Ali DANANDEH MEHR

• PhD
• Professor (Full) at Antalya Bilim University

Modeling non-point source pollution dynamics in inland lake basins is essential for safeguarding water quality, maintaining ecosystem integrity, protecting public health, and advancing long-term environmental sustainability. This study explores non-point pollution dynamics in the Sapanca Lake basin, Turkey, in association with the basin’s land use,...

Drought is a big challenge to world water security and ecosystem resilience, as defined by long dry periods leading to water scarcity. Because of the stochastic recurrence and severe socioeconomic impacts of droughts, precise drought modelling and forecasting are required for effective water resources management. Hence, exploring efficiency of shal...

Achieving the Sustainable Development goal 6 (SDG 6) remains a significant challenge, especially in developing countries where limitation of resources, inadequate infrastructures and environmental pressures are widespread. This review paper examines the studies published between 2010-2024 to focus on key challenges that prevent the progress towards...

Basal melting of ice shelves has become one of the main causes of mass loss from the Greenland ice sheet. However, most studies have focused on individual ice shelves, making it difficult to gain a more comprehensive understanding of basal melting across Greenland ice shelves. To address this issue, we utilized timestamped ArcticDEM strip data core...

Sea level changes is one of the most noteworthy events that affected the World population living in coastal regions. Therefore, sea level change prediction on coastal regions are extremely important challenges for management of coastal zones and mitigating the adverse impact of its variations. As one of the major indicators of global climate change...

Various critical applications, spanning from watershed management to agricultural planning and ecological sustainability, hinge upon the accurate prediction of reference evapotranspiration (ET o). In this context, our study aimed to enhance the accuracy of ET o prediction models by combining a variety of signal decomposition techniques with an Arti...

Droughts may exhibit spatiotemporal heterogeneity at regional scale. Effective drought assessment and management necessitates identifying homogeneous areas. However, previous studies often simplified clustering analysis by focusing only on a single variable. In this study, we present a novel drought risk map for the Southern Plains (SP) region of t...

Fine particulate matter (PM2.5) is a global environmental issue and a serious threat to human health. Reducing PM2.5 emissions is particularly crucial for China and India, which have the highest mortality rates associated with PM2.5 pollution. Prediction and modeling as a vital tool for making accurate PM2.5 concentration reduction policies require...

Sustainable watershed development focuses on building resilience to drought through better water resource management, ecosystem protection, and adaptation strategies. In this study, the spatiotemporal dynamics and inter-seasonal characteristics of meteorological drought across Ankara Province, Turkey, were investigated and compared using a conditio...

Monthly rainfall forecasting is an important task in hydrology. Because of the stochastic nature of rainfall events, probabilistic analysis is considered an appropriate approach for rainfall forecasting. This article introduces a new probabilistic hybrid model, called SMRF, for season-ahead monthly rainfall forecasts. The SMRF model is based on a c...

Existing artificial neural networks (ANNs) have attempted to efficiently identify underlying patterns in environmental series, but their structure optimization needs a trial-and-error process or an external optimization effort. This makes ANNs time consuming and more complex to be applied in practice. To alleviate these issues, we propose a stabili...

Study region: The state of Oklahoma located in the Southern Plains region of the United States. Study focus: The standardized precipitation evapotranspiration index (SPEI) is a widely used meteorological drought index that incorporates potential evapotranspiration (PET) into a precipitation-based index. However, the understanding of the appropriate...

As global climate change poses a challenge to crop production, it is imperative to prioritize effective adaptation of agricultural systems based on a scientific understanding of likely impacts. In this study, we applied an integrated watershed modeling framework to examine the impacts of projected climate on runoff, soil moisture, and soil erosion...

This study aimed to determine the potential for hypoxia in inland freshwater lakes via examining water quality variables, mainly dissolved oxygen (DO). To this end, field studies including environmental surveying and water column sampling at six stations along two routes in Lake Zarivar, Iran, together with laboratory analysis were conducted. Then,...

