Bayesian network model for flood forecasting based on atmospheric ensemble forecasts

The purpose of this study is to propose the Bayesian network (BN) model to estimate flood peaks from atmospheric ensemble forecasts (AEFs). The Weather Research and Forecasting (WRF) model was used to simulate historic storms using five cumulus parameterization schemes. The BN model was trained to compute flood peak forecasts from AEFs and hydrological pre-conditions. The mean absolute relative error was calculated as 0.076 for validation data. An artificial neural network (ANN) was applied for the same problem but showed inferior performance with a mean absolute relative error of 0.39. It seems that BN is less sensitive to small data sets, thus it is more suited for flood peak forecasting than ANN

Evaluating the Anti-Leech Effects of Methanolic Extracts of Peganum harmala L. and Olea europaea L. on Limnatis nilotica. World's Vet

ABSTRACT Leeches had several complications such as pain, itching, inflammation, severe anemia, short-term bleeding, hypersensitivity, and anaphylactic reactions in their hosts. Harmal Peganum harmala L. is used as an analgesic and anti-inflammatory agent and it has antibacterial activity. Olive Olea europaea L. has antibacterial, anti-viral, hypoglycemic and the relaxation of blood vessels properties. Antioxidant properties of olive also had been reported. This study was carried out to detect the effects of methanolic extracts of P. harmala L. and O. europaea L. on L. nilotica immature form. In 2011, 55 immature leeches collected from the southern area of Ilam province were prepared. The methanolic extract of O. europaea L and P. harmala L. were compared with levamisole as the control drug. Distilled water was evaluated as the placebo group which investigated L. nilotica using anti-leech assay. Then extract and drugs were added and their effects were screened for 720 min and time to paralyze, kill and death of each leech was recorded. The results showed that olive methanolic extractions (600 and 300mg) could kill the leeches in an average time of 145±77.57 and171±33.28 min, respectively. An average death time for levamisole was found to be 15±7.49 min. The highest effectiveness was found for levamisole at dose 300 mg. Methanol extracts of the Harmal (300 and 600 μg/m) and springs water showed no anti-leech. In sum, olive plant could use for anti Limnatis nilotica expenditure

Open Drainage and Detention Basin Combined System Optimization

Introduction: Since flooding causes death and economic damages, then it is important and is one of the most complex and destructive natural disaster that endangers human lives and properties compared to any other natural disasters. This natural disaster almost hit most of countries and each country depending on its policy deals with it differently. Uneven intensity and temporal distribution of rainfall in various parts of Iran (which has arid and semiarid climate) causes flash floods and leads to too much economic damages. Detention basins can be used as one of the measures of flood control and it detains, delays and postpones the flood flow. It controls floods and affects the flood directly and rapidly by temporarily storing of water. If the land topography allows the possibility of making detention basin with an appropriate volume and quarries are near to the projects for construction of detention dam, it can be used, because of its faster effect comparing to the other watershed management measures. The open drains can be used alone or in combination with detention basin instead of detention basin solitarily. Since in the combined system of open and detention basin the dam height is increasing in contrast with increasing the open drainage capacity, optimization of the system is essential. Hence, the investigation of the sensitivity of optimized combined system (open drainage and detention basin) to the effective factors is also useful in appropriately design of the combined system. Materials and Methods: This research aims to develop optimization model for a combined system of open drainage and detention basins in a mountainous area and analyze the sensitivity of optimized dimensions to the hydrological factors. To select the dam sites for detention basins, watershed map with scale of 1: 25000 is used. In AutoCAD environment, the location of the dam sites are assessed to find the proper site which contains enough storage volume of the detention basin and the narrower valley. After the initial selection of dam sites, based on the reservoir volume to construction volume ratio of each dam site, best sites were selected to have the higher ratio. The layout of the main drainage scheme that is responsible for collecting and transferring overland flows of farmlands and reservoir outflows was designed. In order to simulate the hydrological processes in upstream watershed and flood analysis, HEC-HMS model which is an extended version of HEC-1 was used as hydrologic model. The optimal combination of open drainages and detention basins was also developed. Watershed in terms of detention basin dams, topography and drainage were divided into 19 smaller sub-basins. The downstream agricultural basin due to the slope and drainage area was divided into 27 sub-basins. Regarding available information of the watershed, SCS method was used to calculate losses and to convert rainfall to runoff hydrograph. In this section Muskingum flood routing method was used considering its accuracy. In the present optimization model, the total cost of the combined system of dams and open drains used as the objective function. It is function bottom outlet diameter which is minimized by using optimization model. Other factors of the simulation model such as dam height and drainage dimensions were defined as function of the diameter of the bottom outlet of dams. After determining the optimal dimensions of the combined system of open drainage and detention basins, a sensitivity analysis was performed on hydrological factors. Results and Discussion: After optimization of the dimensions of open drainage and detention basin integrated-system, sensitivity analysis was carried out on the dimensions of system for variation of flood simulation parameters such as rainfall, curve number and lag time. The error of estimated rainfall effected far less than the curve number (CN) on the optimum dimensions and cost. 10% variation of the rainfall depth caused respectively, 7%, 8% and 10% error in optimum dam height, drainage optimal depth and total cost. Lag time was identified less important effect in the determination of optimal dimensions. As its 10% changing produced 10% error in optimal dimensions costs. Conclusions: The research results showed that curve number is the most important factor in determining the optimal size and cost. As with 10% error in the estimation of curve number caused error rates of 21%, 25% and 24% of the optimal dam height, the optimal depth of the drain and minimized costs, respectively

