A Simplified Rainfall-Streamflow Network Model on Multivariate Regression Analysis for Water Level Forecasting in Klong Luang
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App. Envi. Res. 36(4) (2014): 53-65 Applied Environmental Research Journal homepage : http://www.tci-thaijo.org/index.php/aer A Simplified Rainfall-Streamflow Network Model on Multivariate Regression Analysis for Water Level Forecasting in Klong Luang (KGT.19 Station) Sub-watershed, Chon Buri Province, Thailand Charoen Panant, Samakkee Boonyawat* Department of Conservation, Faculty of Forestry, Kasetsart University, Bangkok, Thailand * Corresponding author: Email: [email protected] Article History Submitted: 7 March 2014/ Accepted: 3 July 2014/ Published online: 17 October 2014 Abstract A simplified rainfall-streamflow network model based on multivariate linear regression (MLR) analysis has been proposed. To determine significant coefficients of streamflow network, eleven MLR models were examined. The study’s three objectives were 1) to develop a novel a mathematical model based on MLR analysis for forecasting optimal water levels; 2) to determine the most significant coefficient of rainfall-streamflow network among in the area of interest in the vicinity of Klong Luang sub-watershed KGT.19 station; and 3) to apply the optimal MLR model for water level and flood forecasting maps in Klong Luang Sub-watershed. We used Geographic Information System (GIS) and Remotely Sensed Data (RS) data recorded from Klong Luang (KGT.19 Station) sub-watershed, and Phanat Nikhom, Chonburi, Ban Bueng and Phan Thong districts, in Chonburi Province, Thailand. The findings indicated that the MLR based Model No. 8 is the most applicable and effective. The proposed model also could be applied in water level forecasting, water resource management, flood hazard planning, and flood early warning. Keywords: Water level forecasting; Multiple regression analysis; Streamflow network model; Klong Luang Sub-watershed 54 App. Envi. Res. 36(4) (2014): 53-65 Introduction therefore an essential tool in flood disaster Geographic Information System (GIS) and mitigation and management. In some cases Remote Sensing (RS) are tools that use the [3-6], conceptual rainfall-streamflow models Digital Elevation Model (DEM) to create e.g. the neural network model, are used even interactive queries, analyze spatial informa- though these models are not intended for tion, edit data, map and present the results of forecasting purposes, An optimal neural net- operations. The GIS and hydrologic model work requires significant training and collec- can both predict flow rate and the flood prone tion of large quantities of data. area. On the other hand, basic water manage- Recent studies have shown that the use of ment theory can be applied to develop an real-time precipitation and streamflow data in integrated model from meteorology, geogra- rainfall and streamflow routing models can phy and hydrography in terms of a spatial be utilized as a parameter for water level information system. Fotheringham and Rogerson forecasting [8-10]. Flood forecasting is a [1] proposed that the topography, land use, component of flood warning, where the soil properties and the moisture status of the distinction between the two is that the study area could be coded as a hydrologic outcome of flood forecasting is a set of response unit named as the curve number, as forecast time-profiles of channel flows or referred to by U.S. Department of Agriculture river levels at various locations, while flood and Soil Conservation Service. warning refers to the task of making use of In Thailand, the Department of Water these forecasts to make decisions about Resources reported that Klong Luang sub- whether warnings of floods should be issued watershed covers an area of 1,897 square to the general public or whether previous kilometers which includes the entire districts warnings should be rescinded or retracted. of Phanat Nikhom and Ban Bueng, some In this study, we propose the streamflow parts of Phan Thong, Nong Yai, Bo Thong, network model based on multivariate regress- Ko Chan, and Muang Chonburi districts. This ion analysis for flood hazard planning and sub-watershed is the most important in simple flood forecasting method without a Chonburi Province, which is the development training process. The Geological survey has centre of the Eastern Seaboard of Thailand. collected streamflow data at Klong Luang The watershed drains from the South-West sub-watershed from 1965 to 1990. Flood Mountain in Bo Thong district and floods prevention can be successful by planning over Phan Thong district every year [2]. land use both in the watershed area and in the The flood disaster area covers 7 districts, flood area. Flood prospecting by proper 17 sub-districts, 66 villages, and a population model and input data may yield the flood of 41,807 persons in 14,075 households, with prone area due to different amounts of damage to 2,871 houses and 36.96 km2 rainfall. The proposed model is able to esti- (23,103 hectares) of agricultural land. It also mate the output