The accumulation of pollutants in the sediment along surface water may negatively affect riparian zones and increase ecological risk. This article investigates the effects of metal sediments on riparian soil via field monitoring and ICP-OES analysis. To this end, pollution levels, seasonal changes, and potential sources of the pollutants were deter...

In arid and semi-arid regions like Iran, sustainable urban and agricultural development is intimately intertwined with the severity, frequency, and duration of meteorological droughts. Prolonged meteorological droughts can trigger hydrological and socio-economic droughts, posing significant challenges to the region’s sustainability. Therefore, the...

This study proposes a numerical model for depth-averaged Reynolds equations (shallow-water equations) to investigate a dam-break problem, based upon a two-dimensional (2D) second-order upwind cell-centre finite volume method. The transportation terms were modelled using a modified approximate HLLC Riemann solver with the first-order accuracy. The p...

Drought forecasting is a vital task for sustainable development and water resource management. Emerging machine learning techniques could be used to develop precise drought forecasting models. However, they need to be explicit and simple enough to secure their implementation in practice. This article introduces a novel explicit model, called multi-...

Hydrocarbons, originating from oil and gas industries, are considered a potential risk for Nayband Bay, a natural marine park with extended mangroves, located on the north coastlines of the Persian Gulf, Iran. This paper determines the potential sources and spatial distribution of hydrocarbons, especially aliphatic hydrocarbons (AHCs), in Nayband B...

This study examined the spatiotemporal climate variability over the Ceyhan River basin in Southern Anatolia, Türkiye using historical rainfall and temperature observations recorded at 15 meteorology stations. Various statistical and geostatistical techniques were employed to determine the significance of trends for each climatic variable in the who...

Meteorological drought is a common hydrological hazard that affects human life. It is one of the significant factors leading to water and food scarcity. Early detection of drought events is necessary for sustainable agricultural and water resources management. For the catchments with scarce meteorological observatory stations, the lack of observed...

In this study, meteorological drought across the Mediterranean Region of Turkey (MRT) was investigated using fuzzy c-means clustering and innovative trend analysis (ITA). To this end, long-term (1971–2021) observed precipitation data from 35 meteorological stations distributed across the MRT were used to cluster the region and calculate standardize...

Drought has negative impacts on water resources, food security, soil degradation, desertification and agricultural productivity. The meteorological and hydrological droughts prediction using standardized precipitation/runoff indices (SPI/SRI) is crucial for effective water resource management. In this study, we suggest ANFISWCA, an adaptive neuro-f...

Once, the Tigris River (with its twin, the Euphrates) was the remarkable river in the west of Asia, making Mesopotamia a cradle of civilization thousands of years ago. Upstream anthropogenic activity has choked the Tigris River, the connecting lifeline across Iraq, and, due to droughts and desertification, caused the country to be plagued by povert...

Drought is the consequence of a significant decline in the hydrological variables such as precipitation, soil moisture, and streamflow that undesirably affects all living beings. There are various indices for drought monitoring and assessment that can identify the characteristics of drought, such as magnitude, severity, and duration. They are obtai...

The evolution of ensemble learning has recently offered a new approach to model complex systems. Inspired by the success of such methods, this paper introduces a new ensemble approach that integrates capabilities of two top state-of-the-art machine learning (ML) methods, namely random forests (RF) and genetic programming (GP), to model and forecast...

In this article, meteorological and agricultural droughts across the Erbil province, Iraq, were assessed using remote sensing data and satellite products. To this end, the long-term (2000–2022) Standardized Precipitation Evapotranspiration index (SPEI) at 1- and 3-month accumulation periods (SPEI-1 and SPEI-3) as well as the Normalized Difference V...

One of the critical consequences of climate change at both local and regional scales is a change in the patterns of extreme climate events such as droughts. Focusing on the different types of droughts, their quantifying indices, associated indicators, and sources of data (remote sensing (RS)/in situ measurements), this article reviewed the recent s...

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...