DAMP: a protocol for contextualising goodness-of-fit statistics in sediment-discharge data-driven modelling

The decision sequence which guides the selection of a preferred data-driven modelling solution is usually based solely on statistical assessment of fit to a test dataset, and lacks the incorporation of essential contextual knowledge and understanding included in the evaluation of conventional empirical models. This paper demonstrates how hydrological insight and knowledge of data quality issues can be better incorporated into the sediment-discharge data-driven model assessment procedure: by the plotting of datasets and modelled relationships; and from an understanding and appreciation of the hydrological context of the catchment being modelled. DAMP: a four-point protocol for evaluating the hydrological soundness of data-driven single-input single-output sediment rating curve solutions is presented. The approach is adopted and exemplified in an evaluation of seven explicit sediment-discharge models that are used to predict daily suspended sediment concentration values for a small tropical catchment on the island of Puerto Rico. Four neurocomputing counterparts are compared and contrasted against a set of traditional log-log linear sediment rating curve solutions and a simple linear regression model. The statistical assessment procedure provides one indication of the best model, whilst graphical and hydrological interpretation of the depicted datasets and models challenge this overly-simplistic interpretation. Traditional log-log sediment rating curves, in terms of soundness and robustness, are found to deliver a superior overall product — irrespective of their poorer global goodness-of-fit statistics

High school student interests in architecture, construction, and engineering education

It is a common and widely held belief that the greatest influencing factor for high school students to enter an architecture-, construction-, or engineering-education program is because either their parents, relatives or friends work or have worked in the industry. However, there is little research that supports this belief. The focus of this research was to analyze characteristics and academic interests of Clark County School District (CCSD) Career and Technical Academies (CATA) students enrolled in architecture, construction and engineering (ACE) curricula. This research analyzed data collected from a survey conducted by CCSD of their students enrolled in ACE courses. Comprehensive descriptive statistics of the survey population were developed. The research analyzed the relationship between CATA students with their academic interest, academic performance, family member\u27s employment, and post-baccalaureate pay. Also, the students\u27 future plans regarding further community college or university ACE education or direct entry into the workforce were analyzed

Modeling Dissolved Oxygen (DO) Concentration Using Different Neural Network Techniques

The concentration of dissolved oxygen (DO) is important for the healthy functioning of aquatic ecosystems, and a significant indicator of the state of aquatic ecosystems. DO is a parameter frequently used to evaluate the water quality on different reservoirs and watersheds.In this study, two different ANN models, that is, the multilayer perceptron (MLP) and radial basis neural network (RBNN), were developed to estimate DO concentration by using various combinations of daily input variables, pH, discharge (Q), temperature (T), and electrical conductivity (EC) measured by U.S. Geological Survey (USGS). The data of Fountain Creek Stream - Gauging Station (USGS Station No: 07106000) which cover 18 years daily data between 1994-2011 were used. The ANN results were compared with those of the multiple linear regression (MLR). Comparison of the results indicated that the MLP and RBNN performed better than the MLR model. The RBNN model with three inputs which are pH, Q,and T was found to be the best model in estimation of DO concentration according to the root mean square error, mean absolute error and determination coefficient (R2) criteria

Comparison of Mann-Kendall and innovative trend method (Şen trend) for monthly total precipitation (Middle Black Sea Region, Turkey)

The objective of this study is to determine possible trend in annual total precipitation based on Mann–Kendall (MK) and a novel method lately published by Şen. The novel method is used for trend analysis of annual total precipitation data recorded at Sinop, Samsun, Ordu, Corum, Amasya, and Tokat provinces in Turkey. This provinces are located in the central Black Sea region of Turkey. The novel Şen’s trend method is applied to this data. According to the Şen’s trend method, peak and low values of annual total precipitation of the six provinces demonstrate same trends (increasing, decreasing, or trendless time series) with the MK test. The study demonstrates that the Şen method can be used for identifying trend analysis of peak and low values of annual total precipitation data. According to the MK trend test, annual total precipitations demonstrate increasing trend for Sinop, Ordu and Tokat provinces while Şen’s method indicates increasing trend in Sinop, Amasya and Tokat in Turkey. As a result, Şen’s method provides an important advantage in terms of especially in all ranges graphically clarification of the data evaluation phase

