Search Results - (( java implication based algorithm ) OR ( using basin learning algorithm ))

Characterization of water quality conditions in the Klang River Basin, Malaysia using self organizing map and K-means algorithm by Mohd Sharif, Sharifah, Mohd Kusin, Faradiella, Asha’ari, Zulfa Hanan, Aris, Ahmad Zaharin

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Flood mapping based on novel ensemble modeling involving the deep learning, Harris Hawk optimization algorithm and stacking based machine learning by Costache R., Pal S.C., Pande C.B., Islam A.R.M.T., Alshehri F., Abdo H.G.

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Developing an ensembled machine learning model for predicting water quality index in Johor River Basin by Sidek L.M., Mohiyaden H.A., Marufuzzaman M., Noh N.S.M., Heddam S., Ehteram M., Kisi O., Sammen S.S.

“…Finally, an ensemble-based machine learning model is designed to predict the WQI using three parameters. …”

Modelling hourly runoff using ann for sg. Sarawak Kanan Basin by Chong, Kah Weng.

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A New Robust Weak Supervision Deep Learning Approach for Reservoir Properties Prediction in Malaysian Basin Field by Ahmad Fuad, M.I., Hermana, M., Jaya, M.S., Ishak, M.A.

“…The conventional seismic inversion approach is practical for operational work, as it only uses simple linearized algorithms and assumptions, but may be less applicable when dealing with a complex geological setting, especially in the Malay basin fields, as it may introduce non-linear noises and non-unique solutions. …”
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Integrated geophysical, hydrogeochemical and artificial intelligence techniques for groundwater study in the Langat Basin, Malaysia / Mahmoud Khaki by Mahmoud, Khaki

“…Resistivity surveys and geochemical analyses were used to delineate regions of Langat Basin that are contaminated by brackish water. …”
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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.

Developing flood mapping procedure through optimized machine learning techniques. Case study: Prahova river basin, Romania by Diaconu D.C., Costache R., Towfiqul Islam A.R.M., Pandey M., Pal S.C., Mishra A.P., Pande C.B.

“…Study focus: This study aims to assess the susceptibility to flooding by using state-of-the-art machine learning and optimization procedures. …”

The potential of a novel support vector machine trained with modified mayfly optimization algorithm for streamflow prediction by Adnan R.M., Kisi O., Mostafa R.R., Ahmed A.N., El-Shafie A.

The implications for ahybrid detection technique against malicious sqlattacks on web applications by Bahjat Arif, Sarajaldeen Akram, Wani, Sharyar

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Evaluation of principal component analysis for reducing seismic attributes dimensions: Implication for supervised seismic facies classification of a fluvial reservoir from the Mala... by Babikir, I., Elsaadany, M., Sajid, M., Laudon, C.

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Estimating the un-sampled ph value via neighbouring points using multi-layer neural network - genetic algorithm by Muhammad Aznil, Ab Aziz, Mohammad Fadhil, Abas, Muhamad Abdul Hasib, Ali, Norhafidzah, Mohd Saad, Mohd Hisyam, Ariff, Mohamad Khairul Anwar, Abu Bashrin

“…GA optimizer is used in MLNN-GA where the result of each learning weight will be the initial weight of the next learning process. …”
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Water level predictio for Limbang basin using multilayer perceptron (mlp) and radial basis function (rbf) neural network by Muhammad Noor Hisyam, Abg Hashim

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Machine-learning guided fracture density seismic inversion: A new approach in fractured basement characterisation by Shamsuddin, A.A.S., Purnomo, E.W., Ghosh, D.P.

“…The main objective of this study is to map potential fracture density based on a new integrated study of a fractured basement area. A machine learning algorithm of well log fracture density - borehole image log (BHI) guided seismic inversion was performed. …”
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Optimized conditioning factors using machine learning techniques for groundwater potential mapping by Kalantar, Bahareh, Al-Najjar, Husam A. H., Pradhan, Biswajeet, Saeidi, Vahideh, Abdul Halin, Alfian, Ueda, Naonori, Naghibi, Seyed Amir

“…In addition, 917 spring locations were identified and used to train and test three machine learning algorithms, namely Mixture Discriminant Analysis (MDA), Linear Discriminant Analysis (LDA) and Random Forest (RF). …”
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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.

“…Due to limited historical data for drought monitoring and forecasting available in the central India of Maharashtra state, implementing machine learning (ML) algorithms could allow for the prediction of future drought events. …”

Digital Quran With Storage Optimization Through Duplication Handling And Compressed Sparse Matrix Method by Ashraf Saleh Mohammad Alomoush

Hourly water level prediction of sungai bedup catchement using pre-developed ANNs model from Siniawan catchment by Hong,, Calvin Chiao Chun

“…The back propagation algorithm was adopted for this study. The models used in this study is the network trained with scaled conjugate gradient algorithm (trainscg) with two hours of antecedent data, learning rate and the number of neurons in the hidden layer of 0.8 and 40 respectively. …”
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Artificial neural networks (ANNS) for daily rainfall runoff modelling by Kuok, King Kuok, Nabil, Bessaih

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Forecasting of meteorological drought using ensemble and machine learning models by Pande C.B., Sidek L.M., Varade A.M., Elkhrachy I., Radwan N., Tolche A.D., Elbeltagi A.

“…Therefore, the Matern GPR model was identified as the finest ML algorithm for predicting SPI-3 and SPI-6 associated with other algorithms. …”