Climate change impact assessment on mild and extreme drought events using copulas over Ankara, Turkey

From Springer Nature via Jisc Publications RouterHistory: received 2019-10-26, accepted 2020-05-04, registration 2020-05-04, pub-electronic 2020-05-22, online 2020-05-22, pub-print 2020-08Publication status: PublishedFunder: University of ManchesterAbstract: 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 monthly rainfall series from five meteorology stations scattered across Ankara Province as well as dynamically downscaled outputs of three global climate models that run under RCP 4.5 and RCP 8.5 scenarios was used to attain the well-known SPI series during the reference period of 1986–2018 and the future period of 2018–2050, respectively. Analyzing drought features in two time periods generally indicated the higher probability of occurrence of drought in the future period. The results showed that the duration of mild droughts may increase, and extreme droughts will occur with longer durations and larger severities. Moreover, joint return period analysis through different copula functions revealed that the return period of mild droughts will remain the same in the near future, while it declines by 12% over extreme droughts in the near future

Genetic programming in water resources engineering: a state-of-the-art review

The state-of-the-art genetic programming (GP) method is an evolutionary algorithm for automatic generation of computer programs. In recent decades, GP has been frequently applied on various kind of engineering problems and undergone speedy advancements. A number of studies have demonstrated the advantage of GP to solve many practical problems associated with water resources engineering (WRE). GP has a unique feature of introducing explicit models for nonlinear processes in the WRE, which can provide new insight into the understanding of the process. Considering continuous growth of GP and its importance to both water industry and academia, this paper presents a comprehensive review on the recent progress and applications of GP in the WRE fields. Our review commences with brief explanations on the fundamentals of classic GP and its advanced variants (including multigene GP, linear GP, gene expression programming, and grammar-based GP), which have been proven to be useful and frequently used in the WRE. The representative papers having wide range of applications are clustered in three domains of hydrological, hydraulic, and hydroclimatological studies, and outlined or discussed at each domain. Finally, this paper was concluded with discussions of the optimum selection of GP parameters and likely future research directions in the WRE are suggested.No sponso

A genetic programming approach to forecast daily electricity demand

International Conference on Theory and Application of Fuzzy Systems and Soft Computing - ICAFS-2018 (13. : 2019 : Warsaw, Poland)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 this aim, using the most recent ED data recorded at northern part of Nicosia, Cyprus, we put forward two daily prediction scenarios subjected to train and validate by GPdotNET, an open source GP software. Minimizing root mean square error between the modeled and observed data as the objective function, the best prediction model at each scenario has been presented for the city. The results indicated the promising role of GP for daily ED prediction in Nicosia, however it suffers from lagged prediction that must be considered in practical application.No sponso

Sediment transport modeling in non-deposition with clean bed condition using different tree-based algorithms

Abstract 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-deposition condition however, the main deficiency of the existing studies is utilization of a limited range of data in model development. To tackle this drawback, six data sets with wide ranges of pipe size, volumetric sediment concentration, channel bed slope, sediment size and flow depth are used for the model development in this study. Moreover, two tree-based algorithms, namely M5 rule tree (M5RT) and M5 regression tree (M5RGT) are implemented, and results are compared to the traditional regression equations available in the literature. The results show that machine learning approaches outperform traditional regression models. The tree-based algorithms, M5RT and M5RGT, provided satisfactory results in contrast to their regression-based alternatives with RMSE = 1.184 and RMSE = 1.071, respectively. In order to recommend a practical solution, the tree structure algorithms are supplied to compute sediment transport in an open channel flow

A new evolutionary hybrid random forest model for SPEI forecasting

Abstract 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 GARF, is attained by integrating genetic algorithm (GA) and hybrid random forest (RF), in which different decision trees are bagged. We applied GARF to model and forecast a multitemporal drought index (SPEI-3 and SPEI-6) at two meteorology stations (Beypazari and Nallihan) in Ankara, Turkey. We compared the associated results with classic RF, standalone extreme learning machine (ELM), and a hybrid ELM model optimized by Bat algorithm (Bat-ELM) to verify the new model accuracy. The performance assessment was performed using graphical and statistical analysis. The forecasting results demonstrated that the GARF outperformed the benchmark models. GARF achieved the least error in a quantitative assessment for the prediction of both SPEI-3 and SPEI-6, particularly in the testing period. The results of this study showed that the new model can improve the forecasting accuracy of the classic RF technique up to 30% and 40% at Beypazari and Nallihan stations, respectively

The impact of Turkey’s water resources development on the flow regime of the Tigris River in Iraq

Abstract Study region: 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 poverty. Study focus: Here, we give a perspective on flow regime alteration in the main corridor of the Tigris River at five crucial points (Cizre, Mosul, Baiji, Baghdad, and Kut) before and after the planned water resources development in Turkey. Turkey’s Tigris River regulation goal is to generate about 7247 GWh of energy and irrigate over 640,000 ha of farmlands. New hydrological insights for the region: We reconstructed the natural flow along the Tigris River. In addition, to evaluate hydrological droughts, we proposed a modified streamflow drought index (MSDI) and compared it with the original streamflow drought index (SDI). The results show that the worst hydrological conditions could be found below the Samarra barrage in Iraq before the Tigris River regulation in Turkey. This negative hydrological condition will be extended to the whole corridor of the Tigris River in Iraq after the implementation of Turkey’s goal. As a result, for example, Cizre and Mosul will experience extreme conditions in 37.5–87.5% of the years; this means a considerable reduction in the Mosul reservoire’s inflow (135–326 m³/sec). Consequently, some parts of Mosul’s hydropower and reservoir capacity will be useless, and hydrological drought upstream of the Samarra barrage will be dominated