Estimation of Tasuj aquifer response to main meteorological parameter variations under Shared Socioeconomic Pathways scenarios

Theoretical and Applied Climatology - In this study, statistical and soft-computing methods are compared in forecasting groundwater levels under Shared Socioeconomic Pathways (SSPs) SSP1-2.6,...     

Soil temperature modeling at different depths using neuro-fuzzy,neural network,and genetic programming techniques

The applicability of artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS), and genetic programming (GP) techniques in estimating soil temperatures (ST) at different depths is investigated in this study. Weather data from two stations, Mersin and Adana, Turkey, were used as inputs to the applied models in order to model monthly STs. The first part of the study focused on comparison of ANN, ANFIS, and GP models in modeling ST of two stations at the depths of 10, 50, and 100 cm. GP was found to perform better than the ANN and ANFIS-SC in estimating monthly ST. The effect of periodicity (month of the year) on models’ accuracy was also investigated. Including periodicity component in models’ inputs considerably increased their accuracies. The root mean square error (RMSE) of ANN models was respectively decreased by 34 and 27 % for the depths of 10 and 100 cm adding the periodicity input. In the second part of the study, the accuracies of the ANN, ANFIS, and GP models were compared in estimating ST of Mersin Station using the climatic data of Adana Station. The ANN models generally performed better than the ANFIS-SC and GP in modeling ST of Mersin Station without local climatic inputs.     

River suspended sediment modelling using a fuzzy logic approach

Correct estimation of sediment volume carried by a river is very important for many water resources projects. Conventional sediment rating curves, however, are not able to provide sufficiently accurate results. In this paper, a fuzzy logic approach is proposed to estimate suspended sediment concentration from streamflow. This study provides forecasting benchmarks for sediment concentration prediction in the form of a numerical and graphical comparison between fuzzy and rating‐curve models. Benchmarking was based on a 5‐year period of continuous streamflow and sediment concentration data of Quebrada Blanca Station operated by the United States Geological Survey. The benchmark results showed that the fuzzy model was able to produce much better results than rating‐curve models. The fuzzy model proposed in the study is site specific and does not simulate the hysteresis effects. Copyright © 2006 John Wiley & Sons, Ltd.     

A numerical approach to the design parameters of permanent stoppings (seals and bulkheads) in Turkish underground coal mines

Observing permanent seals with different physical and mechanical characteristics under various conditions in the field is almost impossible. In conjunction with the development of high-speed computer algorithms, numerical simulation has become one of the major means to study the dynamics of such problems. Therefore, this study covers only the numerical approach to analyze the stability of underground seals. In this regard, the purpose of this study is to provide an insight to the design of underground seals by numerically analyzing their behaviors under different static and dynamic explosion overpressures using a geo-technical commercial software FLAC3D. For this purpose, a series of numerical models were constructed for a typical seal in an underground gallery with different properties such as seal thickness, seal dimensions, gallery geometry, and strength of the seal material to investigate the stability of seals exposed to various static and dynamic explosion overpressures. A total of 896 numerical analyses (512 static and 384 dynamic) were performed and evaluated. Regarding the gallery geometry, it was found that seals constructed in trapeze-shaped galleries are more stable than those constructed in horseshoe-shaped galleries having the same dimensions. Moreover, the results showed that the seal stability increases with the increasing seal thickness rather than the strength of the seal material. The statistical analyses suggest that there is a very strong exponential relationship between the seal thickness and the maximum displacement measured at the midpoints of the outer surfaces of the seals. The coefficients of determination values obtained are in the range of 0.92–0.93 and 0.92–0.95 for static and dynamic analyses, respectively. We proposed formulas which use the longer dimension of the seal (W _max ), maximum allowable displacement on the seal (D _max ), explosion overpressure applied onto the seal (P _exp ), and compressive strength of the seal material (σ _c and σ _cd for static and dynamic conditions, respectively) to predict the minimum required seal thickness (T _s ) for static and dynamic conditions. The proposed formulas enable calculating the necessary seal thickness easily if the explosion overpressure (or hydrostatic pressure) is known or approximated.     

Precipitation pattern modeling using cross-station perception: regional investigation

Establishing robust models for predicting precipitation processes can yield a significant aspect for many applications in water resource engineering and environmental prospective. In particular, understanding precipitation phenomena is crucial for managing the effects of flooding in watersheds. In this research, a regional precipitation pattern modeling was undertaken using three intelligent predictive models incorporating artificial neural network (ANN), support vector machine (SVM) and random forest (RF) methods. The modeling was carried out using monthly time scale precipitation information in a semi-arid environment located in Iraq. Twenty weather stations covering the entire region were used to construct the predictive models. At the initial stage, the region was divided into three climatic districts based on documented research. Initially, modeling was carried out for each district using historical information from regionally distributed meteorological stations for calibration. Subsequently, cross-station modeling was undertaken for each district using precipitation data from other districts. The study demonstrated that cross-station modeling was an effective means of predicting the spatial distribution of precipitation in watersheds with limited meteorological data.     

Prediction of river flow using hybrid neuro-fuzzy models

The complex nature of hydrological phenomena, like rainfall and river flow, causes some limitations for some admired soft computing models in order to predict the phenomenon. Evolutionary algorithms (EA) are novel methods that used to cover the weaknesses of the classic training algorithms, such as trapping in local optima, poor performance in networks with large parameters, over-fitting, and etc. In this study, some evolutionary algorithms, including genetic algorithm (GA), ant colony optimization for continuous domain (ACO_R), and particle swarm optimization (PSO), have been used to train adaptive neuro-fuzzy inference system (ANFIS) in order to predict river flow. For this purpose, classic and hybrid ANFIS models were trained using river flow data obtained from upstream stations to predict 1-, 3-, 5-, and 7-day ahead river flow of downstream station. The best inputs were selected using correlation coefficient and a sensitivity analysis test (cosine amplitude). The results showed that PSO improved the performance of classic ANFIS in all the periods such that the averages of coefficient of determination, R², root mean square error, RMSE (m³/s), mean absolute relative error, MARE, and Nash-Sutcliffe efficiency coefficient (NSE) were improved up to 0.19, 0.30, 43.8, and 0.13%, respectively. Classic ANFIS was only capable to predict river flow in 1-day ahead while EA improved this ability to 5-day ahead. Cosine amplitude method was recognized as an appropriate sensitivity analysis method in order to select the best inputs.     

Soil erosion modeling of watershed using cubic,quadratic and quintic splines

Natural Hazards - Soil erosion is widespread with spatio-temporal variability and is central to the determination of sediment yield, which is vital to proper management of watersheds. We propose a...     

Linear and stratified sampling-based deep learning models for improving the river streamflow forecasting to mitigate flooding disaster

Natural Hazards - Due to the need to reduce the flooding disaster, river streamflow prediction is required to be enhanced by the aid of deep learning algorithms. To achieve accurate model of...