AI Medical Compendium Journal:
Environmental monitoring and assessment

Showing 91 to 100 of 175 articles

Applications of remote sensing vis-à-vis machine learning in air quality monitoring and modelling: a review.

Environmental monitoring and assessment
Environmental contamination especially air pollution is an exponentially growing menace requiring immediate attention, as it lingers on with the associated risks of health, economic and ecological crisis. The special focus of this study is on the adv...

Deep learning-based total suspended solids concentration classification of stream water surface images captured by mobile phone.

Environmental monitoring and assessment
The continuous monitoring of total suspended solids (TSS) in streams plays an important role in the management of hydrological processes, and TSS is also a decisive parameter in the control of pollution in streams. Determination of TSS involves both ...

Analysis of land use and land cover change using machine learning algorithm in Yola North Local Government Area of Adamawa State, Nigeria.

Environmental monitoring and assessment
The dynamic use of land that results from urbanization has an impact on the urban ecosystem. Yola North Local Government Area (Yola North LGA) of Adamawa state, Nigeria, has experienced tremendous changes in its land use and land cover (LULC) over th...

LULC change detection using support vector machines and cellular automata-based ANN models in Guna Tana watershed of Abay basin, Ethiopia.

Environmental monitoring and assessment
Recurrent changes recorded in LULC in Guna Tana watershed are a long-standing problem due to the increase in urbanization and agricultural lands. This research aims at identifying and predicting frequent changes observed using support vector machines...

Is deeper always better? Evaluating deep learning models for yield forecasting with small data.

Environmental monitoring and assessment
Predicting crop yields, and especially anomalously low yields, is of special importance for food insecure countries. In this study, we investigate a flexible deep learning approach to forecast crop yield at the provincial administrative level based o...

Air pollution forecasting based on wireless communications: review.

Environmental monitoring and assessment
The development of contemporary artificial intelligence (AI) methods such as artificial neural networks (ANNs) has given researchers around the world new opportunities to address climate change and air quality issues. The small size, low cost, and lo...

Application of empirical mode decomposition, particle swarm optimization, and support vector machine methods to predict stream flows.

Environmental monitoring and assessment
Modeling stream flows is vital for water resource planning and flood and drought management. In this study, the performance of hybrid models constructed by combining least square support vector machines (LSSVM), empirical model decomposition (EMD), a...

Rainfall-Runoff modelling using SWAT and eight artificial intelligence models in the Murredu Watershed, India.

Environmental monitoring and assessment
The growing concerns surrounding water supply, driven by factors such as population growth and industrialization, have highlighted the need for accurate estimation of streamflow at the river basin level. To achieve this, rainfall-runoff models are wi...

HRFSVM: identification of fish disease using hybrid Random Forest and Support Vector Machine.

Environmental monitoring and assessment
Aquaculture fish diseases pose a serious threat to the security of food supplies. Fish species vary widely, and because they resemble one another so much, it is challenging to distinguish between them based solely on appearance. To stop the spread of...

Machine learning approaches for data-driven process monitoring of biological wastewater treatment plant: A review of research works on benchmark simulation model No. 1(BSM1).

Environmental monitoring and assessment
In the past decade, machine learning techniques have seen wide industrial applications for design of data-based process monitoring systems with an aim to improve industrial productivity. An efficient process monitoring system for wastewater treatment...