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Landslide susceptibility map** of mountain roads based on machine learning combined model
Landslide susceptibility map** of mountain roads is frequently confronted by insufficient historical landslide sample data, multicollinearity of...
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A comparative analysis of machine learning algorithms for predicting wave runup
The present study uses nine machine learning (ML) methods to predict wave runup in an innovative and comprehensive methodology. Unlike previous...
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Evapotranspiration Modeling Using Different Tree Based Ensembled Machine Learning Algorithm
The present study investigates and evaluate the scope and potential of modern computing tools and techniques such as ensembled machine learning...
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Cleaning volcano-seismic event catalogues: a machine learning application for robust systems and potential crises in volcano observatories
Complete and precise volcano-seismic event catalogues are important not only for the statistical value that they possess for describing past volcanic...
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Understanding evacuation behavior for effective disaster preparedness: a hybrid machine learning approach
This paper delves into the pivotal role of machine learning in responding to natural disasters and understanding human behavior during crises....
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Exploitation of the ensemble-based machine learning strategies to elevate the precision of CORDEX regional simulations in precipitation projection
Multi-model Ensembles (MMEs) are widely used to reduce uncertainties associated with simulations and projections of GCM/RCM.MMEs combine the results...
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Feature elimination and comparison of machine learning algorithms in landslide susceptibility map**
Landslide susceptibility assessment was adopted for the Idukki region using 6 machine learning models viz., Adaptive Boosting (AdaBoost), Naïve Bayes...
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Improving Short-range Reservoir Inflow Forecasts with Machine Learning Model Combination
This paper presents a simple and effective framework to combine various data-driven machine learning (ML) algorithms for short-range reservoir inflow...
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Assessment of machine learning models for short-term streamflow estimation: the case of Dez River in Iran
Accurate streamflow prediction is indispensable for efficient water resources management. In recent years, numerous investigations have utilized...
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Groundwater level forecasting in Northern Bangladesh using nonlinear autoregressive exogenous (NARX) and extreme learning machine (ELM) neural networks
Groundwater resources (GWR) are vital to agricultural crop production, everyday life, and economic development. As a result, accurate groundwater...
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Evaluating the integrated performance and effectiveness of RUSLE through machine learning algorithm on soil erosion susceptibility in tropical plateau basin, India
Soil erosion is a huge problem in any plateau region as the population increases with rapid urbanization, which, in turn, increases the amount of...
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Spatial Interpolation Using Machine Learning: From Patterns and Regularities to Block Models
In geospatial data interpolation, as in map**, mineral resource estimation, modeling and numerical modeling in geosciences, kriging has been a...
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Assessment and prediction of regional climate based on a multimodel ensemble machine learning method
Accurate modeling of climate change at local scales is critical for climate applications. This study proposes a regional downscaling model...
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Rainfall-runoff modeling using machine learning in the ungauged urban watershed of Quetta Valley, Balochistan (Pakistan)
Quetta Valley is an integral part of the Pishin Lora Basin (PLB), a prominently water-deficient basin within the Balochistan province of Pakistan....
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Assessing the impact of missing data on water quality index estimation: a machine learning approach
Despite the regulations and controls implemented worldwide by governments and institutions to ensure the availability and quality of water resources,...
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Evaluating Flood Susceptibility in the Brahmaputra River Basin: An Insight into Asia's Eastern Himalayan Floodplains Using Machine Learning and Multi-Criteria Decision-Making
Floods represent a significant threat to human life, property, and agriculture, especially in low-lying floodplains. This study assesses flood...
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Hybrid Extreme Gradient Boosting and Nonlinear Ensemble Models for Suspended Sediment Load Prediction in an Agricultural Catchment
In this study, four individual models namely Hammerstein-Weiner (HW), Extreme Learning Machine (ELM), Long Short-Term Memory (LSTM) and Least Square...
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A new approach for real-time prediction of stick–slip vibrations enhancement using model agnostic and supervised machine learning: a case study of Norwegian continental shelf
Efficient and safe drilling operations require real-time identification and mitigation of downhole vibrations like stick-slip, which can...
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Economical microscale predictions of wind over complex terrain from mesoscale simulations using machine learning
The ability to assess detailed wind patterns in real time is increasingly important for a variety of applications, including wind energy generation,...
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Machine learning for earthquake prediction: a review (2017–2021)
For decades, earthquake prediction has been the focus of research using various methods and techniques. It is difficult to predict the size and...
