Suspended sediment load prediction of river systems: GEP approach

This study presents gene expression programming (GEP), an extension of genetic programming, as an alternative approach to modeling the suspended sediment load relationship for the three Malaysian rivers. In this study, adaptive neuro-fuzzy inference system (ANFIS), regression model, and GEP approaches were developed to predict suspended load in three Malaysian rivers: Muda River, Langat River, and Kurau River [ANFIS (R ²?=?0.93, root mean square error (RMSE)?=?3.19, and average error (AE)?=?1.12) and regression model (R ²?=?0.63, RMSE?=?13.96, and AE?=?12.69)]. Additionally, the explicit formulations of the developed GEP models are presented (R ²?=?0.88, RMSE?=?5.19, and AE?=?6.5). The performance of the GEP model was found to be acceptable compare to ANFIS and better than the conventional models.     

Comparison of FFNN and ANFIS models for estimating groundwater level

Prediction of water level is an important task for groundwater planning and management when the water balance consistently tends toward negative values. In Maheshwaram watershed situated in the Ranga Reddy District of Andhra Pradesh, groundwater is overexploited, and groundwater resources management requires complete understanding of the dynamic nature of groundwater flow. Yet, the dynamic nature of groundwater flow is continually changing in response to human and climatic stresses, and the groundwater system is too intricate, involving many nonlinear and uncertain factors. Artificial neural network (ANN) models are introduced into groundwater science as a powerful, flexible, statistical modeling technique to address complex pattern recognition problems. This study presents the comparison of two methods, i.e., feed-forward neural network (FFNN) trained with Levenberg–Marquardt (LM) algorithm compared with a fuzzy logic adaptive network-based fuzzy inference system (ANFIS) model for better accuracy of the estimation of the groundwater levels of the Maheshwaram watershed. The statistical indices used in the analysis were the root mean square error (RMSE), regression coefficient (R ²) and error variation (EV).The results show that FFNN-LM and ANFIS models provide better accuracy (RMSE = 4.45 and 4.94, respectively, R ² is 93% for both models) for estimating groundwater levels well in advance for the above location.     

Monthly reservoir inflow forecasting using a new hybrid SARIMA genetic programming approach

Forecasting reservoir inflow is one of the most important components of water resources and hydroelectric systems operation management. Seasonal autoregressive integrated moving average (SARIMA) models have been frequently used for predicting river flow. SARIMA models are linear and do not consider the random component of statistical data. To overcome this shortcoming, monthly inflow is predicted in this study based on a combination of seasonal autoregressive integrated moving average (SARIMA) and gene expression programming (GEP) models, which is a new hybrid method (SARIMA–GEP). To this end, a four-step process is employed. First, the monthly inflow datasets are pre-processed. Second, the datasets are modelled linearly with SARIMA and in the third stage, the non-linearity of residual series caused by linear modelling is evaluated. After confirming the non-linearity, the residuals are modelled in the fourth step using a gene expression programming (GEP) method. The proposed hybrid model is employed to predict the monthly inflow to the Jamishan Dam in west Iran. Thirty years’ worth of site measurements of monthly reservoir dam inflow with extreme seasonal variations are used. The results of this hybrid model (SARIMA–GEP) are compared with SARIMA, GEP, artificial neural network (ANN) and SARIMA–ANN models. The results indicate that the SARIMA–GEP model (R ²=78.8, VAF =78.8, RMSE =0.89, MAPE =43.4, CRM =0.053) outperforms SARIMA and GEP and SARIMA–ANN (R ²=68.3, VAF =66.4, RMSE =1.12, MAPE =56.6, CRM =0.032) displays better performance than the SARIMA and ANN models. A comparison of the two hybrid models indicates the superiority of SARIMA–GEP over the SARIMA–ANN model.     

