Estimation of Daily Pan Evaporation Using Two Different Adaptive Neuro-Fuzzy Computing Techniques

This paper investigates the ability of two different adaptive neuro-fuzzy inference systems (ANFIS) including grid partitioning (GP) and subtractive clustering (SC), in modeling daily pan evaporation (E_pan). The daily climatic variables, air temperature, wind speed, solar radiation and relative humidity of two automated weather stations, San Francisco and San Diego, in California State are used for pan evaporation estimation. The results of ANFIS-GP and ANFIS-SC models are compared with multivariate non-linear regression (MNLR), artificial neural network (ANN), Stephens-Stewart (SS) and Penman models. Determination coefficient (R²), root mean square error (RMSE) and mean absolute relative error (MARE) are used to evaluate the performance of the applied models. Comparison of results indicates that both ANFIS-GP and ANFIS-SC are superior to the MNLR, ANN, SS and Penman in modeling E_pan. The results also show that the difference between the performances of ANFIS-GP and ANFIS-SC is not significant in evaporation estimation. It is found that two different ANFIS models could be employed successfully in modeling evaporation from available climatic data.     

NUMERICAL SIMULATION OF SPHERICAL, CYLINDRICAL AND AXIAL BUBBLE CLOUDS COLLAPSE

The nonlinear dynamics of a spherical,cylindrical and axial cloud of cavitation bubbles were numerically simulated in order to learn more about the physical phenomena occurring in the cloud cavitation....     

Comparison of the Lumped and Quasi-distributed Clark Runoff Models in Simulating Flood Hydrographs on a Semi-arid Watershed

In applied hydrology, predicting peak flow for a stream or river is so complex due to temporal and spatial dependency of hydrological variables such as meteorological parameters, variations in soil type and land use. Either advanced distributed hydrological models or simple Lump models can be used for simulating these situations. This paper compares the performance of the quasi-distributed model ModClark versus lumped parameter model Clark in simulating the process of transformation of rainfall to runoff. The aim of this comparison is to identify whether using a complex model which takes into account spatial and temporal distribution parameters, which are hard to prepare and use, will lead to more precise results or not. For the purpose of this study, historical data of Randan basin situated in semi-arid region of Iran in North West of Tehran was used. The size of the catchment is 67.76 km². Reviewing the results of calibration and accuracy of models revealed that both models are able to simulate the hydrology of the catchment in an acceptable way.     

Developing a Multi-Objective Conflict-Resolution Model for Optimal Groundwater Management Based on Fallback Bargaining Models and Social Choice Rules: a Case Study

Conflict-resolution models can be used as practical approaches to consider the contradictions and trade-offs between the involved stakeholders in integrated water resource management. These models are utilized to reach an optimal solution considering agents interactions. In this paper, a new methodology is developed based on multi-objective optimization model (NSGA-II), groundwater simulation model, M5P model tree, fallback bargaining procedures and social choice rules to determine the optimal groundwater management policies with an emphasis on resolving conflicts between stakeholders. By incorporating the multi-objective simulation-optimization model and bargaining methods, the optimal groundwater allocation policies are determined and the preferences of the stakeholders as well as social criteria such as justice are also considered. The obtained data set, based on Monte Carlo analysis of calibrated MODFLOW model, is used for training and validating the M5P meta-models. The validated M5P meta-models are linked with NSGA-II to determine the trade-off curve (Pareto front) for the objectives. Social choice rule and fallback bargaining methods, as conflict-resolution models, are applied to determine the best socio-optimal solution among stakeholders, and their results are compared. The effectiveness of the proposed methodology is verified in a case study of Darian aquifer, Fars province, Iran. Results indicated that the solutions obtained by the proposed conflict-resolution approaches have an appropriate applicability. Total groundwater withdrawal, after applying the optimal groundwater allocations, reduced to 20.85 MCM, resulting in a 4.62 m increase in the mean groundwater level throughout the aquifer.     

Identification of Critical Flood Prone Areas in Data-Scarce and Ungauged Regions: A Comparison of Three Data Mining Models

Flood is one of the most devastating natural disasters with socio-economic consequences. Thus, preparation of the flood prone areas (FPA) map is essential for flood disaster management, and for planning further development activities. The main goal of this study is to investigate new applications of the evidential belief function (EBF), random forest (RF), and boosted regression trees (BRT) models for identifying the FPA in the Galikesh region, Iran. This research was conducted in three main stages such as data preparation, flood susceptibility mapping using EBF, RF, and BRT models and validation of constructed models using receiver operating characteristic (ROC) curve. At first, a flood inventory map was prepared using documentary sources of Iranian Water Resources Department (IWRD) and extensive field surveys. In total, 63 flood locations were identified in the study area. Of these, 47 (75%) floods were randomly selected as training/model building and the remaining 16 (25%) cases were used for the validation purposes. The flood conditioning factors considered in the study area are altitude, slope aspect, slope angle, topographic wetness index, plan curvature, geology, landuse, distance from rivers, drainage density, and soil texture. Subsequently, the FPA maps were prepared using EBF, RF, and BRT models in a GIS environment. Finally, the results were validated using ROC curve and area under the curve (AUC) analysis. From the analysis, it was seen that the EBF (AUC?=?78.67%) and BRT models (AUC?=?78.22%) performed better than RF model (AUC?=?73.33%). Therefore, the resultant FPA maps can be useful for researchers and planner in flood mitigation strategies.     

