Ali DANANDEH MEHR

Antalya Bilim University · Civil Engineering

Here, the capability of the Bat algorithm optimized extreme learning machines ELM (Bat-ELM) is demonstrated for river water temperature (Tw) modelling in the Orda River, Poland. Results using the multilayer perceptron neural network (MLPNN), the classification and regression Tree (CART) and the multiple linear regression (MLR) models were presented...

This article explores the forecasting capabilities of three classic linear and nonlinear autoregressive modeling techniques and proposes a new ensemble evolutionary time series approach to model and forecast daily dynamics in stream dissolved organic carbon (DOC). The model used data from the Oulankajoki River basin, a boreal catchment in Northern...

Machine learning (ML) methods have shown noteworthy skill in recognizing environmental patterns. However, presence of weather noise associated with the chaotic characteristics of water cycle components restricts the capability of standalone ML models in the modeling of extreme climate events such as droughts. To tackle the problem, this article sug...

Precipitation is an important component of the hydrological and energy cycles, as well as a key input parameter for many applications in the fields of hydrology, climatology, meteorology, and weather forecasting research. As a result, estimating precipitation accurately is critical. The purpose of this research is to conduct a comprehensive and com...

Trends in river flow at national scale in Iran remain largely unclear, despite good coverage of river flow at multiple monitoring stations. To address this gap, this study explores the changes in Iranian rivers’ discharge using regression and analysis of variance methods to historically rich data measured at hydrometric stations. Our assessment is...

The COVID-19 pandemic has induced changes in global air quality, mostly short-term improvements, through worldwide lockdowns and restrictions on human mobility and industrial enterprises. In this study, we explored the air pollution status in Tehran metropolitan, the capital city of Iran, during the COVID-19 outbreak. To this end, ambient air quali...

Arctic charr is one of the fish species most sensitive to climate change but studies on their freshwater habitat preferences are limited, especially in the fluvial environment. Machine learning methods offer automatic and objective models for ecohydrological processes based on observed data. However, i) the number of ecological records is often muc...

The development of an accurate soft computing method for groundwater level (GWL) forecasting is essential for enhancing the planning and management of water resources. Over the past two decades, significant progress has been made in GWL prediction using machine learning (ML) models. Several review articles have been published, reporting the advanc...

The paper presents prominent Nordic contributions to stochastic methods in hydrology and water resources during the previous 50 years. The development in methods from analysis of stationary and independent hydrological events to include non-stationarity, risk analysis, big data, operational research and climate change impacts is hereby demonstrated...

This chapter presents the theory and procedures behind supervised machine learning and how genetic programming can be applied to be an effective machine learning algorithm. Due to simple and powerful concept of computer programs, genetic programming can solve many supervised machine learning problems, especially regression and classifications. The...

Genetic programming (GP) is an evolutionary regression method that has received considerable interest to model hydro-environmental phenomena recently. Considering the sparseness of hydro-meteorological stations in northern areas, this study investigates the benefits and downfalls of univariate streamflow modeling at high latitudes using GP and seas...

Empirical relationships between air and water temperatures have been widely described in the literature and a large amount of work has been done on this subject, especially by introducing varieties of approaches ranging from deterministic and energy balance to artificial intelligence models. In the present work, the link between air and water Tempe...

The advancement of the machine learning (ML) models has demonstrated notable progress in geosciences. They can identify the underlying process or causality of natural hazards. This article introduces the development and verification procedures of a new hybrid ML model, namely Bat-ELM for predictive drought modelling. The multi-temporal standardized...

State-of-the-art random forest (RF) models have been documented as versatile tools to solve regression and classification problems in hydrology. They can model stochastic time series by bagging different decision trees. This article introduces a new hybrid RF model that increases the forecasting accuracy of RF-based models. The new model, called GA...

The advancements of artificial intelligence models have demonstrated notable progress in the field of hydrological forecasting. However, predictions of extreme climate events are still a challenging task. This paper presents the development and verification procedures of a new hybrid intelligent model, namely convolutional long short-term memory (C...

Sea level prediction is essential for the design of coastal structures and harbor operations. This study presents a methodology to predict sea level changes using sea level height and meteorological factor observations at a tide gauge in Antalya Harbor, Turkey. To this end, two different scenarios were established to explore the most feasible input...

To reduce the problem of sedimentation in open channels, calculating flow velocity is critical. Undesirable operating costs arise due to sedimentation problems. To overcome these problems , the development of machine learning based models may provide reliable results. Recently, numerous studies have been conducted to model sediment transport in non...

