AIMC Topic: Environmental Monitoring

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Ultrasound-enhanced catalytic degradation of simulated dye wastewater using waste printed circuit boards: catalytic performance and artificial neuron network-based simulation.

Environmental monitoring and assessment
Recent developments of heterogeneous advanced oxidation for refractory organic contaminants and catalysts made of solid waste have attracted much attention. In this work, waste printed circuit board (wPCB) was used for catalytic degradation of simula...

Deep learning system for paddy plant disease detection and classification.

Environmental monitoring and assessment
Automatic detection and analysis of rice crop diseases is widely required in the farming industry, which can be utilized to avoid squandering financial and other resources, reduce yield losses, and improve treatment efficiency, resulting in healthier...

Pattern recognition describing spatio-temporal drivers of catchment classification for water quality.

The Science of the total environment
Classification using spatial data is foundational for hydrological modelling, particularly for ungauged areas. However, models developed from classified land use drivers deliver inconsistent water quality results for the same land uses and hinder dec...

Land-Use and Land-Cover Classification in Semi-Arid Areas from Medium-Resolution Remote-Sensing Imagery: A Deep Learning Approach.

Sensors (Basel, Switzerland)
Detailed Land-Use and Land-Cover (LULC) information is of pivotal importance in, e.g., urban/rural planning, disaster management, and climate change adaptation. Recently, Deep Learning (DL) has emerged as a paradigm shift for LULC classification. To ...

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...

Monitoring and early warning of a metal mine tailings pond based on a deep learning bidirectional recurrent long and short memory network.

PloS one
The effective monitoring and early warning capability of metal mine tailings ponds can improve the associated safety risk management level. The infiltration line is an important core index of tailings pond stability. In this paper, a tailings pond mo...

Modelling built-up land expansion probability using the integrated fuzzy logic and coupling coordination degree model.

Journal of environmental management
The expansion of built-up area is the most noticeable form of urbanization-induced land use/land cover (LULC) change. In the global cities of south, the urban sprawl is increasing rapidly with even higher probabilities of future built-up expansion. T...