Search Results - (( developing effective rainfall algorithm ) OR ( java implication based algorithm ))

Rainfall time series modeling for a mountainous region in West Iran by Mekanik, Fatemeh

“…A feedforward Artificial Neural Network (ANN) rainfall model and a Seasonal Autoregressive Integrated Moving Average (SARIMA) rainfall model were developed to investigate their potentials in forecasting rainfall. …”
Get full text
Get full text

Daily Rainfall Forecasting Using Meteorology Data with Long Short-Term Memory (LSTM) Network by Soo See, Chai, Goh, Kok Luong

“…For time series data forecasting, the Long Short-Term Memory (LSTM) network is shown to be superior as compared to other machine learning algorithms. Therefore, in this research work, a LSTM network is developed to predict daily average rainfall values using meteorological data obtained from the Malaysian Meteorological Department for Kuching, Sarawak, Malaysia. …”
Get full text
Get full text

Optimization of hydropower reservoir system using genetic algorithm for various climatic scenarios by Tayebiyan, Aida

“…The increase in temperature could influence time and magnitude of rainfall by shifting dry and wet seasons. Moreover, the output results indicate a decrease in monthly rainfall. …”
Get full text
Get full text
Get full text

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

“…These factors include identifying relevant atmospheric features contributing to rainfall, addressing missing data, and developing a significant model to predict daily rainfall intensity using appropriate machine-learning techniques. …”
Get full text
Get full text

Investigation of Multimodel Ensemble Performance Using Machine Learning Method for Operational Dam Safety by Basri H., Marufuzzaman M., Mohd Sidek L., Ismail N.

“…Hence, consideration of the development of more flexible inflow forecasting systems is needed. …”

Rainfall modeling using two different neural networks improved by metaheuristic algorithms by Sammen S.S., Kisi O., Ehteram M., El-Shafie A., Al-Ansari N., Ghorbani M.A., Bhat S.A., Ahmed A.N., Shahid S.

“…Rainfall is crucial for the development and management of water resources. …”

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

Get full text
Get full text
Get full text

Early prediction of dengue outbreak using Artificial Neural Network (ANN) / Muhammad Sirajuddin Ismail by Ismail, Muhammad Sirajuddin

“…This study aims to investigate the requirements of utilizing the Artificial Neural Network algorithm for prediction of dengue outbreak. The objective is to develop Dengue Outbreak Prediction System using Artificial Neural Network algorithm and evaluate its performance. …”
Get full text
Get full text

Performance Measurement on Deep Spiking Neural Network (DSNN) Algorithm in Flood Prediction Environment by Roselind, Tei

“…Rainfall data from 30 years (1989-2019) was collected from DID to evaluate the effectiveness of the DSNN algorithm compared to traditional and shallow neural networks algorithms. …”
Get full text
Get full text
Get full text
Get full text

Fine-scale predictive modeling of Aedes mosquito abundance and dengue risk indicators using machine learning algorithms with microclimatic variables by Che Dom, Nazri, Mohd Hardy Abdullah, Nur Athen, Dapari, Rahmat, Salleh, Siti Aekbal

“…However, current models often rely on coarse regional data and fail to account for microclimatic variations, limiting their predictive accuracy in dengue hotspots. This study developed fine-scale predictive models using machine learning algorithms; Artificial Neural Networks (ANN), Random Forest (RF), and Support Vector Machines (SVM) to estimate mosquito abundance and dengue risk at the species level based on daily microclimatic data (temperature, relative humidity, and rainfall) collected over 26 weeks in Kuala Selangor, Malaysia. …”
Get full text
Get full text
Get full text

Optimal allocation and sizing of capacitor bank and distributed generation using particle swarm optimization by El Tawil, Naji Ammar Mansour

“…The research work presented in this thesis had investigated the effect of two additional weather parameters, namely wind speed and rainfall, in addition to the temperature and relative humidity using artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) in predicting the values of load demands. …”
Get full text
Get full text
Get full text

Study of climate change effects on rain height for satellite microwave links by Yong, Xin Yu

“…Then, a simple AI predictive model for rain height is being developed by using Artificial Neural Network (ANN) algorithms with Python programming language. …”
Get full text
Get full text

Predictive modeling of land surface temperature (LST) based on Landsat-8 satellite data and machine learning models for sustainable development by Pande C.B., Egbueri J.C., Costache R., Sidek L.M., Wang Q., Alshehri F., Din N.M., Gautam V.K., Chandra Pal S.

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

Forecasting model for the change in stage of reservoir water level by Nur Athirah, Ashaary

“…During floods, early reservoir water release is one of the actions taken by the reservoir operator to accommodate incoming heavy rainfall. Late water release might give negative effect to the reservoir structure and cause flood at downstream area. …”
Get full text
Get full text
Get full text

Design of a hybrid ANN multi-objective whale algorithm for suspended sediment load prediction by Ehteram M., Ahmed A.N., Latif S.D., Huang Y.F., Alizamir M., Kisi O., Mert C., El-Shafie A.

Advanced data mining techniques for landslide susceptibility mapping by Ibrahim, M.B., Mustaffa, Z., Balogun, A.-L., Hamonangan Harahap, I.S., Ali Khan, M.

“…These predicted datasets were used to develop the Landslides Susceptibility Models. A comparative assessment between the two classifiers against the famous traditional learning algorithm, the Support vector machines (SVM), was conducted. …”
Get full text
Get full text

Flood prediction model for Kuala Terengganu area using predictive analytics by Mohd Zamri, Najwa An-Nisa

“…Three classification algorithms were tested: Decision Tree, Naive Bayes, and Random Forest. …”
Get full text
Get full text

Rain models for the prediction of fade duration at millimeter wavelengths by Othman, Mohd. Afzan

“…The planning of radio communications system requires an estimate of the average annual outage due to fading, which at millimeter wavelengths, is generally dominated by the effects of rain attenuation. Current ITU-R recommendations provide algorithm for estimating the exceedance static of rain-induced attenuation on terrestrial links. …”
Get full text
Get full text

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. …”
Get full text
Get full text