Mohammad Ali Ghorbani

University of Tabriz · Department of Water Engineering

Soil temperature (ST) stands as a pivotal parameter in the realm of water resources and irrigation. It serves as a guide for farmers, enabling them to determine optimal planting and fertilization timings. In the backdrop of regions like Iran, where water resources are scarce, a proficient and economical prediction model for ST, particularly at lowe...

Extended Abstract Introduction The average weather condition in a specific region is defined as climate. The diversity of climatic variables is effective in determining the climate of a region and causes the formation of diverse and different climates. One of the effects of climate change is that causes an increase or decrease in a climate zone and...

We propose a new method for predicting daily river water temperature (Tw) using two input variables, namely: (i) air temperature (Ta); and (ii) river discharge (Q). The study was conducted using data collected at two stations operated by the United States Geological Survey (USGS), located at the Missouri River, USA, i.e., Hermann and St. Joseph sta...

The Taylor diagram is modified in this paper to offer a new model performance metric: the distance of the modelled results to observed statistics. Traditionally, the Taylor diagram is used to compare several models in terms of the shortest distance from the modelled results to the observation point based on visual locations of their RMSE and correl...

Suspended sediment concentration prediction is critical for the design of reservoirs, dams, rivers ecosystems, various operations of aquatic resource structure, environmental safety, and water management. In this study, two different machine models, namely the cascade correlation neural network (CCNN) and feedforward neural network (FFNN) were appl...

Weather forecasting through neural networks has increased and shown the potential for greater accuracy over recent years. Among numerous techniques, machine learning models provide more precise weather and climate prediction outcomes. The objective of this research was to analyze the highest and lowest monthly temperatures, as well as the highest w...

A carbon tax is one of the fundamental cost-effective market tools to limit global temperature and reduce greenhouse gases emissions. In this study, using the provisions of the Paris Agreement and the shadow price of carbon, the potential role of the carbon tax on carbon emission reduction in the agricultural sector of Iran is investigated. To this...

Rainfall is crucial for the development and management of water resources. Six hybrid soft computing models, including multilayer perceptron (MLP)–Henry gas solubility optimization (HGSO), MLP–bat algorithm (MLP–BA), MLP–particle swarm optimization (MLP–PSO), radial basis neural network function (RBFNN)–HGSO, RBFNN–PSO, and RBFGNN–BA, were used in...

In this study, to analyze the frequency of meteorological drought characteristics under the influence of climate change for the future period, copula functions were used. In this regard, SPI index and copula function were used in Zarinehrood watershed, Iran during the base period, and based on the CanESM2 predictors in the period of 2020–2100 on a...

Drought is one of the most important problems that humanity is facing today with effects intensifying and causing many problems in different regions due to climate change and the gradual increase in global warming in recent years. Knowing this phenomenon and managing it correctly can reduce the damage caused by it to some extent. In this study, in...

A carbon tax is one of the fundamental cost-effective market tools to limit global temperature and reduce greenhouse gases emissions. In this study, using the provisions of the Paris Agreement and the shadow price of carbon, the potential role of the carbon tax on carbon emission reduction in the agricultural sector of Iran is investigated. To this...

Artificial neural networks (ANNs) and Sperm Swarm Optimization (SSO)

Soil temperature is a key meteorological parameter that plays an important role in determining rates of physical, chemical and biological reactions in the soil. Ground temperature can vary substantially under different land cover types and climatic conditions. Proper prediction of soil temperature is thus essential for the accurate simulation of la...

Rainfall is crucial for the development and management of water resources. Six hybrid soft computing models, including multilayer perceptron (MLP)–Henry gas solubility optimization (HGSO), MLP–bat algorithm (MLP–BA), MLP–particle swarm optimization (MLP–PSO), radial basis neural network function (RBFNN)–HGSO, RBFNN–PSO, and RBFGNN–BA, were used in...

18 The Taylor diagram is modified in this paper to offer a new model performance metric: the distance 19 of the modelled results to observed statistics. Traditionally, the Taylor diagram is used to compare 20 several models in terms of the shortest distance from the modelled results to the observation point 21 based on visual locations of their RMS...