In this paper, the effects of the meteorological factors on the sea level changes in Antalya Bay were investigated., To this end, hourly sea level and meteorological factors time-series were obtained from Antalya tide gauge station for the period 2007-2008. Tidal components of the region were determined by using harmonic analyze. Then, these compon...

Accurate estimation of the dissolved oxygen concentration is critical and of significant importance for several environmental applications. Over the years, many types of models have been proposed to provide a more accurate estimation of dissolved oxygen at different time scales. Recently, the deep learning paradigm has been increasingly used in sev...

Streamflow forecasting plays a key role in improvement of water resource allocation, management and planning, flood warning and forecasting, and mitigation of flood damages. There are a considerable number of forecasting models and techniques that have been employed in streamflow forecasting and gained importance in hydrological studies in recent d...

A gradient boosting regression tree (GBT) approach is introduced for one- and three-month ahead standardized precipitation-evapotranspiration index (SPEI) classification for Antalya and Ankara in Turkey. First, the numerical target series of SPEI-6 was converted into the categorical vectors of extreme wet, wet, near normal, dry, and extremely dry l...

This paper examines monthly, seasonal, and annual trends in temperature and precipitation time series in Osijek during the period between 1900 and 2018. Two new methods, innovative trend analysis (ITA) and successive average methodology (SAM), together with the classic Mann–Kendall (M–K) and Sen’s slope methods, have been applied to determine poten...

Trends in river flow at national scale in Iran remain largely unclear, despite good coverage of river flow at multiple monitoring stations. To address this gap, this study explores the changes in Iranian rivers’ discharge using regression and analysis of variance methods to historically rich data measured at hydrometric stations. Our assessment is...

This study presents developing procedures and verification of a new hybrid model, namely wavelet packet-genetic programming (WPGP) for short-term meteorological drought forecast. To this end, the multi-temporal standardized precipitation evapotranspiration index (SPEI) has been used as the drought quantifying parameter at two meteorological station...

The paper examines flooding issues under rapid urbanization in Gazipasa city during the past seven years 2013-2019. The Storm Water Management Model (SWMM) integrated with the satellite images representing temporal variation in the land use and land cover (LULC) characteristics of the city were used to determine the variation in the runoff generati...

Nitrate is one of the focal water quality indices in aquatic systems. However, proper estimation of nitrate concentration is a complicated task. In this article, capabilities of deep neural networks (DNN) and conventional artificial neural networks (ANN) to model and predict nitrate concentration in the Klokot River, Bosnia and Herzegovina were inv...

This paper compares the classification and prediction capabilities of decision tree (DT), genetic programming (GP), and gradient boosting decision tree (GBT) techniques for one-month ahead prediction of standardized precipitation index in Ankara province and standardized precipitation evaporation index in central Antalya region. The evolved models...

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...

This paper presents the development and verification of a new multi-stage genetic programming (MSGP) technique, called MSGP-LASSO, which was applied for univariate streamflow forecasting in the Sedre River, an intermittent river in Turkey. The MSGP-LASSO is a practical and cost-neutral improvement over classic genetic programming (GP) that increase...

This paper aims to reduce the setup cost of water supply systems (SCWSS) by decreasing the peak values at water consumptions Water supply system (WSS) is designed by paying attention to worst scenario. It means that a WSS must supply water demanded to people at each scenario. Therefore, there is waste some part of volume needed for transporting wat...

Rainfall hindcasting is one of the most challenging tasks in the hydrometeorological forecasting community. The current ad hoc data-driven approaches appear to be insufficient for forecasting rainfall. The task becomes more difficult, when the forecasts are over a long period of time. To increase the accuracy of seasonal rainfall hindcasting, this...

The state-of-the-art genetic programming (GP) has received a great deal of attention over the past few decades and has been applied to many research areas of water resources engineering, including prediction of hydrometeorological variables, design of hydraulic structures, and recognition of hidden patterns in hydrological phenomena such as rainfal...

This paper presents a new tree-based model, namely Fuzzy Random Forest (FRF), for one month ahead Standardized Precipitation Evapotranspiration Index (SPEI) classification and prediction with a noteworthy application in ungauged catchments. The proposed FRF model uses the global SPEI dataset as the meteorological drought quantifier and applies a fu...

The competition over extracting the energy resources of the Caspian Sea together with the major anthropogenic changes in the coastal zones have resulted in increased pollution and environmental degradation of the sea. We provide the first evaluation of the spatiotemporal variation of chlorophyll-a (Chl-a) across the Caspian Sea. Using remotely sens...

