Rainfall prediction and meteorological drought analysis in the Sakae Krang River basin of Thailand

Wichitarapongsakun, Patana; Sarin, Charoon; Klomjek, Pantip; Chuenchooklin, Sombat

doi:10.1016/j.anres.2016.05.003

Cited by 26 publications

(26 citation statements)

References 7 publications

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“…The trend in wetness however, has paved the way for weak drought conditions in recent times. One research found moderate to weak drought episodes in some areas of Thailand, including the Sakae Krang River basin in their analysis of the rainfall and meteorological drought indicators on 2015 [14]. Such previous analysis thereby endorse the suggestions in this analysis that various regions in the country, including Khampheang Phet, maybe prone to extended periods of drought and wetness with inconsistent expressions of such conditions.…”

Section: )supporting

confidence: 58%

Analysis of Meteorological Data and Drought in the Lower Northern Part of Thailand

Demmsie¹

2021

ITII

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The study areas include Phitsanulok, Nakhon Sawan and Khamphang Phet. The meteorological data between 1986 and 2015, was collected from the meteorological rain gauge stations situated in the Lower Northern part of Thailand. This study involves the analysis of the meteorological data and drought trends in the northern region of Thailand from 1986 to 2015. The study used the Meteorological Drought Monitor software (MDM) to analyze the drought trends through the Standardized Precipitation Index (SPI). From this analysis, we then presented the results through graphs representing the trends in meteorological parameters and drought index. The results showed that, the annual rainfall measurement of the regions analyzed continues to reduce consistently as the temperature increase. The drought trends in these provinces observed to considerably lean towards dry conditions. Nakhon Sawan, for instance, experienced extreme drought in 1977(SPI =-2.5) and moderate wetness in 2013. Kampheang Phet recorded moderately wet conditions (SPI =1.5) in 2013 and 2014 years, and near normal conditions (SPI = -1.1) in 1996. Lastly, Phitsanulok was majorly comprised of moderately dry and severely dry, especially for 2015(SPI =-22). From the findings, awareness should be created to foster the need for environmental conservation, besides to improve environmental protection and the levels of precipitation.

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Section: )supporting

confidence: 58%

Analysis of Meteorological Data and Drought in the Lower Northern Part of Thailand

Demmsie¹

2021

ITII

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“…Single Exponential Smoothing (SES) model has been used by some researchers in previous studies for smoothing fluctuation in sequential demand patterns to provide stable estimations ( Sopipan, 2015 ; Pagourtzi & Assimakopoulos, 2018 ). SES can be used for rainfall predictions ( Wichitarapongsakun et al, 2016 ) using Eq. (1) .…”

Section: Literature Reviewmentioning

confidence: 99%

A new model for learning-based forecasting procedure by combining k-means clustering and time series forecasting algorithms

Hartomo

Nataliani

2021

PeerJ Computer Science

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This paper aims to propose a new model for time series forecasting that combines forecasting with clustering algorithm. It introduces a new scheme to improve the forecasting results by grouping the time series data using k-means clustering algorithm. It utilizes the clustering result to get the forecasting data. There are usually some user-defined parameters affecting the forecasting results, therefore, a learning-based procedure is proposed to estimate the parameters that will be used for forecasting. This parameter value is computed in the algorithm simultaneously. The result of the experiment compared to other forecasting algorithms demonstrates good results for the proposed model. It has the smallest mean squared error of 13,007.91 and the average improvement rate of 19.83%.

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“…Most of the approaches are based on the use of single variables of either precipitation or vegetation conditions. Examples of predictions based on precipitation data are either based on SPI as is the case in Ali [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16], [1,2,20,24] others define a super index of drought indices in the approach of [23] and [25] that define Multi-variate standardised dry index (MSDI) and Drought defining Index (DDI) respectively. The use of vegetation conditions in [21] in a forecast study stands-out in its use of 11 attributes to predict vegetation conditions.…”

Section: Meterological Droughtmentioning

confidence: 99%

A Mixed Model Approach to Vegetation Condition Prediction Using Artificial Neural Networks (ANN): Case of Kenya’s Operational Drought Monitoring

et al. 2019

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Droughts, with their increasing frequency of occurrence, continue to negatively affect livelihoods and elements at risk. For example, the 2011 in drought in east Africa has caused massive losses document to have cost the Kenyan economy over $12bn. With the foregoing, the demand for ex-ante drought monitoring systems is ever-increasing. The study uses 10 precipitation and vegetation variables that are lagged over 1, 2 and 3-month time-steps to predict drought situations. In the model space search for the most predictive artificial neural network (ANN) model, as opposed to the traditional greedy search for the most predictive variables, we use the General Additive Model (GAM) approach. Together with a set of assumptions, we thereby reduce the cardinality of the space of models. Even though we build a total of 102 GAM models, only 21 have R 2 greater than 0.7 and are thus subjected to the ANN process. The ANN process itself uses the brute-force approach that automatically partitions the training data into 10 sub-samples, builds the ANN models in these samples and evaluates their performance using multiple metrics. The results show the superiority of 1-month lag of the variables as compared to longer time lags of 2 and 3 months. The champion ANN model recorded an R 2 of 0.78 in model testing using the out-of-sample data. This illustrates its ability to be a good predictor of drought situations 1-month ahead. Investigated as a classifier, the champion has a modest accuracy of 66% and a multi-class area under the ROC curve (AUROC) of 89.99%

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Rainfall prediction and meteorological drought analysis in the Sakae Krang River basin of Thailand

Cited by 26 publications

References 7 publications

Analysis of Meteorological Data and Drought in the Lower Northern Part of Thailand

Analysis of Meteorological Data and Drought in the Lower Northern Part of Thailand

A new model for learning-based forecasting procedure by combining k-means clustering and time series forecasting algorithms

A Mixed Model Approach to Vegetation Condition Prediction Using Artificial Neural Networks (ANN): Case of Kenya’s Operational Drought Monitoring

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