Prediction of local scour around complex piers using GEP and M5-Tree

Local scour around piers is one of the main causes of bridge failures. In this study, three robust techniques, artificial neural networks (ANNs), M5-Tree, and Gene Expression Programming (GEP), were employed for prediction of scour depth around complex piers. The clear water condition was chosen for all experimental tests. The results indicated that pier diameter (b _c) and foundation level (Y) are the main parameters for local scour. Furthermore, the minimum scour depth occurs in range of Y/b _c = 1.1~1.3. In next step, to evaluate the mentioned techniques, a wide range of dataset was collected from the present study and literature. The radial base function (RBF) with R ² = 0.945 and RMSE = 0.031 provides better prediction in comparison with conventional equations, M5-Tree (R ² = 0.883, RMSE = 0.292) and the GEP techniques (R ² = 0.811 and RMSE = 0.263). The equations developed by M5-Tree and GEP are more useful for practical purposes and can be easily employed to predict the depth of scour at complex piers.     

A new developed approach for the prediction of ground vibration using a hybrid PSO-optimized ANFIS-based model

Ground vibration is one of the common environmental effects of blasting operation in mining industry, and it may cause damage to the nearby structures and the surrounding residents. So, precise estimation of blast-produced ground vibration is necessary to identify blast-safety area and also to minimize environmental effects. In this research, a hybrid of adaptive neuro-fuzzy inference system (ANFIS) optimized by particle swarm optimization (PSO) was proposed to predict blast-produced ground vibration in Pengerang granite quarry, Malaysia. For this goal, 81 blasting were investigated, and the values of peak particle velocity, distance from the blast-face and maximum charge per delay were precisely measured. To demonstrate the performance of the hybrid PSO–ANFIS, ANFIS, and United States Bureau of Mines empirical models were also developed. Comparison of the predictive models was demonstrated that the PSO–ANFIS model [with root-mean-square error (RMSE) 0.48 and coefficient of determination (R ²) of 0.984] performed better than the ANFIS with RMSE of?1.61 and R ² of 0.965. The mentioned results prove the superiority of the newly developed PSO–ANFIS model in estimating blast-produced ground vibrations.     

Developing synergy regression models with space-borne ALOS PALSAR and Landsat TM sensors for retrieving tropical forest biomass

Forest stand biomass serves as an effective indicator for monitoring REDD (reducing emissions from deforestation and forest degradation). Optical remote sensing data have been widely used to derive forest biophysical parameters inspite of their poor sensitivity towards the forest properties. Microwave remote sensing provides a better alternative owing to its inherent ability to penetrate the forest vegetation. This study aims at developing optimal regression models for retrieving forest above-ground bole biomass (AGBB) utilising optical data from Landsat TM and microwave data from L-band of ALOS PALSAR data over Indian subcontinental tropical deciduous mixed forests located in Munger (Bihar, India). Spatial biomass models were developed. The results using Landsat TM showed poor correlation (R² = 0.295 and RMSE = 35 t/ha) when compared to HH polarized L-band SAR (R² = 0.868 and RMSE = 16.06 t/ha). However, the prediction model performed even better when both the optical and SAR were used simultaneously (R² = 0.892 and RMSE = 14.08 t/ha). The addition of TM metrics has positively contributed in improving PALSAR estimates of forest biomass. Hence, the study recommends the combined use of both optical and SAR sensors for better assessment of stand biomass with significant contribution towards operational forestry.     

An application of artificial intelligence for rainfall-runoff modeling

This study proposes an application of two techniques of artificial intelligence (AI) for rainfall-runoff modeling: the artificial neural networks (ANN) and the evolutionary computation (EC). Two different ANN techniques, the feed forward back propagation (FFBP) and generalized regression neural network (GRNN) methods are compared with one EC method, Gene Expression Programming (GEP) which is a new evolutionary algorithm that evolves computer programs. The daily hydrometeorological data of three rainfall stations and one streamflow station for Juniata River Basin in Pennsylvania state of USA are taken into consideration in the model development. Statistical parameters such as average, standard deviation, coefficient of variation, skewness, minimum and maximum values, as well as criteria such as mean square error (MSE) and determination coefficient (R ²) are used to measure the performance of the models. The results indicate that the proposed genetic programming (GP) formulation performs quite well compared to results obtained by ANNs and is quite practical for use. It is concluded from the results that GEP can be proposed as an alternative to ANN models.     

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.     

