Meysam Alizamir

Accurate monitoring of dissolved oxygen (DO) levels is critical for stakeholders to effectively safeguard water resources and aquatic ecosystem health. This research presents an innovative data fusion framework based on Bayesian model averaging (BMA) by the combination of several neuroscience models (deep learning methodologies) including multilaye...

Colorectal cancer (CRC) is a form of cancer that impacts both the rectum and colon. Typically, it begins with a small abnormal growth known as a polyp, which can either be non-cancerous or cancerous. Therefore, early detection of colorectal cancer as the second deadliest cancer after lung cancer, can be highly beneficial. Moreover, the standard tre...

In recent decades, securing drinkable water sources has become a pressing concern for populations in various regions worldwide. Therefore, to address the growing need for potable water, contemporary water purification technologies can be employed to convert saline sources into drinkable supplies. Therefore, the prediction of important parameters of...

Accurately predicting soil temperature (Ts) serves as the foundation of geothermal applications, modern irrigation strategies in arid agricultural landscapes, and understanding ecosystem changes. Also, this parameter is crucial for estimating crop water requirements, thereby enabling efficient management of scarce water resources in these moisture-...

Precisely forecasting how concrete reinforced with fiber-reinforced polymers (FRP) responds under compression is essential for fine-tuning structural designs, ensuring constructions fulfill safety criteria, avoiding overdesigning, and consequently minimizing material expenses and environmental impact. Therefore, this study explores the viability of...

Changes in soil temperature (ST) play an important role in the main mechanisms within the soil, including biological and chemical activities. For instance, they affect the microbial community composition, the speed at which soil organic matter breaks down and becomes minerals. Moreover, the growth and physiological activity of plants are directly i...

Accurate daily solar radiation prediction is a crucial task for the management and generation of solar energy as one of the alternatives to fossil fuels. In this study, the prediction accuracy of new machine learning methods, wavelet long short-term memory (WLSTM), wavelet multi-layer perceptron artificial neural network (WMLPANN), long short-term...

Reliable and precise estimation of solar energy as one of the green, clean, renewable and inexhaustible types of energies can play a vital role in energy management, especially in developing countries. Also, solar energy has less impact on the earth’s atmosphere and environment and can help to lessen the negative effects of climate change by loweri...

The likelihood of surface water and groundwater contamination is higher in regions close to land-fills due to the possibility of leachate percolation, which is a potential source of pollution. There-fore, proposing a reliable framework for monitoring leachate and groundwater parameters is an essential task for the managers and authorities of water...

Accurate predictions of significant wave heights are important for a number of maritime applications, such as design of coastal and offshore structures. In the present study, an ensemble approach of Bayesian model averaging (BMA) is used for the prediction of significant wave heights. The BMA is used in conjunction with three machine learning metho...

As an indicator measured by incubating organic material from water samples in rivers, the most typical characteristic of water quality items is biochemical oxygen demand (BOD5) concentration, which is a stream pollutant with an extreme circumstance of organic loading and controlling aquatic behavior in the eco-environment. Leading monitoring approa...

Soil moisture plays an important role in water distribution among various components of hydrological cycle and energy exchanges between the atmosphere and the earth’s surface. Its accurate estimation is necessary for optimal water management in agriculture, environment, and other related fields. This study describes the development and applications...

Climate change is leading to changing patterns of precipitation and increasingly extreme global weather. There is an urgent need to synthesize our current knowledge on climate risks to water security, which in turn is fundamental for achieving sustainable water management. Climate Risk and Sustainable Water Management discusses hydrological extreme...

Drought modelling is an important issue because it is required for curbing or mitigating its effects, alerting the people to the its consequences, and water resources planning. This study investigates the capability of a deep learning method, long short-term memory (LSTM), in forecasting drought calculated from monthly rainfall data obtained from f...