This paper presents a new hybrid model, called ENN-SA, for spatiotemporal drought prediction. In ENN-SA, an Elman neural network (ENN) is conjugated with simulated annealing (SA) optimization and support vector machine (SVM) classification algorithms for the standardized precipitation index (SPI) modeling at multiple stations. The proposed model co...

Seasonal precipitation forecasting is one of the most challenging tasks in stochastic hydrology. This article proposes a new ensemble model, called EGP, to a season-ahead forecast of total seasonal precipitation. The EGP integrates evolutionary genetic programming (GP) and gene expression programming (GEP) techniques with multiple linear regression...

Large-scale oceanic oscillations and their teleconnections with meteorological events are of great importance in macro-scale climatic studies. In this regard, this study investigates the spatiotemporal teleconnections between four oceanic oscillations, namely North Atlantic Oscillation (NAO), El Niño/Southern Oscillation (ENSO), Atlantic Multi-Deca...

The anthropogenic impacts of development and frequent droughts have limited Iran's water availability. This has major implications for Iran's agricultural sector which is responsible for about 90% of water consumption at the national scale. This study investigates if declining water availability impacted agriculture in Iran. Using the Mann‐Kendall...

In this study, Regression in the Reproducing Kernel Hilbert Space (RRKHS) technique which is a non-linear regression approach formulated in the reproducing kernel Hilbert space (RRKHS) is applied for rainfall-runoff (R-R) modeling for the first time. The RRKHS approach is commonly applied when the data to be modeled is highly non-linear, and conseq...

Climate change, one of the major environmental challenges facing mankind, has caused intermittent droughts in many regions resulting in reduced water resources. This study investigated the impact of climate change on the characteristics (occurrence, duration, and severity) of meteorological drought across Ankara, Turkey. To this end, the observed m...

Many power plants use fossil fuels to produce electrical energy. A safe fuel supply is an important issue in power plant operation. Although traditional use of pipeline systems is a common economical method, serious environmental problems are inevitable in case of a leak, a puncture, a rupture, or any other mechanical damages in the pipeline system...

Submarine pipelines have become one of the popular ways of transboundary water supply. The hydraulic design of these pipelines is of significant technical challenges for engineers as it requires a comprehensive energy loss analysis. The major portion of energy loss in a submarine pipeline is created by friction losses. Besides, many fittings and co...

This study evaluates the impacts of climate change on the floodway and floodway fringe along the Shahrchi River located at Lake Urmia Basin, Iran. The raw historical (1971–2000) and near future (2021–2050) precipitation and temperature data were obtained from Middle East and North Africa domain of the Coordinated Regional Climate Downscaling Experi...

Sedimentation in sewer networks is a major problem in urban hydrology. In comparison to the well-known classic sediment transport models, this study investigates the capabilities of soft computing methods, including multigene genetic programming (MGGP), gene expression programming, and multilayer perceptron to derive accurate sewer design models. A...

Assimilating unique features of genetic programming (GP) and gene expression programming (GEP), this study introduces a hybrid algorithm which results in promising incipient non-cohesive sediment motion models. The new models use the dimensionless input parameters including relative particle size, relative deposited bed thickness, channel friction...

Recently, as an alternative method for monitoring of drainage systems, Internet of Things (IoT) technology is initiated in smart cities. IoT is used for detection of the location of the sediment deposition within the drainage pipe system to alert for repairing before complete blocking. However, from the hydraulic point of view, it is reasonable to...

Mitigation of sediment deposition in lined open channels is an essential issue in hydraulic engineering practice. Hence, the limiting velocity to keep the channel bottom clean from sediment deposits should be determined and expressed equation in all form. Recently, sediment transport modeling using various artificial intelligence (AI) techniques ha...

The standardized precipitation evapotranspiration index (SPEI) at three different time scales (i.e., SPEI-3, SPEI-6, and SPEI-12) from six meteorology stations located in Turkey are modeled in this study. To this end, two types of classic time series models, namely linear autoregressive (AR) and non-linear bi-linear (BL) are used. Furthermore, the...

Highlights • Design of GP model for electricity demand prediction. • Design of DT model for electricity demand prediction. • GP is slightly superior to DT. • GP provides explicit model. • GP and DT can be used for 1-day ahead forecast of electricity in Nicosia. Article Info Abstract Several recent studies have used various data mining techniques to...

