Assessment of empirical and regression methods for infilling missing streamflow data in Little Ruaha catchment Tanzania

Kamwaga, Stanislaus; Mulungu, Deogratias M.M.; Valimba, Patrick

doi:10.1016/j.pce.2018.05.008

Cited by 12 publications

(8 citation statements)

References 17 publications

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“…Hal ini berdasarkan sebuah peristiwa atau kejadian sebelum dataset tersedia dengan melakukan asumsi bahwa data adalah satu seri catatan yang berkelanjutan. Cara selanjutnya adalah menghapus waktu hilangnya data dan menganggap data yang tersisa sebagai kumpulan data berkelanjutan [1].…”

Section: Imputasi Nilai Hilangunclassified

“…PENDAHULUAN Air adalah sumber daya alam yang hadir dalam berbagai bentuk, seperti sungai, sumur, danau dan waduk. Pengembangan sumber daya untuk berbagai keperluan, termasuk konsep hidrologi, sumber daya air, dan penanggulangan banjir, merupakan dasar bagi pembangunan sosial ekonomi masyarakat [1]. Dampak perubahan iklim juga menjadi faktor pendukung perubahan dinamis air dalam wujud benda, mulai dari bentuk, warna, debit, dan bau.…”

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“…Secara tradisional, sistem pemodelan simulasi hidrologi diklasifikasikan menjadi tiga kelompok utama, yaitu black box empiris, konseptual terpusat, dan model berbasis fisik terdistribusi. Secara umum, konsep tradisional kurang memiliki akurasi dalam sampel data yang akan dianalisis [1]. Masalah paling umum dalam dataset nyata dan analisis statistik adalah nilai yang hilang, dengan persentase nilai yang hilang bervariasi dari satu kumpulan data ke kumpulan data lainnya.…”

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Metode Imputasi pada Data Debit Daerah Aliran Sungai Opak, Provinsi DI Yogyakarta

Irnawan

Hidayah

Nugroho

2021

JNTETI

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Ketersediaan data sumber daya air di Indonesia memiliki beberapa permasalahan yang kompleks terkait dengan kesempurnaan data. Permasalahan yang terjadi saat pendataan di beberapa instansi di Indonesia adalah kurangnya keakuratan dan kelengkapan data. Terdapat beberapa metode yang dapat digunakan untuk imputasi nilai hilang, misalnya k-Nearest Neighbors Imputation (k-NNi) dan Multivariate Imputation by Chained Equation (MICE). Tujuan makalah ini adalah membandingkan dan menemukan metode yang paling tepat dalam menggunakan dataset DAS Opak di Provinsi DIY. Karakteristik DAS Opak adalah berbentuk kipas atau melebar sehingga memberikan waktu konsentrasi yang lebih rendah dan menghasilkan aliran yang lebih tinggi. Hasil perbandingan validasi statistik, nilai rata-rata RMSE dan MAE, yang paling konsisten adalah metode k-NNi dengan nilai k = 28, sedangkan untuk perbandingan nilai R-Squared, metode k-NNi dengan nilai k = 28 mendapatkan nilai rata-rata terbaik sebesar 80%, disusul metode k-NNi sebesar k = 7 sebagai nilai k default dengan persentase 73%. Metode perbandingan MICE mendapatkan nilai persentase rata-rata terendah dari metode lainnya, yaitu sebesar 63%.

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Section: Imputasi Nilai Hilangunclassified

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Metode Imputasi pada Data Debit Daerah Aliran Sungai Opak, Provinsi DI Yogyakarta

Irnawan

Hidayah

Nugroho

2021

JNTETI

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show abstract

“…They developed the estimation model based on the correlations between the stations and the periodic behavior of data in each station. Kamwaga et al (2018) completed the missing data in 19-year time-series flow data by using experimental, univariate, and multivariate regression methods in a basin in Tanzania. In Tanzania basins, missing data is a common problem inflow data sets.…”

Section: Introductionmentioning

confidence: 99%

Potential of kernel and tree-based machine-learning models for estimating missing data of rainfall

