Search Results - rainfall evaluation method algorithm

Rainfall prediction system using Artificial Neural Network (ANN) / Izzat Izzuddin Zulkarnain by Zulkarnain, Izzat Izzuddin

“…The objectives of the project were to study the ANN algorithm for rainfall prediction, gather relevant data for training the model, and implement and evaluate the performance of the developed system. …”
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Rainfall forecasting model using machine learning methods: Case study Terengganu, Malaysia by Ridwan W.M., Sapitang M., Aziz A., Kushiar K.F., Ahmed A.N., El-Shafie A.

A hybrid climate model for rainfall forecasting based on combination of self-organizing map and analog method by Natita Wangsoh, Wiboonsak Watthayu, Dusadee Sukawat

“…To improve capability of rainfall forecasting, three different measures were evaluated. …”
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Assessing rainfall prediction models: Exploring the advantages of machine learning and remote sensing approaches by Latif S.D., Alyaa Binti Hazrin N., Hoon Koo C., Lin Ng J., Chaplot B., Feng Huang Y., El-Shafie A., Najah Ahmed A.

“…Satellites and radar are used to gather regional precipitation data for hybrid models. An algorithm trained on historical rainfall measurements would then process the data. …”

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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Reservoir system modelling using nondominated sorting genetic algorithm in the framework of climate change by Nurul Nadrah Aqilah, Tukimat

“…In conclusion, this finding contributes toward the development of models using evolution algorithm and statistical methods for sustainable water resources planning and management in the context of future climate change…”
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Prediction of Oil Palm Yield Using Machine Learning in the Perspective of Fluctuating Weather and Soil Moisture Conditions: Evaluation of a Generic Workflow by Khan N., Kamaruddin M.A., Ullah Sheikh U., Zawawi M.H., Yusup Y., Bakht M.P., Mohamed Noor N.

“…The performance of two top regression models, namely Extra Tree and AdaBoost was evaluated using six statistical evaluation metrics. …”

Modeling time series data using Genetic Algorithm based on Backpropagation Neural network by Haviluddin

“…This research utilizes a genetic algorithm (GA) to optimize the multi-layer FFNN performance and structure in modelling three datasets: network traffic, rainfall, and tourist. …”
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Leachate generation rate modeling using artificial intelligence algorithms aided by input optimization method for an MSW landfill by Abunama, Taher, Othman, Faridah, Ansari, Mozafar, El-Shafie, Ahmed

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Optimizing dam and reservoir operations using machine learning by Mohammad Abdullah Adib Almubaidin

Bat algorithm and neural network for monthly streamflow prediction by Zaini N., Malek M.A., Yusoff M., Osmi S.F.C., Mardi N.H., Norhisham S.

“…The applications of artificial intelligence (AI) have been proved to have better performance as compared to conventional statistical method in streamflow prediction. Therefore, this study proposed on the development of streamflow prediction model AI techniques namely Bat algorithm (BA) and backpropagation neural network (BPNN). …”

Drought-events and potential impacts on land use/land cover dynamics based on AVHRR data of 1992-2003 for central Iran by Mokhtari, Ahmad

“…At the first phase, primarily, Standard Precipitation Index (SPI) computed for 1970-2003 rainfall records of weather gauge stations. Then, the methods with the best performances depicted to produce the spatio-temporal SPI drought index layer. …”
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Data-driven rice yield predictions and prescriptive analytics for sustainable agriculture in Malaysia by Marong, Muhammad, Husin, Nor Azura, Zolkepli, Maslina, Affendey, Lilly Suriani

“…Utilizing machine learning algorithms as decision-support tools, the study analyses commonly used models—Linear Regression, Support Vector Machines, Random Forest, and Artificial Neural Networks—alongside key environmental factors such as temperature, rainfall, and historical yield data. …”
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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. Two machine learning algorithms, named Support Vector Regression (SVR) and Random Forest (RF), were applied to predict ETo and rice irrigation requirements using only climatic data (rainfall, temperature, relative humidity, and wind speed). …”
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Flood susceptibility analysis and its verification using a novel ensemble support vector machine and frequency ratio method by Tehrany, Mahyat Shafapour, Pradhan, Biswajeet, Jebur, Mustafa Neamah

“…In order to examine the efficiency of the proposed ensemble method and to show the proficiency of SVM, another machine learning algorithm such as decision tree (DT) was applied and the results were compared. …”
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Integrated geophysical, hydrogeochemical and artificial intelligence techniques for groundwater study in the Langat Basin, Malaysia / Mahmoud Khaki by Mahmoud, Khaki

“…These results confirm that, for all the networks the Levenberg-Marquardt algorithm is the most effective algorithm to model the groundwater level. …”
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Prediction of meteorological drought and standardized precipitation index based on the random forest (RF), random tree (RT), and Gaussian process regression (GPR) models by Elbeltagi A., Pande C.B., Kumar M., Tolche A.D., Singh S.K., Kumar A., Vishwakarma D.K.

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

“…In the performance evaluation, the model with 80:20 training and testing data ratio produced the best result in predicting the flood damage cost. …”
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Evaluating the vertical accuracy of lidar and open source dem for oil palm plantation planning and design by Alfred Michael Sikab, Wong, Wilson Vun Chiong

“…This study proposes a hybrid correction framework that integrates a random forest (RF) machine learning algorithm and a geographically weighted regression (GWR) a spatially adaptive statistical method to enhance the vertical accuracy of open-source DEMs for terrain-sensitive applications. …”
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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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