Search
Search Results
-
Forecasting duration characteristics of near fault pulse-like ground motions using machine learning algorithms
The duration characteristics of near-fault earthquake ground motions play a significant role in the dynamic response of a structure. Linear...
-
Comparison of machine learning algorithms to predict dissolved oxygen in an urban stream
Water quality monitoring for urban watersheds is critical to identify the negative urbanization impacts. This study sought to identify a successful...
-
Evaluation of machine learning algorithms for groundwater quality modeling
Groundwater quality is typically measured through water sampling and lab analysis. The field-based measurements are costly and time-consuming when...
-
Predicting carbon dioxide emissions in the United States of America using machine learning algorithms
Carbon dioxide (CO 2 ) emissions result from human activities like burning fossil fuels. CO 2 is a greenhouse gas, contributing to global warming and...
-
Evaluating deep learning and machine learning algorithms for forecasting daily pan evaporation during COVID-19 pandemic
In this study, a deep learning algorithm namely long short-term memory (LSTM) has been developed for forecasting daily pan evaporation at Sydney...
-
Detailed analysis of Türkiye's agricultural biomass-based energy potential with machine learning algorithms based on environmental and climatic conditions
In the study, the biomass and energy potential of each province of Türkiye was calculated for the years 2010–2021, using data from 15 different...
-
Electrical energy recovery from wastewater: prediction with machine learning algorithms
Wind, solar, biomass, tidal, etc. are renewable energy sources obtained from natural sources. Among these resources, biomass can be characterized as...
-
Assessment of long-term mangrove distribution using optimised machine learning algorithms and landscape pattern analysis
Mangrove ecosystems provide numerous benefits, including carbon storage, coastal protection and food for marine organisms. However, map** and...
-
Impacts of meteorological variables and machine learning algorithms on rice yield prediction in Korea
As crop productivity is greatly influenced by weather conditions, many attempts have been made to estimate crop yields using meteorological data and...
-
Optimizing machine learning algorithms for spatial prediction of gully erosion susceptibility with four training scenarios
Gully erosion causes high soil erosion rates and is an environmental concern posing major risk to the sustainability of cultivated areas of the...
-
Assessing surface water pollution in Hanoi, Vietnam, using remote sensing and machine learning algorithms
Rapid urbanization led to significant land-use changes and posed threats to surface water bodies worldwide, especially in the Global South. Hanoi,...
-
Predicting potential reforestation areas by Quercus ilex (L.) species using machine learning algorithms: case of upper Ziz, southeastern Morocco
The selection of appropriate areas for reforestation remains a complex task because of influence by several factors, which requires the use of new...
-
Use of machine learning-based classification algorithms in the monitoring of Land Use and Land Cover practices in a hilly terrain
The current high rate of urbanization in develo** countries and its consequences, like traffic congestion, slum development, scarcity of resources,...
-
Remote sensing retrieval of inland water quality parameters using Sentinel-2 and multiple machine learning algorithms
Remote sensing has long been an effective method for water quality monitoring because of its advantages such as high coverage and low consumption....
-
Estimating ground-level PM2.5 using subset regression model and machine learning algorithms in Asian megacity, Dhaka, Bangladesh
Fine particulate matter (PM 2.5 ) has become a prominent pollutant due to rapid economic development, urbanization, industrialization, and transport...
-
Land subsidence susceptibility map**: a new approach to improve decision stump classification (DSC) performance and combine it with four machine learning algorithms
Land subsidence is a worldwide threat. In arid and semiarid lands, groundwater depletion is the main factor that induce the subsidence resulting in...
-
Assessment of machine learning algorithms and new hybrid multi-criteria analysis for flood hazard and map**
Floods in Iran cause a lot of damage in different places every year. The 2019 floods of the Gorgan and Atrak rivers basins in the north of Iran were...
-
A review of deep learning and machine learning techniques for hydrological inflow forecasting
Conventional machine learning models have been widely used for reservoir inflow and rainfall prediction. Nowadays, researchers focus on a new...
-
Gully Erosion Susceptibility Assessment Using Different Machine Learning Algorithms: A Case Study of Shazand Watershed in Iran
Soil, as a valuable natural resource, provides a large number of services and plays an important role in the environment and world economy. Soil...
-
A survey on advanced machine learning and deep learning techniques assisting in renewable energy generation
The sustainability of the earth depends on renewable energy. Forecasting the output of renewable energy has a big impact on how we operate and manage...