magnitude of streamflow for causes major economic and social damage water resources management and minimizing and disruption to trade, industry and infra- flood areas in Klong Luang sub-watershed. structure across the eastern region of Thailand, especially in Mueang Chonburi, Phanat Nikhom, Methods Ban Bueng and Phan Thong districts. We propose a multiple linear regression The use of global ensemble streamflow (MLR) to model the relationship between the forecasting and flood early warning is explanatory and response variables. The MLR App. Envi. Res. 36(4) (2014): 53-65 55 uses several explanatory variables to forecast 2008. Data were analyzed using MATLAB the outcome of a response variable. The study’s R2010B. hypothesis is that the water level depends on Due to major economic and social damage the rainfall and streamflow variables [8]. to trade, industry and infrastructure in the Therefore, the general forecasting equation affected eastern region of Thailand, we focus based on MLR can be expressed as follows: mainly on four severely-impacted districts in the vicinity of Klong Luang (KL) KGT.19 station sub-watershed: Phanat Nikhom (PN), Mueang Chon Buri (MC), Ban Bueng (BB), for (1) and Phan Thong (PT) as shown in Figure 2. The latitude and longitude coordinates were where provided by the Royal Thai Survey Depart- ment and Meteorological Station Information is a output variable of responses (Chon Buri Province). is a design matrix of predictor variables Results and discussions The data are presented in Figure 3, while is vector or matrix of regression coefficients regression parameters of annual rainfall against annual streamflow at Klong Luang sub-water is a constant, with multivariate normal shed are tabulated in Tables 1 and 2. Based distribution on the results shown in Table 1, the highest Using the ordinary multivariate normal regression and best correlation coefficients maximum likelihood estimation [7], the regress- can be found at MC and PT, respectively. ion coefficients can readily be obtained. In These results shown that the water rainfall at this preliminary study, we examine the possi- MC, PN and PT can statistically forecast the ble MLR based model by using rainfall water streamflow at KL, whereas the results database as dependent variable. After analysis, from BB are less clearly correlated. The we focus on the average monthly streamflow positive regression coefficient indicates the volume in the enclosed drainage area. This increasing trend of rainfall. Since the data section presents the MLR based streamflow used in this study covered a relatively short and rainfall results. To show the results in period from 1965 to 1990, the effectiveness terms of the MLR model, the linear regress- of forecasting can be improved by using a ion line and its 95% confidence interval are larger data set covering a longer duration. In determined. This suggests the forecasted stream- order to apply the proposed model in practice, flow at KGT.19 station by the rainfall periods the 95% confidence interval was determined from 1965–1990. The general scheme of water as shown in Table 2. The equation (1) can be level forecasting model based on MLR model extended to provide a simplified MLR model is depicted in Figure 1. We collected the in this study, and can be expressed as follows following data: Topographic map from Royal (Equation 2): where ẞ is the regression coef- Thai Survey Department (RTSD, 1997) series ficient. We then applied equation (2) to forecast L 7018 scale 1:50,000 (sheet 5235I- IV 5236 streamflow over the period 1965 to 1990 II 5236III 5335III and 5335IV), and remote (m=26). It can be solved by the ordinary sensing data by LANDSAT-5 SATELLITE multivariate normal maximum likelihood estimation [7]. We used the meregress, the 56 App. Envi. Res. 36(4) (2014): 53-65 multivariate linear regression functions in their respective capabilities in forecasting MATLAB's Statistics Toolbox, function to streamflow, as shown in Figure. 6. We also determine coefficients for a multivariate normal found that expected values, µ, are similar for regression of the d-dimensional responses. both models (µ0=115.9619, µ=111.0704 (in The two optimal MLR based models for million cubic metre: mcm) for the proposed streamflow network have been found as MLR model and the original water stream- shown in Figure 4. All forecasted results flow, respectively). After excluding outliers, were determined using the 11 models, as the probability curve reverted to follow close shown in Table 3. After forecasting, we to the proposed MLR model. Therefore, the employed the MLR model in equation (2) to MLR model is identified as the most suitable show the different results predicted by the 11 and practical approach to preliminary models. Figure 5 shows the obtained results, investigation, presenting the fastest compu- including an outlier due to overshoot rainfall tation without requiring a training process. data in 1974. We applied the optimal MLR We applied the optimal model (Model 8) to models in Figure 4 to simulate forecasting forecast streamflow for the period 1991-2004.