Sattari

Falsafian

Irvem

et al. 2020

Engineering Applications of Computational Fluid Mechanics

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In this study, two kernel-based models were used which include Support Vector Regression (SVR) and Gaussian Process Regression (GPR) and were compared with two tree-based models that are M5 and Random Forest (RF) for estimating missing monthly precipitation data in Antakya, Dortyol, Iskenderun and Samandag stations, which are the important precipitation stations in the Eastern Mediterranean region, Turkey. For this purpose, firstly 10% random precipitation data were assumed as missing data for the period 1980-2019. Secondly, the missing data in each station was estimated with the data of other stations within the framework of four data combinations scenarios. In Kernelbased SVR and GPR methods, the RBF kernel gave suitable results for the selected study area. While SVR and RF methods gave very close estimation results, the SVR method gave relatively better results than the other methods especially in error minimizing aspects. Gaussian function based GPR model generally tries to estimate missing data closer to means. This is the main disadvantage of the GPR model and therefore it is unsuccessful in the estimation process. Finally, the results showed that the algorithms based on machine learning are successful in estimating the missing precipitation data.

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“…Background Hydrological studies have been determined by many scholars to be heavily dependent on quantitative data, the quality of these data and how the referred variable behaves over a long period of time (Valimba et al, 2006;Yaseen et al, 2019). Some scholars e.g., (Clark, Kavetski and Fenicia, 2011;Kamwaga, Mulungu and Valimba, 2018;Cudennec et al, 2020) pointed out that data issues will always remain to be a core component of the hydrological sciences. Their character and in uence on the way the discipline is practiced may vary through time, but their intrinsic role in understanding and managing water resources and hazards, as well as in developing sound water policies dictates their continuing importance (Cudennec et al, 2020).…”

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confidence: 99%

Analysis of Spatial and Temporal Trend for Hydro-climatic Parameters in the Kilombero River Catchment, Tanzania

Sigalla¹,

Valimba

Selemani

et al. 2023

Preprint

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Perceived and real-life water availability have raised social and economic conflicts that necessitate proper water management. This requires an understanding of spatial-temporal trends of hydro-climatic variables as the main contributor to available water for use by sectors of economy. In this study, we have analyzed the trend of hydro-climatic variables viz. precipitation, temperature, evapotranspiration and river discharge. One downstream river gauge station was used for discharge data whereas a total of 9 daily observed and 29 grided satellite stations were used for climate data. Climate Hazards Group InfraRed Precipitation was used for precipitation data and Observational-Reanalysis Hybrid was used for Temperature data. Mann-Kendall Statistical test, Sen’s slope estimator and ArcMap Inverse Distance Weighted Interpolation functionality were employed for temporal, magnitude and spatial trend analysis respectively. Results show that on the latter, there are three main climatic zones in the study area viz. Udzungwa escarpment, Kilombero valley and Mahenge escarpment. On the former, with exception of the declining potential evapotranspiration trend, all other variables are on increase. This is with catchment rates of 2.08 mm/y, 0.05 oC/y, 0.02 oC/y, 498.6 m3/s/y and − 2.27 mm/y for precipitation, Tmax, Tmin, river discharge and PET respectively. Furthermore, there is a synonymous seasonality pattern for all variables with significant range, especially in river discharge. However, it is recommended to improve water resources management practices to limit flow impairment as expansions in sectors of economy are expected. Furthermore, landuse change analysis is recommended to ascertain actual trend and hence future water uptake.

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Assessment of empirical and regression methods for infilling missing streamflow data in Little Ruaha catchment Tanzania

Cited by 12 publications

References 17 publications

Metode Imputasi pada Data Debit Daerah Aliran Sungai Opak, Provinsi DI Yogyakarta

Metode Imputasi pada Data Debit Daerah Aliran Sungai Opak, Provinsi DI Yogyakarta

Potential of kernel and tree-based machine-learning models for estimating missing data of rainfall

Analysis of Spatial and Temporal Trend for Hydro-climatic Parameters in the Kilombero River Catchment, Tanzania

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