Using the Fuzzy Clustering and Principle Component Analysis for Assessing the Impact of Potential Evapotranspiration Calculation Method On the Modified RDI Index

The modified reconnaissance drought index (RDIe) which is a modified version of RDI is presented for assessing drought conditions with an emphasis on agricultural drought. The potential evapotranspiration (PET) and effective rainfall are required climatic variables to calculate RDIe. Although the FAO Penman–Monteith (FPM) equation is the reference method for determining the PET, due to the need for data of a large number of climatic variables it is difficult to use in areas with shortage climatic data. Therefore, in this research, using the fuzzy clustering (FC) and principle component analysis (PCA) methods, the influence of PET calculation methods including FPM (used as reference method), FAO Penman (FP), Hargreaves-Samani (HS), Blaney-Criddle (BC), Turc (Tu), Jensen-Haise (JH), Priestley–Taylor (PT) and FAO24 Radiation (Ra) methods on the RDIe (in 1, 3 and 12-month time scales) was assessed. In this study the climatic data series of 5 stations in Fars province, Iran from 1989 to 2018 was used. Based on the results of PCA model, in short-term time scales (1 and 3-month), the calculated RDIe values based on the HS method (at 100% of stations) and in long-term time scale (annual) based on the FP method (at 60% of stations) had the highest correlation with RDIe based on the FPM method. According to the results of FC method, in 1-month time scale, the values of RDIe using PT and HS methods (at 100% and 80% of selected stations, respectively), in 3-month time scale, the values of RDIe using PT, HS and Ra methods (at 100% of stations) and in annual time scale, the values of RDIe using FP method (at 60% of stations) had the highest similarities with the values of RDIe using FPM. Therefore, it is recommended to replace the FPM method with HS (in 1 and 3-month time scales) and FP (in 12-month time scales) methods in areas with minimum available meteorological data.

     

Spatiotemporal Modelling of Water Balance Components in Response to Climate and Landuse Changes in a Heterogeneous Mountainous Catchment

Landuse change and climate change are the main drivers of hydrological processes. The purpose of this study was to analyse the separate and combined future effects of climate and landuse changes on water balance components on different spatial and temporal scales using the integrated hydrological Soil and Water Assessment Tool model. The study focused on the changes and relationship between water yield (WYLD) and sediment yield (SYLD) in the heterogeneous Taleghan Catchment in Iran. For future climate scenarios, RCP 4.5 and RCP 8.5 of GFDL-ESM2M GCM were used for 2020–2040. A Markov chain model was used to predict landuse change in the catchment. The results indicated an increase in precipitation and evapotranspiration. The findings also showed that the relationship between WYLD and SYLD is direct and synergic. Climate change has a stronger effect on WYLD than landuse change, whereas landuse change has a stronger effect on SYLD. The conversion of rangelands to barren land is the most critical landuse change that could increase SYLD. The highest increase in WYLD and SYLD in scenario RCP4.5 resulted from the combined effects of climate and landuse change. We estimated WYLD of about 295 mm and SYLD of around 17 t/ha. The proposed methodology is universal and can be applied to similar settings to identify the most vulnerable regions. This can help prioritize management strategies to improve water and soil management in watersheds.

     

Social exclusion and labour market transitions: a multi-state multi-spell analysis using the BHPS

In this empirical paper, we assess how social exclusion arises in the context of labour market transition behaviour. We estimate a multi-state multi-spell competing risks model and identify five states: high skilled employment, intermediate skilled employment, low skilled employment, unemployment and out-of-the-labour market. Using data from the first seven waves of the British Household Panel Survey, we show that a substantial number of workers were trapped in a vicious circle of low-skilled employment, unemployment and inactivity in the 1990s. Workers who are part of the so-called flexible workforce are more likely to suffer social exclusion.     

Testing sensitivity of student enrollment with respect to tuition at an institution of higher education

This study was conducted to measure the impact of H-University's (HU's) tuition increases on enrollment. Based on an internal survey, this study attempts to explain the sensitivity of student enrollment to tuition variations. In addition, this paper develops an aggregate enrollment model and uses the common economic variables such as tuition, income, wage rates, financial aids, and unemployment rates to explain the sensitivity of demand. The most significant finding of this study is that tuition consideration seems to have a relatively small effect on students' decisions. Actually, enrollment at HU (a private institution) have increased despite higher tuition rate. Possible justifications could be proposed, such as the necessity of higher education and the fact that higher education is a continued investment in human capital, in which the more relevant decision factor is the corresponding expected rate of return and not just the cost of investment. Presented at the International Atlantic Economic Society's Conference, Vienna, Austria, March 2003.