Improving the coastal aquifers’ vulnerability assessment using SCMAI ensemble of three machine learning approaches

The main objective of this study is to integrate adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM) and artificial neural network (ANN) to design an integrated supervised committee machine artificial intelligence (SCMAI) model to spatially predict the groundwater vulnerability to seawater intrusion in Gharesoo-Gorgan Rood coastal aquifer placed in the northern part of Iran. Six hydrological GALDIT parameters (i.e., G groundwater occurrence, A aquifer hydraulic conductivity, L level of groundwater above sea level, D distance from the shore, I impact of the existing status of seawater intrusion in the region, and T thickness of the aquifer) were considered as inputs for each model. In the training step, the values of GALDIT’s vulnerability index were conditioned by using the values of TDS concentration in order to obtain the conditioned vulnerability index (CVI). The CVI was considered as the target for each model. After training the models, each model was tested using a separate TDS dataset. The results indicated that the ANN and ANFIS algorithms performed better than the SVM algorithm. The values of correlation were obtained as 88, 87, and 80% for ANN, ANFIS, and SVM models, respectively. In the testing step of the SCMAI model, the values of RMSE, R², and r were obtained as 6.4, 0.95, and 97%, respectively. Overall, SCMAI model outperformed other models to spatially predicting vulnerable zones. The result of the SCMAI model confirmed that the western zones along the shoreline had the highest vulnerability to seawater intrusion; therefore, it seems critical to consider emergency protection plans for study area.

Graphic abstract

     

Predicting longitudinal dispersion coefficient using ANN with metaheuristic training algorithms

A reliable prediction of dispersion coefficient can provide valuable information for environmental scientists and river engineers as well. The main objective of this study is to apply intelligence techniques for predicting longitudinal dispersion coefficient in rivers. In this regard, artificial neural network (ANN) models were developed. Four different metaheuristic algorithms including genetic algorithm (GA), imperialist competitive algorithm (ICA), bee algorithm (BA) and cuckoo search (CS) algorithm were employed to train the ANN models. The results obtained through the optimization algorithms were compared with the Levenberg–Marquardt (LM) algorithm (conventional algorithm for training ANN). Overall, a relatively high correlation between measured and predicted values of dispersion coefficient was observed when the ANN models trained with the optimization algorithms. This study demonstrates that the metaheuristic algorithms can be successfully applied to make an improvement on the performance of the conventional ANN models. Also, the CS, ICA and BA algorithms remarkably outperform the GA and LM algorithms to train the ANN model. The results show superiority of the performance of the proposed model over the previous equations in terms of DR, R ² and RMSE.     

Global calibration of a novel,branched GDGT-based soil pH proxy

Recently, 6-methyl branched glycerol dialkyl glycerol tetraethers (brGDGTs) were separated from 5-methyl brGDGTs, which are used in brGDGT-based proxies. Here we analyzed brGDGTs in 27 soil samples along the 400 mm isoline of mean annual precipitation in China by using tandem 2D liquid chromatography. The fractional abundance of 6-methyl brGDGTs showed a positive correlation with soil pH, while that of 5-methyl brGDGTs decreased with increasing soil pH. The abundance ratio of 6-/5-methyl brGDGTs, namely the isomerization of branched tetraethers (IBT), was calculated. The correlation of IBT with pH (pH = 6.33 − 1.28 × IBT; R² 0.89; root mean squared error, RMSE, 0.24) was much stronger than that of the traditionally used cyclization index of branched tetraethers (CBT) with pH (R² 0.52; RMSE 0.49) and comparable with that of CBT′ with pH (R² 0.88; RMSE 0.25). Compiling all available data from 319 soil samples resulted in a global calibration: pH = 6.53 − 1.55 × IBT (R² 0.72; RMSE 0.65), which has a better correlation than the CBT_5ME-pH proxy (R² 0.63; RMSE 0.78), but a weaker correlation than the CBT′-pH proxy (R² 0.85; RMSE 0.52). Our result suggests that the IBT is a promising indicator for soil pH, particularly in cases when some compounds in the CBT′ index cannot be determined.     

