Utilizing InVEST ecosystem services model combined with deep learning and fallback bargaining for effective sediment retention in Northern Iran.

This study aimed to integrate game theory and deep learning algorithms with the InVEST Ecosystem Services Model (IESM) for Sediment Retention (SR) modeling in the Kasilian watershed, Iran. The Kasilian watershed is characterized by multiple sub-watersheds, which vary in their environmental conditions and SR potential, with a total of 19 sub-watersheds. The research was carried out in four phases: mapping SR using the IESM, implementing the Fallback bargaining algorithm based on game theory, applying deep learning algorithms (CNN, LSTM, RNN), and performing statistical analysis for optimal model selection.

Assessing the performance of machine learning algorithms for analyzing land use changes in the Hyrcanian forests of Iran.

Land use changes are of critical importance in understanding and managing environmental sustainability and resource utilization. Machine learning algorithms (MLAs) have emerged as powerful tools for analyzing and predicting land use changes, offering the potential to uncover patterns and trends that may not be readily apparent through traditional methods. This study is aimed at evaluating the efficiency of various MLAs (such as SVM, KNN, CART, Naïve Bayes, and Random Forest) in analyzing LULC changes in Northeast Iran.

Using algorithmic game theory to improve supervised machine learning: A novel applicability approach in flood susceptibility mapping.

This study was carried out with the aim of applying Condorcet and Borda scoring algorithms based on Game Theory (GT) to determine flood points and Flood Susceptibility Mapping (FSM) based on Machine Learning Algorithms (MLA) including Random Forest (RF), Support Vector Regression (SVR), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) in the Cheshmeh-Kileh watershed, Iran. Therefore, first, FS conditioning factors including Aspect (A), Elevation (E), Euclidean distance (Euc), Forest (F), NDVI, Precipitation (P), Plan Curvature (PC), Profile Curvature (PC), Residential (R), Rangeland (R), Slope (S), Stream Power Index (SPI), Topographic Position Index (TPI), and Topographic Wetness Index (TWI) were quantified in each Sub-Watershed (SW). Based on this, flood and non-flood points were identified based on both GT algorithms.

Groundwater quality modeling and determining critical points: a comparison of machine learning to Best-Worst Method.

In Iran, similar to other developing countries, groundwater quality has been seriously threatened. Therefore, this study aimed to apply Machine Learning Algorithms (MLAs) in Groundwater Quality Modeling (GQM) and determine the optimal algorithm using the Best-Worst Method (BWM) in Ardabil province, Iran. Groundwater quality parameters included calcium (Ca), magnesium (Mg), sodium (Na), potassium (K), chlorine (Cl), sulfate (SO), total dissolved solids (TDS), bicarbonate (HCO), electrical conductivity (EC), and acidity (pH).

Determination of flood probability and prioritization of sub-watersheds: A comparison of game theory to machine learning.

Floods often significantly impact human lives, properties, and activities. Prioritizing areas in a region for mitigation based on flood probability is essential for reducing losses. In this study, two game theory (GT) algorithms - Borda and Condorcet - were used to determine the areas in the Tajan watershed, Iran that were most likely to flood, and two machine learning models - random forest (RF), and artificial neural network (ANN) - were used to model flood probability (the probability of flooding).