AI Medical Compendium Journal:
Environmental monitoring and assessment

Showing 111 to 120 of 175 articles

Modeling monthly reference evapotranspiration process in Turkey: application of machine learning methods.

Environmental monitoring and assessment
In this study, the predictive power of three different machine learning (ML)-based approaches, namely, multi-gene genetic programming (MGGP), M5 model trees (M5Tree), and K-nearest neighbor algorithm (KNN), for long-term monthly reference evapotransp...

Data-driven predictive modeling of PM concentrations using machine learning and deep learning techniques: a case study of Delhi, India.

Environmental monitoring and assessment
The present study intends to use machine learning (ML) and deep learning (DL) models to forecast PM concentration at a location in Delhi. For this purpose, multi-layer feed-forward neural network (MLFFNN), support vector machine (SVM), random forest ...

Use of support vector machine and cellular automata methods to evaluate impact of irrigation project on LULC.

Environmental monitoring and assessment
Land use and land cover (LULC) both define the earth's surface both anthropogenically and naturally. It helps maintain global balance but changes in land use create inequality. The LULC modification adversely affects physical parameters such as infil...

Vegetation detection using vegetation indices algorithm supported by statistical machine learning.

Environmental monitoring and assessment
In precision agriculture (PA), the usage of image processing, artificial intelligence, data analysis, and internet of things provides an increase in efficiency, energy, and time saving. In image processing-based applications, vegetation detection, in...

Application of artificial intelligence models for prediction of groundwater level fluctuations: case study (Tehran-Karaj alluvial aquifer).

Environmental monitoring and assessment
The nonlinear groundwater level fluctuations depend on the interaction of many factors such as evapotranspiration, precipitation, groundwater abstraction, and hydrogeological characteristics, making groundwater level prediction a complex task. Ground...

MODWT-ANN hybrid models for daily precipitation estimates with time-delayed entries in Amazon region.

Environmental monitoring and assessment
Hydrological analyses based on precipitation records in the Amazon are essential due to their importance in climate regulation and regional and global atmospheric circulation. However, there are limitations related to data series with short periods a...

Determination of impervious area of Saroor Nagar Watershed of Telangana using spectral indices, MLC, and machine learning (SVM) techniques.

Environmental monitoring and assessment
Urbanization affects the local wind and water cycle by changing the natural surface and atmospheric conditions, which further changes the local climate and climate system. Assessment of built-up-area changes in a rapidly growing urban area within a s...

Modelling the reference crop evapotranspiration in the Beas-Sutlej basin (India): an artificial neural network approach based on different combinations of meteorological data.

Environmental monitoring and assessment
Accurate prediction of the reference evapotranspiration (ET) is vital for estimating the crop water requirements precisely. In this study, we developed multi-layer perceptron artificial neural network (MLP-ANN) models considering different combinatio...

Applicability of recurrent neural networks to retrieve missing runoff records: challenges and opportunities in Turkey.

Environmental monitoring and assessment
Acquiring river flow records is the primary precondition for providing optimal water resource management practices and preserving the ecohydrological balance. In Turkey, some river gauging stations go intermittently out of service due to some technic...

Deep learning approaches in remote sensing of soil organic carbon: a review of utility, challenges, and prospects.

Environmental monitoring and assessment
The use of neural network (NN) models for remote sensing (RS) retrieval of landscape biophysical and biochemical properties has become popular in the last decade. Recently, the emergence of "big data" that can be generated from remotely sensed data a...