AI Medical Compendium Journal:
Environmental monitoring and assessment

Showing 121 to 130 of 175 articles

An efficient strategy for predicting river dissolved oxygen concentration: application of deep recurrent neural network model.

Environmental monitoring and assessment
Dissolved oxygen (DO) concentration in water is one of the key parameters for assessing river water quality. Artificial intelligence (AI) methods have previously proved to be accurate tools for DO concentration prediction. This study presents the imp...

Identifying of Quercus vulcanica and Q. frainetto growing in different environments through deep learning analysis.

Environmental monitoring and assessment
Quercus is one of the important elements of forests worldwide. But the diagnosis of the species in this genus in particular using leaves is pretty challenging due to the presence of natural hybrids and phenotypically plastic trait expression. In this...

Prediction of soil-bearing capacity on forest roads by statistical approaches.

Environmental monitoring and assessment
The soil-bearing capacity is one of the important criteria in dimensioning the superstructure. In Turkey, predictability of California Bearing Ratio values, which may be used in the planning and dimensioning of forest roads, of which about 26% lacks ...

A water quality prediction model based on variational mode decomposition and the least squares support vector machine optimized by the sparrow search algorithm (VMD-SSA-LSSVM) of the Yangtze River, China.

Environmental monitoring and assessment
Accurate and reliable water quality forecasting is of great significance for water resource optimization and management. This study focuses on the prediction of water quality parameters such as the dissolved oxygen (DO) in a river system. The accurac...

Estimating evapotranspiration by coupling Bayesian model averaging methods with machine learning algorithms.

Environmental monitoring and assessment
Evapotranspiration (ET) is one of the most important components of global hydrologic cycle and has significant impacts on energy exchange and climate change. Numerous models have been developed to estimate ET so far; however, great uncertainties in m...

Prediction of the outlet flow temperature in a flat plate solar collector using artificial neural network.

Environmental monitoring and assessment
In the current research, the efficiency of a solar flat plate collector (SFPC) was examined experimentally, while the system was modeled with an artificial neural network (ANN) under semi-arid weather conditions of Rafsanjan, Iran. Based on the backp...

Deep learning for automated analysis of fish abundance: the benefits of training across multiple habitats.

Environmental monitoring and assessment
Environmental monitoring guides conservation and is particularly important for aquatic habitats which are heavily impacted by human activities. Underwater cameras and uncrewed devices monitor aquatic wildlife, but manual processing of footage is a si...

Estimation and easy calculation of the Palmer Drought Severity Index from the meteorological data by using the advanced machine learning algorithms.

Environmental monitoring and assessment
Drought, which has become one of the most severe environmental problems worldwide, has serious impacts on ecological, economic, and socially sustainable development. The drought monitoring process is essential in the management of drought risks, and ...

Multitemporal time series analysis using machine learning models for ground deformation in the Erhai region, China.

Environmental monitoring and assessment
Ground deformation (GD) has been widely reported as a global issue and is now an ongoing problem that will profoundly endanger the public safety. GD is a complex and dynamic problem with many contributing factors that occur over time. In the literatu...

Comparative study of artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS) and multiple linear regression (MLR) for modeling of Cu (II) adsorption from aqueous solution using biochar derived from rambutan (Nephelium lappaceum) peel.

Environmental monitoring and assessment
Presence of copper within water bodies deteriorates human health and degrades natural environment. This heavy metal in water is treated using a promising biochar derived from rambutan (Nephelium lappaceum) peel through slow pyrolysis. This research c...