Search Results - rainfall estimation ((methods algorithm) OR (means algorithm))

The use of radar-rainfall inputs for quantitative precipitation estimation (QPE) in Klang River Basin / Suzana Ramli by Ramli, Suzana

“…The average value for root mean square error (RMSE) also reduced from 89.90 to 20.30 while the bias denotes average error reduction from 3.20 to 1.22.The improved radar rainfall as quantitative precipitation estimation (QPE) has also been applied in the rainfall-runoff modeling with grid-based Soil Conservation Service Curve Number (SCS-CN) method and GIS utilization. …”
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High-Resolution Downscaling with Interpretable Relevant Vector Machine: Rainfall Prediction for Case Study in Selangor by Abdul Rashid, Raghdah Rasyidah, Shaharudin, Shazlyn Milleana, Sulaiman, Nurul Ainina Filza, Zainuddin, Nurul Hila, Mahdin, Hairulnizam, Mohd Najib, Summayah Aimi, Hidayat, Rahmat

“…We used Nash-Sutcliffe Efficiency (NSE) and Root Mean Square Error (RMSE) as evaluation metrics. This study concluded that Relevance Vector Machine (RVM) models are suitable for forecasting future rainfall since they can support large rainfall extremes and generate reliable daily rainfall estimates based on rainfall extremes. …”
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A comparative effectiveness of hierarchical and non-hierarchical regionalisation algorithms in regionalising the homogeneous rainfall regions by Chuan, Zun Liang, Wan Nur Syahidah, Wan Yusoff, Azlyna, Senawi, Mohd Akramin, Mohd Romlay, Fam, Soo-Fen, Wendy Ling, Shinyie, Tan Lit, Ken

“…The results of the analysis show that Forgy K-means non-hierarchical (FKNH), Hartigan- Wong K-means non-hierarchical (HKNH), and Lloyd K-means non-hierarchical (LKNH) regionalisation algorithms are superior to other automated agglomerative hierarchical and non-hierarchical regionalisation algorithms. …”
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A comparative effectiveness of hierarchical and nonhierarchical regionalisation algorithms in regionalising the homogeneous rainfall regions by Zun, Liang Chuan, Wan Yusof, Wan Nur Syahidah, Senawi, Azlyna, Mohd Akramin, Mohd Romlay, Soo, Fen Fam, Wendy, Ling Shinyie, Tan, Lit Ken

“…The results of the analysis show that Forgy K-means non-hierarchical (FKNH), HartiganWong K-means non-hierarchical (HKNH), and Lloyd K-means non-hierarchical (LKNH) regionalisation algorithms are superior to other automated agglomerative hierarchical and non-hierarchical regionalisation algorithms. …”
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A comparative effectiveness of hierarchical and non-hierarchical regionalisation algorithms in regionalising the homogeneous rainfall regions by Zun, Liang Chuan, Fam, Soo Fen, Wan Yusof, Wan Nur Syahidah, Senawi, Azlyna, Mohd Akramin, Mohd Romlay, Wendy, Ling Shinyie, Tan, Lit Ken

“…The results of the analysis show that Forgy K-means non-hierarchical (FKNH), Hartigan-Wong K-means non-hierarchical (HKNH), and Lloyd K-means non-hierarchical (LKNH) regionalisation algorithms are superior to other automated agglomerative hierarchical and non-hierarchical regionalisation algorithms. …”
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A comparative effectiveness of hierarchical and nonhierarchical regionalisation algorithms in regionalising the homogeneous rainfall regions by Chuan, Zun Liang, Wan Nur Syahidah, Wan Yusoff, Azlyna, Senawi, Mohd Akramin, Mohd Romlay, Fam, Soo-Fen, Shinyie, Wendy Ling, Ken, Tan Lit

“…The results of the analysis show that Forgy K-means non-hierarchical (FKNH), HartiganWong K-means non-hierarchical (HKNH), and Lloyd K-means non-hierarchical (LKNH) regionalisation algorithms are superior to other automated agglomerative hierarchical and non-hierarchical regionalisation algorithms. …”
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TRMM Satellite Algorithm Estimates to Represent the Spatial Distribution of Rainstorms by Marina, Patrick, Mah, Yau Seng, Putuhena, Frederik Josep, Wang, Yin Chai, Onni Suhaiza, Selaman

“…These findings suggest that satellite algorithm estimations from TRMM are suitable to represent the spatial distribution of extreme rainfall.…”
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River flow prediction based on improved machine learning method: Cuckoo Search-Artificial Neural Network by Zanial W.N.C.W., Malek M.B.A., Reba M.N.M., Zaini N., Ahmed A.N., Sherif M., Elshafie A.