Given the increasing population growth and rapid global economic development, the conflict between reduced available water resources and increased water demand in many countries has become a challenging issue; confirming the necessity of allocating optimal water resources to equate water conservation and environmental sustainability. Uncertainties...

This paper addresses the relationship between income inequality and environmental quality in the agriculture sector as the most related sector to the environment. In this context, we used a panel data set for 28 provinces of Iran during 2003-2017 and implemented panel quantile regression. To choose the best econometric specification the Taylor diag...

Nowadays, in order to sustainably manage surface and groundwater resources, it is crucial to have knowledge of groundwater levels. The Shabestar Plain is one of the plains with water stress that was selected as the study area in this study. A question arises regarding how models with hybrid properties can boost the capabilities of metamodels in lig...

This paper investigates the dynamics of the time-series of water temperature of the Skokomish River (2019–2020) at hourly time scale by employing well-known nonlinear methods of chaotic data analysis including average mutual information, false nearest neighbors, correlation exponent, and local divergence rates. The delay time and the embedding dime...

Monitoring groundwater level provides sufficient information on groundwater quantity and quality and is vital in effective management of water resources. This study applies transfer entropy coupled with directed-weighted complex network for the analysis of groundwater levels in the Sina river-basin, Maharashtra, India. All observation wells present...

A numerical model is developed in this study using the finite element method (FEM) to estimate relative total uplift force for different positions of holes of drainage gallery in the foundation of Guangzhao gravity dam, located in China. The data of the relative total uplift force generated for different input combinations using the FEM were used t...

There are two limitations in the analysis of drought characteristics, which this study has investigated and resolved. First, the limitation of the length of the statistical period regarding the presentation of meteorological drought characteristics and the other is the frequency analysis. The first case was solved by using CRU climate data and the...

The Taylor diagram is modified in this paper to offer a new model performance metric: the distance of the modelled results to observed statistics. Traditionally, the Taylor diagram is used to compare several models in terms of the shortest distance from the modelled results to the observation point based on visual locations of their RMSE and correl...

The potential of the soil to hold plant nutrients is governed by the cation-exchange capacity (CEC) of any soil. Estimating soil CEC aids in conventional soil management practices to replenish the soil solution that supports plant growth. In this study, a multiple model integration scheme supervised with a hybrid genetic algorithm-neural network (M...

The quantitative analysis of rainfall provides an in-depth understanding of the spatio-temporal variation of rainfall patterns. The present study aims to implement complex networks for studying the temporal connections of monthly rainfall in different rainfall regimes of Turkey between 1977 and 2016. The rainfall data of 151 rain gauges were recons...

A probabilistic-based groundwater model is developed to address the spatio-temporal distribution of the severity and duration of groundwater droughts for an aquifer having high groundwater fluctuations over space and time. Many studies were performed in the past to analyse the groundwater drought using various hydrological and meteorological indice...

Carbon dioxide (CO2) is the primary cause of global warming and climate change. Iran is the seventh-largest carbon emitter in the world and it is vital to pay attention to controlling CO2 emission in Iran. The prediction is the first and critical key to control air pollution. The main aim of this study is to investigate the power of the GP-ARX mode...

Assessing spatial variability of drought-prone areas is important for disaster preparedness and impact management. This study applied state-of-the-art geographically weighted regression hybridized with kriging method (GWRKrig) to map the spatial variability of drought-prone areas in the northwest of Iran based on the Standardized Precipitation Inde...

Precipitation and temperature are the most important climate parameters, which vary both spatially and temporally. In the present study, rainfall data of 11 synoptic stations and 40 rain gauge stations and mean air temperature data of 11 synoptic stations of Ardabil province for the period 2009-2019 collected from the Meteorological Organization of...

Finding an accurate computational method for estimating pan evaporation (EPm) can be useful in the application of these methods for the development of sustainable agricultural systems and water resources management. In the present study, the proposed hybrid method called multiple model-support vector machine (MM-SVM) with the aim of showing the inc...