Optimized functional linked neural network for predicting diaphragm wall deflection induced by braced excavations in clays

Deep excavation during the construction of underground systems can cause movement on the ground, especially in soft clay layers. At high levels, excessive ground movements can lead to severe damage to adjacent structures. In this study, finite element analyses (FEM) and the hardening small strain (HSS) model were performed to investigate the deflection of the diaphragm wall in the soft clay layer induced by braced excavations. Different geometric and mechanical properties of the wall were investigated to study the deflection behavior of the wall in soft clays. Accordingly, 1090 hypothetical cases were surveyed and simulated based on the HSS model and FEM to evaluate the wall deflection behavior. The results were then used to develop an intelligent model for predicting wall deflection using the functional linked neural network (FLNN) with different functional expansions and activation functions. Although the FLNN is a novel approach to predict wall deflection; however, in order to improve the accuracy of the FLNN model in predicting wall deflection, three swarm-based optimization algorithms, such as artificial bee colony (ABC), Harris’s hawk’s optimization (HHO), and hunger games search (HGS), were hybridized to the FLNN model to generate three novel intelligent models, namely ABC-FLNN, HHO-FLNN, HGS-FLNN. The results of the hybrid models were then compared with the basic FLNN and MLP models. They revealed that FLNN is a good solution for predicting wall deflection, and the application of different functional expansions and activation functions has a significant effect on the outcome predictions of the wall deflection. It is remarkably interesting that the performance of the FLNN model was better than the MLP model with a mean absolute error (MAE) of 19.971, root-mean-squared error (RMSE) of 24.574, and determination coefficient (R²) of 0.878. Meanwhile, the performance of the MLP model only obtained an MAE of 20.321, RMSE of 27.091, and R² of 0.851. Furthermore, the results also indicated that the proposed hybrid models, i.e., ABC-FLNN, HHO-FLNN, HGS-FLNN, yielded more superior performances than those of the FLNN and MLP models in terms of the prediction of deflection behavior of diaphragm walls with an MAE in the range of 11.877 to 12.239, RMSE in the range of 15.821 to 16.045, and R² in the range of 0.949 to 0.951. They can be used as an alternative tool to simulate diaphragm wall deflections under different conditions with a high degree of accuracy.     

Determination and prediction of standardized precipitation index (SPI) using TRMM data in arid ecosystems

Drought over a period threatens the water resources, agriculture, and socioeconomic activities. Therefore, it is crucial for decision makers to have a realistic anticipation of drought events to mitigate its impacts. Hence, this research aims at using the standardized precipitation index (SPI) to predict drought through time series analysis techniques. These adopted techniques are autoregressive integrating moving average (ARIMA) and feed-forward backpropagation neural network (FBNN) with different activation functions (sigmoid, bipolar sigmoid, and hyperbolic tangent). After that, the adequacy of these two techniques in predicting the drought conditions has been examined under arid ecosystems. The monthly precipitation data used in calculating the SPI time series (SPI 3, 6, 12, and 24 timescales) have been obtained from the tropical rainfall measuring mission (TRMM). The prediction of SPI was carried out and compared over six lead times from 1 to 6 using the model performance statistics (coefficient of correlation (R), mean absolute error (MAE), and root mean square error (RMSE)). The overall results prove an excellent performance of both predicting models for anticipating the drought conditions concerning model accuracy measures. Despite this, the FBNN models remain somewhat better than ARIMA models with R?≥?0.7865, MAE?≤?1.0637, and RMSE?≤?1.2466. Additionally, the FBNN based on hyperbolic tangent activation function demonstrated the best similarity between actual and predicted for SPI 24 by 98.44%. Eventually, all the activation function of FBNN models has good results respecting the SPI prediction with a small degree of variation among timescales. Therefore, any of these activation functions can be used equally even if the sigmoid and bipolar sigmoid functions are manifesting less adjusted R² and higher errors (MAE and RMSE). In conclusion, the FBNN can be considered a promising technique for predicting the SPI as a drought monitoring index under arid ecosystems.     

Application of neural network and ANFIS model for earthquake occurrence in Iran

This study examined the spatial-temporal variations in seismicity parameters for the September 10^th, 2008 Qeshm earthquake in south Iran. To this aim, artificial neural networks and Adaptive Neural Fuzzy Inference System (ANFIS) were applied. The supervised Radial Basis Function (RBF) network and ANFIS model were implemented because they have shown the efficiency in classification and prediction problems. The eight seismicity parameters were calculated to analyze spatial and temporal seismicity pattern. The data preprocessing that included normalization and Principal Component Analysis (PCA) techniques was led before the data was fed into the RBF network and ANFIS model. Although the accuracy of RBF network and ANFIS model could be evaluated rather similar, the RBF exhibited a higher performance than the ANFIS for prediction of the epicenter area and time of occurrence of the 2008 Qeshm main shock. A proper training on the basis of RBF network and ANFIS model might adopt the physical understanding between seismic data and generate more effective results than conventional prediction approaches. The results of the present study indicated that the RBF neural networks and the ANFIS models could be suitable tools for accurate prediction of epicenteral area as well as time of occurrence of forthcoming strong earthquakes in active seismogenic areas.     