This study evaluates the potential of kriging-based (kriging and kriging-logistic) and machine learning models (MARS, GBRT, and ANN) in predicting the effluent arsenic concentration of a wastewater treatment plant. Two distinct input combination scenarios were established, using seven quantitative and qualitative independent influent variables. In...

One of the main concerns of environmental and ecological managers for rivers, lakes, reservoirs and marine ecosystems is developing a reliable and efficient predictive model for chlorophyll-a concentration. In this study, online sequential extreme learning machine, M5Prime tree, multi-layer perceptron artificial neural network, response surface met...

Total organic carbon (TOC) has vital significance for measuring water quality in river streamflow. The detection of TOC can be considered as an important evaluation because of issues on human health and environmental indicators. This research utilized the novel hybrid models to improve the predictive accuracy of TOC at Andong and Changnyeong statio...

The implementation of novel machine learning models can contribute remarkably to simulating the degradation of concrete due to environmental factors. This study considers the sulfuric acid corrosive factor in wastewater systems to simulate concrete mass loss using five machine learning models. The models include three different types of extreme lea...

Soil temperature profile (Ts) is an essential regulator of a plant's root growth, evapotranspiration rates, and hence soil water content. A more accurate model for forecasting Ts presents many worldwide opportunities in improving irrigation efficiency in arid climates and help attain sustainable water resources management. This research compares th...

We evaluate the potential of kriging-based (kriging and kriging-logistic) and machine learning models (MARS, GBRT, and ANN) in predicting the effluent arsenic concentration of a wastewater treatment plant. Two distinct input combination scenarios were established, using seven quantitative and qualitative independent influent variables. In the first...

Chlorophyll-a is one of the main indicators for water quality (WQ) analysis in environmental monitoring of aquatic ecosystems. WQ degradation is mostly result of the increase of the concentration of chlorophyll-a in a waterbody, however, proper estimation of daily chlorophyll-a concentration is a complex problem. In this study, the standard extreme...

Forecasting intermittent streamflow is essential for water management and water quality, planning of water supply, hydropower and irrigation systems. This chapter proposes a novel intelligent data analytic method using extreme learning machines combined with discrete wavelet transform (ELM-DWT) to forecast intermittent streamflow. Daily streamflow...

There is a need to develop an accurate and reliable model for predicting suspended sediment load (SSL) because of its complexity and difficulty in practice. This is due to the fact that sediment transportation is extremely nonlinear and is directed by numerous parameters such as rainfall, sediment supply, and strength of flow. Thus, this study exam...

The present study investigated the potential of new ensemble method, Bayesian model averaging (BMA), in modeling monthly solar radiation based on climatic data. Data records covered monthly maximum temperature (Tmax), minimum temperature (Tmin), sunshine hours (Hs), wind speed (Ws), relative humidity (RH), and solar radiation values obtained from t...

Streamflow forecasting is a vital task for hydrology and water resources engineering, and the different artificial intelligence (AI) approaches have been employed for this purposes until now. Additionally, the forecasting accuracy and uncertainty estimation are the meaningful assignments that need to be recognized. The addressed research investigat...

Accurately prediction of lake level fluctuations is essential for water resources planning and management. In the present study, the potential of a novel method, deep echo state network (Deep ESN), is investigated for monthly lake level prediction and its results are compared with three data driven methods, artificial neural networks (ANNs), extrem...

The biochemical oxygen demand (BOD), one of widely utilized variables for water quality assessment, is metric for the ecological division in rivers. Since the traditional approach to predict BOD is time-consuming and inaccurate due to inconstancies in microbial multiplicity, alternative methods have been recommended for more accurate prediction of...

Accurate estimation of dew point temperature (Tdew) has a crucial role in sustainable water resources management. This study investigated kernel extreme learning machine (KELM), boosted regression tree (BRT), radial basis function neural network (RBFNN), multilayer perceptron neural network (MLPNN), and multivariate adaptive regression spline (MARS...

