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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. …”

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.

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.

Precipitation forecasting using multilayer neuralNetwork and support vector machine optimization based on flow regime algorithm taking intoAccount uncertainties of soft computing m... by Banadkooki F.B., Ehteram M., Ahmed A.N., Fai C.M., Afan H.A., Ridwam W.M., Sefelnasr A., 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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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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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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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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Comparative Assessment of Improved SVM Method under Different Kernel Functions for Predicting Multi-scale Drought Index by Pande C.B., Kushwaha N.L., Orimoloye I.R., Kumar R., Abdo H.G., Tolche A.D., Elbeltagi A.

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. …”

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 the literature, mostly statistical and machine learning methods are used individually; however, their integration can enhance the final output. …”
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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. …”

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

On the training sample size and classification performance: An experimental evaluation in seismic facies classification by Babikir, I., Elsaadany, M., Sajid, M., Laudon, C.

“…Machine learning algorithms (MLAs) perform better when enough high-quality training data is provided. …”
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Remote sensing technologies for unlocking new groundwater insights: a comprehensive review by Ibrahim, Abba, Wayayok, Aimrun, Mohd Shafri, Helmi Zulhaidi, Toridi, Noorellimia Mat

“…The evolution of the techniques analyses spans from empirical reliance on sparse point data to the assimilation of multi-platform satellite measurements using sophisticated machine learning algorithms. …”
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Applications of Data-driven Models for Daily Discharge Estimation Based on Different Input Combinations by Kumar M., Elbeltagi A., Pande C.B., Ahmed A.N., Chow M.F., Pham Q.B., Kumari A., Kumar D.