Estimation of Flow Discharge Model at Temef Watershed - East Nusa Tenggara Using TRMM Satellite Data

Aprianto Nomleni, E. Suhartanto, D. Harisuseno
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Abstract

Data collection based on satellite TRMM (Tropical Rainfall Measuring Mission) presents one of the good alternatives in estimating rainfall. TRMM technology can minimize manual rainfall recording errors and improve rainfall accuracy for hydrological analysis. The analysis method used in this research is divided into 3 (three) stages, namely Hydrology analysis, Statistical Analysis and Artificial Neural Network Analysis. From the results of TRMM JAXA analysis in the Temef Watershed Area of East Nusa Tenggara Province obtained TRMM JAXA satellite rainfall relationship to observation data shows rainfall patterns between the two data are interconnected but for cases with very high observation rainfall, TRMM rainfall data tends to be low. From statistical method analysis, the relationship between observation rainfall and TRMM JAXA rainfall obtained results with a "Very Strong" interpretation indicated by the results of 9 years calibration and 1 year validation where the selected equation is a polynomial equation (y=-0,0123x2 + 1,5553x + 20,222). Rain data correction results simulated with Debit data to see the relationship between rain and discharge that occurred, this analysis using Artificial Neural Network with Backpropagation method, the results showed a "Strong" interpretation where statistically the value of Nash-Sutcliffe Efficiency (NSE) 0.920, the coefficient value of correlation of field discharge and TRMM rainfall is 0,877 % and the relative error occurred is 2,62%
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利用TRMM卫星数据估算Temef流域-东努沙登加拉的流量模式
基于卫星热带降雨测量任务(TRMM)的数据采集是估算降雨量的一种较好的替代方法。TRMM技术可以最大限度地减少人工降雨记录的误差,提高水文分析的降雨精度。本研究采用的分析方法分为3个阶段,即水文分析、统计分析和人工神经网络分析。从TRMM JAXA在东努沙登加拉省Temef流域获得的TRMM JAXA卫星降雨与观测数据的关系分析结果来看,两者之间的降雨模式是相互关联的,但在观测降雨量非常高的情况下,TRMM降雨数据往往较低。通过统计方法分析,观测降雨量与TRMM JAXA降雨量之间的关系得到了“非常强”的解释结果,该结果由9年校准和1年验证的结果表明,所选方程为多项式方程(y=-0,0123x2 + 1,5553x + 20,222)。降雨数据的修正结果模拟了Debit数据,以了解降雨与发生的流量之间的关系,本文采用人工神经网络与反向传播方法进行分析,结果显示出“强”解释,统计上Nash-Sutcliffe效率(NSE)值为0.920,田间流量与TRMM降雨量的相关系数值为0,877%,相对误差为2,62%
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