Rainfall and evaporation, which are known as two complex and unclear processes in hydrology, are among the key processes in the design and management of water resource projects. The application of artificial intelligence, in comparison with physical and empirical models, can be effective in the face of the complexity of hydrological processes. The...

The potential of the soil to hold plant nutrients is governed by cation exchange capacity (CEC) of any soil. Estimating soil CEC aids in conventional soil management practices to replenish the soil solution that supports plant growth. In the present study, a multiple model integration scheme driven by hybrid GANN (MM-GANN) was developed and employe...

Predicting the amount of soil exchange capacity is very valuable because it is a key indicator of soil quality for nutrient storage. In this research, using a neural-fuzzy network (ANFIS), the soil exchange capacity value was predicted with the input parameters of soil properties (such as clay, sludge, sand, gypsum and organic matter). The study ar...

Algerian climate is characterized by the transition between the subtropical climate in the north and the hot Saharan climate in the south. Understanding the spatiotemporal variability of rainfall patterns in such areas has significant implications for water resources management. To account for the spatial variation in the rainfall pattern of north...

Carbon dioxide (CO2) is the most critical factor affecting climate change and is a severe threat to human health. The Economic Cooperation Organization (ECO) member countries have unsuitable situation in terms of CO2 emission. The main aim of this paper is to investigate the relationship between CO2 emission, economic growth, energy consumption, an...

Hazards and disasters have always negative impacts on the way of life. Landslide is an overwhelming natural as well as man-made disaster that causes loss of natural resources and human properties throughout the world. The present study aimed to assess and compare the prediction efficiency of different models in landslide susceptibility in the Kysu...

Adequate understanding of the temporal connections in rainfall is important for reliable predictions of rainfall and, hence, for water resources planning and management. This research aims to study the temporal connections in rainfall using complex networks concepts. First, the single-variable rainfall time series is represented in a multi-dimensio...

Finding an accurate computational method for predicting pan evaporation ( EP ), can be useful in the application of these methods for the development of sustainable agricultural systems and water resources management. In the present study, the proposed hybrid method called Multiple Model-Support Vector Machine (MM-SVM) with the aim of increasing th...

Soil wind erodibility is an important soil characteristic controlling soil particle susceptibility to wind erosion. Measurement of soil wind erodibility in the field and wind tunnel is time-consuming and expensive. Therefore, using artificial intelligence techniques to predict soil wind erodibility could be less costly and time-consuming. This stud...

Background and Objectives: In hydrology, the frequent ill-posed Inverse problems suffer from overfitting which leads to omitting the model parameters with less outputs fitness to observations. These parameters might have better fitting during other periods. They should not be rejected but should be considered in some way. Generalized Likelihood Unc...

In this research, the physical, chemical and biological properties of 19 soil indicators using 80 samples (0–25 cm) were quantified to measure the soil quality index (SQI) in Miandoab region, Iran, across different land uses. These properties include aggregate stability (AS), bulk density (BD), soil moisture content (θm), saturation percentage (SP)...

One type of long-crested weir is oblique weir. Oblique weirs are longer than standard weirs. Therefore, they can pass more discharge capacity than weirs at the given channel width. The main objective of the present study was to investigate the efficacy of several intelligent models including multiple linear regression (MLR), Gaussian process regres...

Flood is a devastating natural hazard that may cause damage to the environment infrastructure, and society. Hence, identifying the susceptible areas to flood is an important task for every country to prevent such dangerous consequences. The present study developed a framework for identifying flood-prone areas of the Topľa river basin, Slovakia usin...

Land suitability analysis is a necessity to achieve sustainable agricultural productivity with the optimum utilization of the available resources. Lack of proper knowledge on the best combination of factors that suit production of rice and wheat has contributed to low production in the Sone river command, Bihar. The aim of this study is to develop...

Gates in dams and irrigation canals have been used for the purpose of controlling discharge or water surface regulation. To compute the discharge under a gate, discharge coefficient (Cd) should be first determined precisely. From a novel point of view, this study investigates the effect of sill shape under the vertical sluice gate on Cd using four...

