Machine learning in agriculture: A comprehensive updated review
The digital transformation of agriculture has evolved various aspects of management into
artificial intelligent systems for the sake of making value from the ever-increasing data …
artificial intelligent systems for the sake of making value from the ever-increasing data …
Flash drought: Review of concept, prediction and the potential for machine learning, deep learning methods
This paper reviews the Flash Drought concept, the uncertainties associated with FD
prediction, and the potential of Machine Learning (ML) and Deep learning (DL) for future …
prediction, and the potential of Machine Learning (ML) and Deep learning (DL) for future …
River water quality index prediction and uncertainty analysis: A comparative study of machine learning models
SBHS Asadollah, A Sharafati, D Motta… - Journal of environmental …, 2021 - Elsevier
Abstract The Water Quality Index (WQI) is the most common indicator to characterize surface
water quality. This study introduces a new ensemble machine learning model called Extra …
water quality. This study introduces a new ensemble machine learning model called Extra …
Application of a hybrid ARIMA-LSTM model based on the SPEI for drought forecasting
D Xu, Q Zhang, Y Ding, D Zhang - Environmental Science and Pollution …, 2022 - Springer
Drought forecasting can effectively reduce the risk of drought. We proposed a hybrid model
based on deep learning methods that integrates an autoregressive integrated moving …
based on deep learning methods that integrates an autoregressive integrated moving …
[HTML][HTML] Analysis of runoff generation driving factors based on hydrological model and interpretable machine learning method
S Wang, H Peng, Q Hu, M Jiang - Journal of Hydrology: Regional Studies, 2022 - Elsevier
Abstract Study Region Xiaoqing River Basin, Shandong Province, China Study Focus
Identifying the driving factors of temporal and spatial variation in runoff is key to water …
Identifying the driving factors of temporal and spatial variation in runoff is key to water …
Comparative evaluation of LSTM, CNN, and ConvLSTM for hourly short-term streamflow forecasting using deep learning approaches
A Dehghani, HMZH Moazam, F Mortazavizadeh… - Ecological …, 2023 - Elsevier
This study investigates the effectiveness of three deep learning methods, Long Short-Term
Memory (LSTM), Convolutional Neural Network (CNN), and Convolutional Long Short-Term …
Memory (LSTM), Convolutional Neural Network (CNN), and Convolutional Long Short-Term …
Water quality management using hybrid machine learning and data mining algorithms: An indexing approach
One of the key functions of global water resource management authorities is river water
quality (WQ) assessment. A water quality index (WQI) is developed for water assessments …
quality (WQ) assessment. A water quality index (WQI) is developed for water assessments …
A contemporary review on drought modeling using machine learning approaches
Drought is the least understood natural disaster due to the complex relationship of multiple
contributory factors. Its beginning and end are hard to gauge, and they can last for months or …
contributory factors. Its beginning and end are hard to gauge, and they can last for months or …
Public perception of artificial intelligence and its connections to the sustainable development goals
SC Yeh, AW Wu, HC Yu, HC Wu, YP Kuo, PX Chen - Sustainability, 2021 - mdpi.com
Artificial Intelligence (AI) will not just change our lives but bring about revolutionary
transformation. AI can augment efficiencies of good and bad things and thus has been …
transformation. AI can augment efficiencies of good and bad things and thus has been …
Machine learning models development for accurate multi-months ahead drought forecasting: Case study of the Great Lakes, North America
The Great Lakes are critical freshwater sources, supporting millions of people, agriculture,
and ecosystems. However, climate change has worsened droughts, leading to significant …
and ecosystems. However, climate change has worsened droughts, leading to significant …