This study investigates the main characteristics (duration, severity, and trend) of meteorological drought events over Ankara Province, Turkey. We used 46 years of observed monthly precipitation and temperature series from six meteorological stations distributed across the study area to derive the well-known meteorological drought indices; the stan...

Our cities face non-stop growth in population and infrastructures and require more energy every day. Energy management is the key success for the smart cities concept since electricity is one of the essential resources which has no alternatives. The basic role of the smart energy concept is to optimize the consumption and demand in a smart way in o...

A precise forecast of streamflow in intermittent rivers is of major difficulties and challenges in watershed management, particularly in arid and semiarid regions. The present research study introduces an ensemble gene expression programming (EGEP) modeling approach to 1- and 2-day ahead streamflow forecasts that meet both accuracy and simplicity c...

Coastal zones are expose to significant changes owing to the influence of both natural and artificial events. The causes and origins of such changes define the magnitude and characteristics of potential hazards for neighboring zones. This chapter demonstrates how drone technology was used to determine the recent morphological changes in the Boğaçay...

It is well documented that standalone machine learning methods are not suitable for rainfall forecasting in long lead-time horizons. The task is more difficult in arid and semiarid regions. Addressing these issues, the present paper introduces a hybrid machine learning model, namely multiple genetic programming (MGP), that improves the predictive a...

This paper investigates a meteorological drought analysis at two meteorology stations near metropolitan city of Ankara, Turkey. The standardized precipitation evapotranspiration index (SPEI) were estimated at the reference period 1971–2000 and compared with those of the near future climate period (2016–2040) under RCP4.5 and RCP8.5 greenhouse gas s...

Using regionally downscaled and adjusted outputs of three global climate models (GCMs), meteorological drought analysis was accomplished across Ankara, the capital city of Turkey. To this end, standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI) were projected under (representative concentration path...

Water is one of the concrete mixing components that directly affects the workability, durability, and mechanical properties of concrete elements. Generally, potable water which is suitable for human consumption is considered as an appropriate choice as mixing water in hydraulic cement concrete. However, quality of potable water and its physical and...

Plastic pipes, especially polyethylene pipes have grown to become one of the frequently utilized material in pipeline systems owing to its advantages of corrosion, biological and chemical resistance over traditional metal pipes. Similar to any other material, design of polyethylene pipeline system requires a comprehensive and detailed friction head...

Soft computing (SC) is a group of techniques and methodologies applied to solve a wide range of problems spread in several areas of science. The aim is to exploit tolerance for imprecision, uncertainty and partial truth to achieve tractability, robustness and low solution cost. Some of the well-stablished methodologies include Neural Networks, Evol...

Precipitation is an essential parameter of the hydrological cycle that known as the most important climatic variable in water resources management. In this study, monthly rainfall data obtained from Iranian Meteorological Organization at the period 1951 to 2017 were used to model monthly rainfall in Urmia city. To this end, we used two different me...

Floods are of paramount importance weather-borne disasters. With increasing trend on global temperature, considerable change in extreme weather conditions like floods were reported in recent studies. This reveals the fact that safety analysis of the existed hydraulic infrastructures like dam reservoirs must be revisit based upon climate change scen...

The Greater Melen water supply Project (GMP) is a large-scale interbasin freshwater transfer project that provides domestic and industrial water requirements of Istanbul. Its foster resource is Melen River, lying in Duzce Province, Black Sea region. In addition to water supply, hydroelectric power generation has been aimed through a hydroelectric p...

Rainfall is considered the hardest weather variable to forecast, and its cause-effect relationships often cannot be expressed in simple or complex mathematical forms. This study introduces a novel hybrid model to month ahead forecasting monthly rainfall amounts which is motivated to be used in semi-arid basins. The new approach, called MPSA-MGGP, i...

A number of recent researches have compared machine learning techniques to find more reliable approaches to solve variety of engineering problems. In the present study, capability of canonical genetic programming (GP) technique to model daily electrical energy consumption (ED) as an alternative for electrical demand prediction was investigated. For...

Emotional artificial neural network (EANN) is a cutting-edge artificial intelligence method that has been used by researchers in the engineering and medical sciences over the recent years. First introduced in the 1999s, EANN is the combination of physiological and neural sciences for investigation of complex processes. Rainfall-runoff is a complex...

This paper presents the calibration and evaluation of two genetic programming (GP) methods, namely classis GP and gene expression programming (GEP) for turbidity prediction at drinking water distribution networks. Classic GP first method was used to model turbidity at the main water source of Bihac town (Bosnia and Herzegovina) and GEP second metho...