Search
Search Results
-
Using Artificial Neural Networks and Spectral Indices to Predict Water Availability in New Capital (IKN) and Its’ Surroundings
This study aims to predict water availability in New Capital (IKN) and its surroundings using artificial neural networks and spectral indices as...
-
Evaluation of Feature Selection Methods in Estimation of Precipitation Based on Deep Learning Artificial Neural Networks
Precipitation is the most important element of the water cycle and an indispensable element of water resources management. This paper’s aim is to...
-
Remote sensing, artificial neural networks, and spatial interpolation methods for modelling soil chemical characteristics
The increase in global population, rapid urbanization, and continuous soil degradation have disrupted the balance between food demand and supply....
-
Combining artificial neural networks and genetic algorithms to model nitrate contamination in groundwater
Increasing the concentration of nitrates in aquifer systems reduces water quality and causes serious diseases and complications for human health....
-
Predicting monthly streamflow using artificial neural networks and wavelet neural networks models
Improving predicting methods for streamflow series is an important task for the water resource planning, management, and agriculture process. This...
-
Modeling with Artificial Neural Networks to estimate daily precipitation in the Brazilian Legal Amazon
Hydrological analyses carried out based on precipitation in the Brazilian Legal Amazon (BLA) are essential due to their importance in climate...
-
Prediction of Soil–Water Characteristic Curves in Bimodal Tropical Soils Using Artificial Neural Networks
Laborious and time-consuming tests are required for the determination of the soil–water characteristic curve (SWCC), often leading to the adoption of...
-
Application of Artificial Neural Networks for the Prediction of the Intensity of Ground Vibration at the Veliki Krivelj Copper Mine
AbstractThis article presents an artificial neural network (ANN)-based mathematical model for the prediction of the intensity of ground vibration at...
-
Predicting significant wave height with artificial neural networks in the South Atlantic Ocean: a hybrid approach
Accurate simulations of significant wave height (Hs) are extremely important for the safety of navigation, port operations, and oil and gas...
-
A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in the Ziveh Aquifer–West Azerbaijan, NW Iran
In many parts of the world, especially where surface water resources are rare or not available, groundwater as the largest source of freshwater is...
-
Estimating the joint shear strength of exterior beam–column joints using artificial neural networks via experimental results
Beam–column joints play an important role in resisting lateral loads induced by earthquakes. Previous post-earthquake reports have indicated that the...
-
The statistical analysis of training data representativeness for artificial neural networks: spatial distribution modelling of heavy metals in topsoil
A four-step dividing algorithm of sampling points for artificial neural networks is presented to select a representative training subset for...
-
Hydro-meteorological landslide triggering thresholds based on artificial neural networks using observed precipitation and ERA5-Land soil moisture
Landslide prediction is key for the development of early warning systems. In this work, we develop artificial neural networks (ANNs) that can...
-
Spatiotemporal Comparative Analysis of Dry/Wet Phenomenon of the Rainy Period Using Artificial Neural Networks and Markov Chains
The work presented in this paper is a spatiotemporal analysis of the dry/wet phenomenon of the rainy period in northern Algeria to predict the...
-
Comparison of Hydrological Modeling, Artificial Neural Networks and Multi-Criteria Decision Making Approaches for Determining Flood Source Areas
Flood risk management is a critical task which necessitates flood forecasting and identifying flood source areas for implementation of prevention...
-
A comprehensive review of seismic inversion based on neural networks
Seismic inversion is one of the fundamental techniques for solving geophysics problems. To obtain the elastic parameters or petrophysical parameters,...
-
Compressive strength prediction of ternary blended geopolymer concrete using artificial neural networks and support vector regression
The development of ternary blended geopolymers is one of the recent advancements in geopolymer concrete technology, which utilizes different source...
-
On water level forecasting using artificial neural networks: the case of the Río de la Plata Estuary, Argentina
The Río de la Plata Estuary (RdP) is frequently affected by large storm surges that have historically caused social and economic losses. According to...
-
Urban flood prediction using ensemble artificial neural network: an investigation on improving model uncertainty
Reducing the impact of artificial neural networks (ANN) affected by sources of uncertainty is crucial to improving the reliability of the flood...
-
Artificial neural networks for predicting soil water retention data of various Brazilian soils
Knowledge of the soil water retention (SWR) data is necessary for modeling soil water movement and assessing soil water holding capacity and...