Evaluation of blast-induced ground vibrations in open-pit mines by using adaptive neuro-fuzzy inference systems

This study addresses the effects of rock characteristics and blasting design parameters on blast-induced vibrations in the Kangal open-pit coal mine, the Tülü open-pit boron mine, the K?rka open-pit boron mine, and the TKI Çan coal mine fields. Distance (m, R) and maximum charge per delay (kg, W), stemming (m, SB), burden (m, B), and S-wave velocities (m/s, Vs) obtained from in situ field measurements have been chosen as input parameters for the adaptive neuro-fuzzy inference system (ANFIS)-based model in order to predict the peak particle velocity values. In the ANFIS model, 521 blasting data sets obtained from four fields have been used (r ² = 0.57–0.81). The coefficient of ANFIS model is higher than those of the empirical equation (r ² = 1). These results show that the ANFIS model to predict PPV values has a considerable advantage when compared with the other prediction models.     

Natural concentrations of lead in ancient Arctic and Antarctic ice

Concentrations of Pb and K were determined in a series of veneer layers chiseled in sequence from the outside toward the center of each of the five 1500–5500yr old ice core sections that had been drilled in Greenland and Antarctic ice. They were analogs of very old ice samples analyzed earlier by Herronet al. (1977) and Craginet al. (1975), who reported high concentrations of Pb in them. Lead contamination, existing at exterior concentrations of about 10⁶ ng/kg ice, had intruded to the centers of the cores, establishing interior values of at least 1.4 ng/kg ice in three electromechanically drilled Camp Century core sections taken from fluid filled drill holes. Corresponding Pb concentration changes were 3 × 10⁴ ng/kg ice to 1.2 ng/kg ice in two thermally drilled New Byrd Station core sections taken from non-fluid filled drill holes. Contamination made the lowest center concentrations serve only as upper limits to the original concentrations of Pb in the ice.Potassium concentrations decreased from exterior values of about 5 × 10⁵ ng/kg ice to an interior value of 2 × 10³ ng/kg ice in the Camp Century core sections and from 8 × 10⁴ ng/kg ice to 9 × 10² ng/kg ice in New Byrd Station core sections. Potassium contamination effects were not large within the central portions of the cores.These data verify earlier findings by Murozumiet al. (1969) and extend to a broader geographical significance the general validity of their observation of a ~ 300-fold increase of Pb concentrations in the Greenland ice sheet during the past 3000 yr. Our findings refute claims by Herronet al. (1977) and Craguinet al. (1975) that 100-fold excesses of natural Pb exist in 800 yr old Greenland ice above levels contributed by silicate dusts. Our new data also show that average Pb concentrations of 26 ng Pb/kg ice, claimed by Boutron and Lorius (1979) to be natural and present for 60 yr in snow strata in Antarctica, did not exist in old Antarctic ice, and that Pb concentrations have increased at least 10-fold in that ice during the past century.Virtually all of the present day ~300-fold excess of Pb above natural levels in Greenland ice can be shown to be caused by industrial Pb emissions to the atmosphere on the basis of the following factors: (1) the historic increase of Pb in snow strata coincides with the historic increase of industrial Pb production and atmospheric emissions (2) mass inventories of industrial emissions can account for the excess Pb in polar snow (3) new quantitative measurements of Pb emissions from volcanic plumes by Buat-Menard and Arnold (1978), Pattersonet al. (1981), and Buat-Menardet al. (1981), and from sea spray by Ng and Patterson (1981) and Settle and Patterson (1981). show that these natural sources cannot account for 99% of the excess Pb above contributions by silicate dusts observed today in the atmosphere; and (4) the historic increase of Pb in snow strata is paralleled by analogous increases of excess Pb shown by isotopic tracers to be industrial in water-laid sediments in a remote continental region (Shirahataet al., 1980). It is now known, however, that snows display about a 10-fold greater excess of industrial Pb above crustal silicate concentrations than exists in the air above the snows.     

