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 …

The present study aimed to model reconnaissance drought index (RDI) time series at three
various time scales (ie, RDI-6, RDI-9, RDI-12). Two weather stations located at Iran, namely …

Accurate rainfall forecasting in watersheds is of indispensable importance for predicting
streamflow and flash floods. This paper investigates the accuracy of several forecasting …

The present study proposes five standard artificial intelligence models including Artificial
Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), Group Method of …

Drought forecasting is a vital task for sustainable development and water resource
management. Emerging machine learning techniques could be used to develop precise …

The gradient descent (GD) and Levenberg–Marquardt (LM) algorithms are commonly
adopted methods for training artificial neural network (ANN) models for modeling various …

This paper compares the classification and prediction capabilities of decision tree (DT),
genetic programming (GP), and gradient boosting decision tree (GBT) techniques for one …

Drought is a serious natural disaster that has a long duration and a wide range of influences.
To decrease drought-induced losses, drought prediction is the basis of corresponding …

Drought forecasting is an essential component of efficient water resource management that
helps water planners mitigate the severe consequences of water shortages. This is …

This paper used machine learning to model the prediction of carbonation depth and the
analysis of feature parameters for recycled aggregate concrete (RAC). Specifically, a …