Search
Search Results
-
Acoustic impedance prediction based on extended seismic attributes using multilayer perceptron, random forest, and extra tree regressor algorithms
Acoustic impedance is the product of the density of a material and the speed at which an acoustic wave travels through it. Understanding this...
-
Assessing landslide susceptibility based on hybrid multilayer perceptron with ensemble learning
Landslides have brought about serious human and economic losses worldwide. Modeling landslide susceptibility is an important technology to avoid the...
-
Improving PPP-RTK-based vehicle navigation in urban environments via multilayer perceptron-based NLOS signal detection
As the latest representative of GNSS positioning technology, the PPP-RTK method, which is able to achieve centimeter-level positioning using a single...
-
Using neural network modeling to improve the detection accuracy of land subsidence due to groundwater withdrawal
Despite the high efficiency of remote sensing methods for rapid and large-scale detection of subsidence phenomena, this technique has limitations...
-
Precise prediction of polar motion using sliding multilayer perceptron method combining singular spectrum analysis and autoregressive moving average model
The precise prediction of polar motion parameters is needed for the astrogeodynamics, navigation and positioning of the deep space probe. However,...
-
Random Forest and Multilayer Perceptron hybrid models integrated with the genetic algorithm for predicting pan evaporation of target site using a limited set of neighboring reference station data
This study explores the application of machine learning algorithms for the prediction of pan evaporation (Ep), which is a critical factor in water...
-
Estimation of the Standardized Precipitation Evapotranspiration Index (SPEI) Using a Multilayer Perceptron Artificial Neural Network Model for Central India
This study presents an artificial neural network (ANN) approach to estimate the drought events in the Indian state of Madhya Pradesh, also known as...
-
Analyzing the effect size of urban growth driving factors: application of multilayer-perceptron Markov-chain model for the Riyadh city
This paper presents a predictive analysis of the urban growth in Riyadh city for the years 2030 and 2050. The Multi-Layer Perceptron Markov Chain...
-
Deep learning tool: reconstruction of long missing climate data based on spatio-temporal multilayer perceptron
Long-term monitoring of climate data is significant for gras** the law and development trend of climate change and guaranteeing food security....
-
Predicting liquefaction-induced lateral spreading by using the multigene genetic programming (MGGP), multilayer perceptron (MLP), and random forest (RF) techniques
Landslides refer to a wide range of processes that result in the downward and outward movement of slope-forming materials, which may spread....
-
Towards a better consideration of rainfall and hydrological spatial features by a deep neural network model to improve flash floods forecasting: case study on the Gardon basin, France
Flash floods frequently hit the Mediterranean regions and cause numerous fatalities and heavy damage. Their forecast is still a challenge because of...
-
Neural Network Analysis as a Base of the Future System of Water–Environmental Regulation
AbstractThe article considers neural-network methods and technologies, which are relatively new even for many researchers and experts, as applied to...
-
Multilayer perceptron and support vector machine trained with grey wolf optimiser for predicting floods in Barak river, India
Flood prediction is significant for decision makers to plan, design and manage water resource systems for its contribution to decreasing life and...
-
Improving rainfall forecast at the district scale over the eastern Indian region using deep neural network
Indian Summer Monsoon (ISM) rainfall is largely contributed by synoptic scale low-pressure systems over the Bay of Bengal and moves towards Indian...
-
A comparative study of the XGBoost ensemble learning and multilayer perceptron in mineral prospectivity modeling: a case study of the Torud-Chahshirin belt, NE Iran
Precisely selecting the exploration criteria and building robust machine-learning models are two critical issues for enhancing the efficiency of...
-
Cumulative oil production in flow unit-crossing wells estimated by multilayer perceptron networks
Knowing the ultimate oil production in wells is a crucial point for reservoir planning and management to anticipate value for money. Commercial...
-
Seasonal rainfall pattern using coupled neural network-wavelet technique of southern Uttarakhand, India
Hydrological data is crucial for accurate forecasting of precipitation which can be used for water resources planning and management. The purpose of...
-
Using the Methods of Neural Network Learning for Peak Water Level Prediction: A Case Study for the Rivers in the Dvina-Pechora Basin
AbstractThe paper examines the implementation of neural network methods for predicting peak water levels during the period of spring ice drift by the...
-
-
Precipitable water vapor fusion of MODIS and ERA5 based on convolutional neural network
Sensing precipitable water vapor (PWV) in the earth’s atmosphere is of significant importance for contributing to severe weather event monitoring and...