“…Therefore, it is necessary to precisely estimate how the river flow will alter as a result of changing rainfall patterns. …”

Satellite based quantitative rainfall estimation for flash flood forecasting / Wardah Tahir by Tahir, Wardah

“…In this study, a rainfall estimation algorithm using the information from the geostationary meteorological satellite infrared (IR) images is developed for potential input to a flood forecasting system. …”
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Machine learning techniques for reference evapotranspiration and rice irrigation requirements prediction: a case study of Kerian irrigation scheme, Malaysia by Mohd Nasir, Muhammad Adib, Harun, Sobri, Zainuddin, Zaitul Marlizawati, Kamal, Md Rowshon, Che Rose, Farid Zamani

“…ETo and rice irrigation requirements were first estimated using FAO Penman–Monteith (FAO-PM56) and the water balance model, respectively, and the obtained results were used as reference values in the machine learning algorithms. …”
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Investigating the relationship between the urban heat island effect and short-duration extreme rainfall in Kuala Lumpur by Tan, Yan Kai

“…However, the mean intensity of extreme rainfall events remained relatively stable. …”
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Performance evaluation and error decomposition of CMORPH satellite precipitation estimation for Klang Valley, Malaysia by Chai Voon Hao, Ren Jie Chin, L Ling, Y F Huang, Eugene Zhen Xiang Soo

“…Satellite Precipitation Estimations (SPEs) have gained traction as a viable substitute for estimating urban rainfall. …”
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Optimization of hydropower reservoir system using genetic algorithm for various climatic scenarios by Tayebiyan, Aida

“…The first step, ANN was calibrated and validated by using daily observed evapotranspiration, rainfall, and stream flow (2003-2012). In order to estimate daily evapotranspiration, daily observed Min and Max temperature was used in the estimation based on Hargreaves-Samani equation. …”
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Prediction of Temerloh River water level for prediction of flood using Artificial Neural Network (ANN) method by Muhamad Afiq, Mustafa

“…The research will be trained using back propagation method to estimate the flood water level at Temerloh River. …”
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Real-Time Flood Inundation Map Generation Using Decision Tree Machine Learning Method: Case Study of Kelantan River Basins by Sidek L.M., Basri H., Marufuzzaman M., Deros A.M., Osman S., Hassan F.A.

“…Therefore, flash flood and short-term flood prediction require numerical rainfall estimation, which employs falls, mudflow, melted ice, etc. …”

An improved streamflow model with climate and land use factors for Hulu Langat Basin by Falamarzi, Yashar

“…Thus, in the present study, to achieve the objectives, first, the James W. Kirchner (JWK) method was modified and the modified model (MJWK) was then combined with the Soil Conservation Service (SCS) effective rainfall estimation method (MJWK-SCS model) to estimate river flow. …”
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Estimation Of Time Of Concentration Using Triangulated Irregular Network Method by Ewe, Teik Tsia

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Suspended sediment load prediction using artificial neural network and ant lion optimization algorithm by Banadkooki F.B., Ehteram M., Ahmed A.N., Teo F.Y., Ebrahimi M., Fai C.M., Huang Y.F., El-Shafie A.

Flood damage cost prediction using random forest / Ainul Najwa Azahari and Norlina Mohd Sabri by Azahari, Ainul Najwa, Mohd Sabri, Norlina

“…The research used the rainfall and streamflow data from the year 2012 to 2022 as attributes to forecast the cost of the JPS structures damages in Terengganu. …”
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Precipitation trend analysis for The Langat River Basin, Selangor, Malaysia by Palizdan, Narges

“…Next, the homogeneous regions were then formed using the K-mean Clustering method. The applied homogenous region analysis method in the present study is a new approach. …”
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