Modelling COD concentration by using three different ANFIS techniques

Artificial intelligence (AI) techniques have been successfully performed in many different water resources applications such as rainfall-runoff, precipitation, evaporation, discharge (Q), dissolved oxygen (DO), chemical oxygen demand (COD), biological oxygen demand (BOD), sediment concentration and lake levels by many researchers over the last three decades. In this study, three different adaptive neuro-fuzzy inference system (ANFIS) techniques, ANFIS with fuzzy clustering (ANFIS-FCM), ANFIS with grid partition (ANFIS-GP) and ANFIS with subtractive clustering (ANFIS-SC), were developed to estimate COD concentration by using various combinations of daily input important variables water suspended solids (SS), discharge (Q), temperature (T) and pH. Root mean square error (RMSE), mean absolute error (MAE) and determination coefficient (R2) statistics were used for the comparison criteria. Training, testing and validation phase’s results of the optimal ANFIS models were also graphically compared each other. Comparison of the results indicated that the ANFIS-SC(1,0.3,1) model whose input is water SS was found to be slightly better than the other models in estimation of COD according to the comparison criteria in testing phase. In the validation phase, however, ANFISFCM( 1,3,gauss,1) model performed slightly better than ANFIS-GP(3,trimf,constant,1) and ANFIS-SC(1,0.3,1) models. It can be said that three different ANFIS techniques provide similar accuracy in estimating COD

Drought forecasting in eastern Australia using multivariate adaptive regression spline, least square support vector machine and M5Tree model

Drought forecasting using standardized metrics of rainfall is a core task in hydrology and water resources management. Standardized Precipitation Index (SPI) is a rainfall-based metric that caters for different time-scales at which the drought occurs, and due to its standardization, is well-suited for forecasting drought at different periods in climatically diverse region. This study advances drought modelling using multivariate adaptive regression splines (MARS), least square support vector machine (LSSVM), and M5Tree models by forecasting SPI in eastern Australia. MARS model incorporated rainfall as mandatory predictor with month (periodicity), Southern Oscillation Index, Pacific Decadal Oscillation Index and Indian Ocean Dipole, ENSO Modoki and Nino 3.0, 3.4 and 4.0 data added gradually. The performance was evaluated with root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (r2). Best MARS model required different input combinations, where rainfall, sea surface temperature and periodicity were used for all stations, but ENSO Modoki and Pacific Decadal Oscillation indices were not required for Bathurst, Collarenebri and Yamba, and the Southern Oscillation Index was not required for Collarenebri. Inclusion of periodicity increased the r2 value by 0.5–8.1% and reduced RMSE by 3.0–178.5%. Comparisons showed that MARS superseded the performance of the other counterparts for three out of five stations with lower MAE by 15.0–73.9% and 7.3–42.2%, respectively. For the other stations, M5Tree was better than MARS/LSSVM with lower MAE by 13.8–13.4% and 25.7–52.2%, respectively, and for Bathurst, LSSVM yielded more accurate result. For droughts identified by SPI ≤ − 0.5, accurate forecasts were attained by MARS/M5Tree for Bathurst, Yamba and Peak Hill, whereas for Collarenebri and Barraba, M5Tree was better than LSSVM/MARS. Seasonal analysis revealed disparate results where MARS/M5Tree was better than LSSVM. The results highlight the importance of periodicity in drought forecasting and also ascertains that model accuracy scales with geographic/seasonal factors due to complexity of drought and its relationship with inputs and data attributes that can affect the evolution of drought events

Streamflow forecasting of Astore River with Seasonal Autoregressive Integrated Moving Average model

Simulation of streamflow is one of important factors in water utilization. In this paper, a linear statistical model i.e. Seasonal Autoregressive Integrated Moving Average model (SARIMA) is applied for modeling streamflow data of Astore River (1974 – 2010). On the basis of minimum Akaike Information Criteria Corrected (AICc) and Bayesian Information Criteria (BIC) values, the best model from different model structures has been identified. For testing period (2004-2010), the prediction accuracy of selected SARIMA model in comparison of auto regressive (AR) is evaluated on basis of root mean square error (RMSE), the mean absolute error (MAE) and coefficient of determination (R2 ). The results show that SARIMA performed better than AR model and can be used in streamflow forecasting at the study site