Mathematical expression of discharge capacity of compound open channels using MARS technique

In this paper, analytical methods, artificial neural network (ANN) and multivariate adaptive regression splines (MARS) techniques were utilised to estimate the discharge capacity of compound open channels (COC). To this end, related datasets were collected from literature. The results showed that the divided channel method with a coefficient of determination (R ²) value of 0.76 and root mean square error (RMSE) value of 0.162 has the best performance, among the various analytical methods tested. The performance of applied soft computing models with R ²=0.97 and RMSE = 0.03 was found to be more accurate than analytical approaches. Comparison of MARS with the ANN model, in terms of developed discrepancy ratio (DDR) index, showed that the accuracy of MARS model was better than that of MLP model. Reviewing the structure of the derived MARS model showed that the longitudinal slope of the channel (S), relative flow depth (H _r ) and relative area (A _r ) have a high impact on modelling and forecasting the discharge capacity of COCs.     

Process length variation of the cyst of the dinoflagellate Protoceratium reticulatum in the North Pacific and Baltic‐Skagerrak region: calibration as an annual density proxy and first evidence of pseudo‐cryptic speciation

Process length variation of cysts of the dinoflagellate Protoceratium reticulatum (Claparède et Lachmann) Bütschli in surface sediments from the North Pacific was investigated. The average process length showed a significant inverse relation to annual seawater density: σ_{t annual} = ?0.8674 × average process length + 1029.3 (R² = 0.84), with a standard error of 0.78 kg m^?3. A sediment trap study from Effingham Inlet in British Columbia revealed the same relationship between average process length and local seawater density variations. In the Baltic–Skagerrak region, the average process length variation was related significantly to annual seawater density: σ_{t annual} = 3.5457 × average process length ? 993.28 (R² = 0.86), with a standard error of 3.09 kg m^?3. These calibrations cannot be reconciled, which accentuates the regional character of the calibrations. This can be related to variations in molecular data (small subunit, long subunit and internal transcribed spacer sequences), which show the presence of several genotypes and the occurrence of pseudo‐cryptic speciation within this species. Copyright © 2012 John Wiley & Sons, Ltd.     

Evaluation of Alexandria Eastern Harbor evaporation estimate methods

The best evaporation estimate method for Alexandria Eastern Harbor has been derived following the evaluation of performance of five evaporation estimate methods, namely, (1) Bowen ratio energy balance (BREB), (2) De Bruin–Keijman (DK), (3) Priestley–Taylor (PT), (4) Brutsaert–Stricker (BS), and (5) Penman (PM), based on two consecutive summer seasons of 2010 and 2011 data. Different statistical measurements of goodness of fit, namely, coefficient of determination (R²), root mean square error (RMSE), relative bias (RB), and index of agreement (D) have been chosen for the evaluation of the performance. When the Bowen ratio is known a priori, the DK and PT methods have been found the best evaporation estimate methods. The responses of the three methods BREB, DK, and PT were found comparable to each other, while the PM method response differed to match with the responses of the other three methods. The Bowen ration (β) of 0.05 and Priestley–Taylor (PT) coefficient (α) of 1.23 derived from the analysis can be extended for evaporation estimates in Alexandria Eastern Harbor, Egypt.     

Study on the Crystal of Daomanite

It was not possible to carry out a complete analyses of crystal, as the experiment by Ding and Shi et al.. It's analysis precision R=0.25 or more big than this, which value are not satisfied for single crystal study, but we through many test and found the best: [R(int)=14.5%]. The final fullmatix least-squares refinement on F2 converged to R1=0.0791 and wR2=0.1864 for 704 observed reflections [I 3 2s(I)]. Daomanite is orthorhombic system, space group Cmc21, a=3.7520(8))?, b=15.844(4) ?, c=5.8516(12) ?, α=β=γ=90°. V=347.86(14)?3, Z=4. Daomanite chemical formula is Cu Pt AsS 2. Idealized composition Me+M2+M2+S2=CuS ·PtA s S. There is no other similar mineral in the world.