Lake evaporation in arid zones: Leveraging Landsat 8's water temperature retrieval and key meteorological drivers.

This study assessed the accuracy of various methods for estimating lake evaporation in arid, high-wind environments, leveraging water temperature data from Landsat 8. The evaluation involved four estimation techniques: the FAO 56 radiation-based equation, the Schendel temperature-based equation, the Brockamp & Wenner mass transfer-based equation, and the VUV regression-based equation. The study focused on the Chah Nimeh Reservoirs (CNRs) in the arid region of Iran due to its distinctive wind patterns and dry climate.

Satellite-based ensemble intelligent approach for predicting forest fire: a case of the Hyrcanian forest in Iran.

A machine learning-based approach is applied to simulate and forecast forest fires in the Golestan province in Iran. A dataset for no-fire, medium confidence (MC) fire events, and high confidence (HC) fire events is constructed from MODIS-MOD14A2. Nine climate variables from NASA's FLDAS are used as input variables, and 12 dates and 915 study points are considered.

Quantification of the meteorological and hydrological droughts links over various regions of Iran using gridded datasets.

Drought is a gradual phenomenon that occurs slowly and directly impacts human life and agricultural products. Due to its significant damage, comprehensive studies must be conducted on drought events. This research employs precipitation and temperature from a satellite-based gridded dataset (i.

Natural wetlands efficiency assessment in removing sugarcane fields' drainage contaminants: a case study in Khuzestan, Southwest Province of Iran.

The present study investigated the efficiency of a real-scale natural wetland (Naseri Wetland) in the qualitative treatment of agricultural drainage of Khuzestan sugarcane for 1 year (2019-2020). This study divides the wetland length into three equal parts in W1, W2, and W3 stations. The efficiency of the wetland in removing contaminants such as Cr, Cd, BOD, TDS, TN, and TP is evaluated by field sampling, laboratory analysis, and t-test.

Spatial monitoring of meteorological drought characteristics based on the NASA POWER precipitation product over various regions of Iran.

Drought directly impacts the human economy and society, so a proper understanding of its spatiotemporal characteristics in different time scales and return periods can be effective in its evaluation and risk warning. In this research, the spatiotemporal variation of drought characteristics in 70 investigated stations in Iran during 1981-2020 was examined, evaluated, and compared. The Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) have been used on time scales of 1, 3, 6, 9, 12, and 24 months to calculate the meteorological drought.

Developing a generic relation for predicting sediment pick-up rate using symbolic soft computing techniques.

Sediment pick-up rate has been investigated using experimental and numerical approaches. However, the use of soft computing methods for its prediction has received less attention so far. In this study, genetic programming (GP), grammatical evolution (GE), and gradient boosting machine (GBM) algorithms are employed to develop a relation in dimensionless form for predicting sediment pick-up rate in open channel flow based on two experimental datasets.

A robust stochastic approach in correcting the TRMM precipitation product and simulating flood features.

Logical management and decision-making on water resources require reliable weather variables, where precipitation is considered the main weather variable. Accurate estimation of precipitation is the most important topic in hydrological studies. Due to the lack of a dense network and low temporal and spatial resolution levels at ground-level rain gauges, especially in developing countries, remote sensing methods have been used widely.

Satellite-based monitoring of meteorological drought over different regions of Iran: application of the CHIRPS precipitation product.

In the present study, the spatiotemporal evaluation of the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) satellite precipitation product is performed in capturing meteorological drought over different climatic regions of Iran. The performance of the product as a high spatial resolution dataset in monitoring drought is evaluated against the 68 meteorological stations from short to long scale (i.e.

An efficient strategy for predicting river dissolved oxygen concentration: application of deep recurrent neural network model.

Dissolved oxygen (DO) concentration in water is one of the key parameters for assessing river water quality. Artificial intelligence (AI) methods have previously proved to be accurate tools for DO concentration prediction. This study presents the implementation of a deep learning approach applied to a recurrent neural network (RNN) algorithm.

Application of ensemble machine learning model in downscaling and projecting climate variables over different climate regions in Iran.

This study evaluates the future climate fluctuations in Iran's eight major climate regions (G1-G8). Synoptic data for the period 1995-2014 was used as the reference for downscaling and estimation of possible alternation of precipitation, maximum and minimum temperature in three future periods, near future (2020-2040), middle future (2040-2060), and far future (2060-2080) for two shared socioeconomic pathways (SSP) scenarios, SSP119 and SSP245. The Gradient Boosting Regression Tree (GBRT) ensemble algorithm has been utilized to implement the downscaling model.

Estimating the transient storage parameters for pollution modeling in small streams: a comparison of newly developed hybrid optimization algorithms.

The transient storage model (TSM) is a common approach to assess solute transport and pollution modeling in rivers. Several formulas have been developed to estimate TSM parameters. This study develops a new hybrid optimization algorithm consisting of the dragonfly algorithm and simulated annealing (DA-SA) algorithms.

Forecasting standardized precipitation index using data intelligence models: regional investigation of Bangladesh.

A noticeable increase in drought frequency and severity has been observed across the globe due to climate change, which attracted scientists in development of drought prediction models for mitigation of impacts. Droughts are usually monitored using drought indices (DIs), most of which are probabilistic and therefore, highly stochastic and non-linear. The current research investigated the capability of different versions of relatively well-explored machine learning (ML) models including random forest (RF), minimum probability machine regression (MPMR), M5 Tree (M5tree), extreme learning machine (ELM) and online sequential-ELM (OSELM) in predicting the most widely used DI known as standardized precipitation index (SPI) at multiple month horizons (i.

New formulations for prediction of velocity at limit of deposition in storm sewers based on a stochastic technique.

Sedimentation in storm sewers strongly depends on velocity at limit of deposition. This study provides application of a novel stochastic-based model to predict the densimetric Froude number in sewer pipes. In this way, the generalized likelihood uncertainty estimation (GLUE) is used to develop two parametric equations, called GLUE-based four-parameter and GLUE-based two-parameter (GBTP) models to enhance the prediction accuracy of the velocity at the limit of deposition.