As a measure of water quality, water turbidity might be a source of water pollution in drinking water resources. Henceforth, having a reliable tool for predicting turbidity values based on common water quantity/quality measured parameters is of great importance. In the present paper, the performance of the online sequential extreme learning machine...

This study investigates the potential of two evolutionary neuro-fuzzy inference systems, adaptive neuro-fuzzy inference system (ANFIS) with particle swarm optimization (ANFIS-PSO) and genetic algorithm (ANFIS-GA), in modelling reference evapotranspiration (ET 0). The hybrid models were tested using Nash-Sutcliffe efficiency, root mean square errors...

The potential of different models – deep echo state network (DeepESN), extreme learning machine (ELM), extra tree (ET) and regression tree (RT) – in estimating dew point temperature by using meteorological variables is investigated. The variables consist of daily records of average air temperature, atmospheric pressure, relative humidity, wind spee...

In this study, the potential of six different machine learning models, gradient boosting tree (GBT), multilayer perceptron neural network (MLPNN), two types of adaptive neuro-fuzzy inference systems (ANFIS) based on fuzzy c-means clustering (ANFIS-FCM) and subtractive clustering (ANFIS-SC), multivariate adaptive regression spline (MARS), and classi...

Soil temperature has a vital importance in biological, physical and chemical processes of terrestrial ecosystem and its modeling at different depths is very important for land-atmosphere interactions. The study compares four machine learning techniques, extreme learning machine (ELM), artificial neural networks (ANN), classification and regression...

The proper prediction of water sales revenue allows for pricing policies with a specified trend for the optimized use of water resources. The present work focuses on the prediction of the economic status of water sales revenue in a semi-arid environment. To meet this objective, evaporation data (E), dam input water volume (I), and dam output water...

Prediction of dissolved oxygen which is an important water quality (WQ) parameter is crucial for aquatic managers who have responsibility for the ecosystem health's maintenance and for the management of reservoirs related to WQ. This study proposes a new ensemble method, Bayesian model averaging (BMA), for estimating hourly dissolved oxygen. The po...

Sediment transportation in water bodies may cause many problems for the water resources projects and damage the environment. Hence, modeling sediment load components, including suspended sediment load (SSL) and bedload (BL) in rivers is of prime importance. Effective modeling of SSL and BL remains a challenging task due to their complex hydrologica...

Evapotranspiration is an important parameter in linking ecosystem functioning, climate and carbon feedbacks, agricultural management, and water resources. This study investigates the applicability of wavelet extreme learning machine (WELM) model which uses discrete wavelet transform and ELM methods in estimating daily reference evapotranspiration (...

The production of a desired product needs an effective use of the experimental model. Present study proposes an extreme learning machine (ELM) and a support vector machine (SVM) methods integrated with the response surface methodology (RSM) to tackle the complexity in optimization and prediction of the ethyl ester and methyl ester production proces...

Management strategies for sustainable sugarcane production need to deal with the increasing complexity and variability of the whole sugar system. Moreover, they need to accommodate the multiple goals of different industry sectors and the wider community. Traditional disciplinary approaches are unable to provide integrated management solutions, and...

Considerable effort has been made to determine which of the most common prediction modeling techniques performs best, based on crash‐related data. Accordingly, the present study aims to evaluate how crashes in the urban road network are affected by contributing factors. Therefore, in the present paper, a comparison has been done among four artifici...

The ability of the extreme learning machine (ELM) model is investigated in modelling groundwater level (GWL) fluctuations using hydro-climatic data obtained for Hormozgan Province, southern Iran. Monthly precipitation, evaporation and previous GWL data were used as model inputs to estimate 1-, 2- and 3-month-ahead GWLs. Developed ELM models compris...

The accuracies of three different evolutionary artificial neural network (ANN) approaches, ANN with genetic algorithm (ANN-GA), ANN with particle swarm optimization (ANN-PSO) and ANN with imperialist competitive algorithm (ANN-ICA), were compared in estimating groundwater levels (GWL) based on precipitation, evaporation and previous GWL data. The i...