theory [2] has attracted worldwide attention of researchers and has been utilized in diversified fields of study such as natural science, engineering science, and many others [3]. Grey systems theory focuses mainly on systems that have partially known and unknown information. Other solutions similar to grey systems were initiated through technological progression. Solutions to un- certain systems have become a challenge for further develop- mentinanyassociatedfields.However,onepossiblemeanscan be provided by different variations of grey systems theory [4]. GM(1,1) model is an important model in grey models family. Hindawi Advances in Meteorology Volume 2018, Article ID 7169130, 11 pages https://doi.org/10.1155/2018/7169130