Accurately predicting river flows over daily timescales is considered as an important task for sustainable management of freshwater ecosystems, agricultural applications, and water resources management. In this research paper, artificial intelligence (AI) techniques, namely the cascade correlation neural networks (CCNN) and the random forest (RF) m...

The forecasting of lake water level is one of the complex problems in the hydrology field owing to the incorporating with various hydrological and morphological characteristics. In this research, newly hybrid data intelligence (DI) model based on the integration of the Multilayer Perceptron (MLP) and Whale Optimization Algorithm (WOA), is developed...

Due to the significant effects of CO2 emissions on climate change and global warming, as well as its serious hazards to human health, the prediction of CO2 emission is a vital issue. The main aim of this paper is to evaluate the power of the Inclusive Multiple Model (IMM) as a novel approach to predict CO2 emission in the agriculture sector of Iran...

A spillway is a structure used to regulate the discharge flowing from hydraulic structures such as a dam. It also helps to dissipate the excess energy of water through the still basins. Therefore, it has a significant effect on the safety of the dam. One of the most serious problems that may be happening below the spillway is bed scouring, which le...

This research aims to model soil temperature (ST) using machine learning models of multilayer perceptron (MLP) algorithm and support vector machine (SVM) in hybrid form with the Firefly optimization algorithm, i.e. MLP-FFA and SVM-FFA. In the current study, measured ST and meteorological parameters of Tabriz and Ahar weather stations in a period of...

Mapping for Groundwater Potential Indices (GPI) is investigated for study areas with sparse data by the customary ten general-purpose data layers with a scoring system of rates and weights but assigning their values give rise to subjectivity. Learning rates/weights from site-specific data reduces subjectivity through unsupervised models. The use of...

The barriers for the development of continuous monitoring of Suspended Sediment Concentration (SSC) in channels/rivers include costs and technological gaps but this paper shows that a solution is feasible by: (i) using readily available high-resolution images; (ii) transforming the images into image analytics to form a modelling dataset; and (iii)...

Temperature is one of the most significant elements in climate and weather forecasting. There was an increase in the earth’s surface (land and ocean) temperature by 0.6 ± 0.2 °C during 1901–2000 (NOAA, Global Climate Report 2017). In evaluating the effects of climate change, the spatiotemporal variability of temperature was examined in the Chhattis...

A framework is formulated in this paper for data-driven modelling practices to characterise Inclusive Multiple Modelling (IMM) practices with multiple goals of enhancing the extracted information from given datasets and learning from multiple models. This can be a shift from traditional practices with the single goal of selecting a ‘superior’ model...

Weirs are one the most important hydraulic structures for flow control, water measurement and regulating of upstream water elevation in canals, irrigation networks and rivers. For a given of channel width, inclined weirs have longer effective length in comparison with the usual rectangular sharp crested weirs, and this could be effective on their d...

Monitoring hourly river flows is indispensable for flood forecasting and disaster risk management. The objective of the present study is to develop a suite of hourly river flow forecasting models for the Albert river, located in Queensland, Australia using various machine learning (ML) based models including a relatively new and novel artificial in...

Adequate simulation of pan evaporation is a crucial concern in water resource planning, providing drinking water supplies and reservoir management. The potential of several predictive models including multiple model-artificial neural network (MM-ANN), multivariate adaptive regression spline (MARS), support vector machine (SVM), multi-gene genetic p...

Rainfall, as one of the key components of hydrological cycle, plays an undeniable role for accurate modelling of other hydrological components. Therefore, a precise forecasting of annual rainfall is of the high importance. In this regard, several studies have been tried to predict annual rainfall of different climate zones using machine learning an...

Reference evapotranspiration (ETo) is a major component of the hydrological cycle linking the irrigation water requirement and planning and management of water resources. In this research, the potential of co-active neuro-fuzzy inference system (CANFIS) was investigated against the multilayer perceptron neural network (MLPNN), radial basis neural n...

In this research, gradual and rapid changes of hydro-climatological variables, trends were analyzed for the Lighvanchai basin. Also, the natural (stationary) and impacted periods were identified. For this purpose, the traditional Mann-Kendall (MK1), modified (MK3) and Pettitt methods were used and for determination of the trend line slope the Senʼs...