Prediction of evaporation from dam reservoirs under climate change using soft computing techniques.

This study aimed to predict evaporation from dam reservoirs using artificial intelligence considering climate change. Mahabad Dam, near Lake Urmia, in northwestern Iran, is used to investigate the proposed approach. There are three parts to the study presented herein.

A hybrid approach based on simulation, optimization, and estimation of conjunctive use of surface water and groundwater resources.

Due to limited groundwater resources in arid and semi-arid areas, conjunctive use of surface water and groundwater is becoming increasingly important. In view of this, there are needs to improve the methods for conjunctive use of surface and groundwater. Using numerical models, optimization algorithms, and machine learning, we created a new comprehensive methodological structure for optimal allocation of surface and groundwater resources and optimal extraction of groundwater.

Predicting monthly evaporation from dam reservoirs using LS-SVR and ANFIS optimized by Harris hawks optimization algorithm.

Evaporation is a crucial factor in hydrological studies; its precise measurement has always been challenging due to the costly recording tolls. Therefore, machine learning models that can give reliable predictive results with the least information available have been recommended for evaporation prediction. This study was conducted in the central of Iran using the data related to the Doroudzan dam.

Novel approach for predicting groundwater storage loss using machine learning.

Comprehensive national estimates of groundwater storage loss (GSL) are needed for better management of natural resources. This is especially important for data scarce regions with high pressure on groundwater resources. In Iran, almost all major groundwater aquifers are in a critical state.

The prediction of longitudinal dispersion coefficient in natural streams using LS-SVM and ANFIS optimized by Harris hawk optimization algorithm.

Accurate calculation of the longitudinal dispersion coefficient (K) of pollution is essential in modeling river pollution status. Various equations are presented to calculate the K using experimental, analytical, and mathematical methods. Although machine learning models are more reliable than experimental equations in the presence of uncertainties missing data, they have not been widely used in predicting K.

The prediction of aquifer groundwater level based on spatial clustering approach using machine learning.

Water resources management requires a proper understanding of the status of available and exploitable water. One of the useful management tools is the use of simulation models that are highly efficient in spite of the complex problems in the groundwater sector. In the present study, three data-based models, namely, group method of data handling (GMDH), Bayesian network (BN), and artificial neural network (ANN), have been investigated to simulate the groundwater levels and assess the quantitative status of aquifers.