Assessment of Climate Change Effects on Shahcheraghi Reservoir Inflow

Introduction: Forecasting the inflow to the reservoir is important issues due to the limited water resources and the importance of optimal utilization of reservoirs to meet the need for drinking, industry and agriculture in future time periods. In the meantime, ignoring the effects of climate change on meteorological and hydrological parameters and water resources in long-term planning of water resources cause inaccuracy. It is essential to assess the impact of climate change on reservoir operation in arid regions. In this research, climate change impact on hydrological and meteorological variables of the Shahcheragh dam basin, in Semnan Province, was studied using an integrated model of climate change assessment. Materials and Methods: The case study area of this study was located in Damghan Township, Semnan Province, Iran. It is an arid zone. The case study area is a part of the Iran Central Desert. The basin is in 12 km north of the Damghan City and between 53° E to 54° 30’ E longitude and 36° N to 36° 30’ N latitude. The area of the basin is 1,373 km2 with average annual inflow around 17.9 MCM. Total actual evaporation and average annual rainfall are 1,986 mm and 137 mm, respectively. This case study is chosen to test proposed framework for assessment of climate change impact hydrological and meteorological variables of the basin. In the proposed model, LARS-WG and ANN sub-models (7 sub models with a combination of different inputs such as temperature, precipitation and also solar radiation) were used for downscaling daily outputs of CGCM3 model under 3 emission scenarios, A2, B1 and A1B and reservoir inflow simulation, respectively. LARS-WG was tested in 99% confidence level before using it as downscaling model and feed-forward neural network was used as raifall-runoff model. Moreover, the base period data (BPD), 1990-2008, were used for calibration. Finally, reservoir inflow was simulated for future period data (FPD) of 2015-2044 and compared to BPD. The best ANN sub-model has minimum Mean Absolute Relative Error (MARE) index (0.27 in test phases) and maximum correlation coefficient (ρ) (0.82 in test phases). Results and Discussion: The tested climate change scenarios revealed that climate change has more impact on rainfall and temperature than solar radiation. The utmost growth of monthly rainfall occurred in May under all the three tested climate change scenarios. But, rainfall under A1B scenario had the maximum growth (52%) whereas the most decrease occurred (–21.5%) during January under the A2 climate change scenario. Rainfall dropped over the period of June to October under the three tested climate change scenarios. Furthermore, in all three scenarios, the maximum temperature increased about 2.2 to 2.6°C in May but the lowest increase of temperature occurred in January under A2 and B1 scenarios as 0.3 and 0.5°C, respectively. The maximum temperature usually increased in all months compared to the baseline period. Minimum and maximum temperatures enlarged likewise in all months, with 2.05°C in September under A2 climate change scenario. Conversely, solar radiation change was comparatively low and the most decreases occurred in February under A1B and A2 climate change scenarios as –4.2% and –4.3% , respectively, and in August under the B1 scenario as –4.2%. The greatest increase of solar radiation occurs in April, November, and March by 3.1%, 3.2%, and 4.9% for A1B, A2, and B1 scenarios, respectively. The impact of climate change on rainfall and temperature can origin changes on reservoir inflow and need new strategies to adapt reservoir operation for change inflows. Therefore, first, reservoir inflow in future period (after climate change impact) should be anticipated for the adaptation of the reservoir. A Feed-Forward (FF) Multilayer-Perceptron (MLP) Artificial Neural Network (ANN) model was nominated for the seven tested ANN models based on minimization of error function. The selected model had 12 neurons in the hidden layer, and two delays. The comparison of forecasted flow hydrograph by selecting an ANN model and observed one proved that forecasted flow hydrograph can follow observed one closely. By comparison with the IHACRES model, this model displayed a 54% and 46% lower error functions for validation data. The selected model was used to forecast flow for the climate change scenarios of the future period. Conclusions: The results show a reduction of monthly flow in most months and annual flow in all studied scenarios. The following main points can be concluded: • By climate change, flow growths in dry years and it declines in wet and normal years. • The studied climate change scenarios showed that climate change has more impact on rainfall and temperature than solar radiation

Heavy Rainfall Forecasting Model Using Artificial Neural Network for Flood Prone Area

Interest in monitoring severe weather events is cautiously increasing because of the numerous disasters that happen in the recent years in many world countries. Although to predict the trend of precipitation is a difficult task, there are many approaches exist using time series analysis and machine learning techniques to provide an alternative way to reduce impact of flood cause by heavy precipitation event. This study applied an Artificial Neural Network (ANN) for prediction of heavy precipitation on monthly basis. For this purpose, precipitation data from 1965 to 2015 from local meteorological stations were collected and used in the study. Different combinations of past precipitation values were produced as forecasting inputs to evaluate the effectiveness of ANN approximation. The performance of the ANN model is compared to statistical technique called Auto Regression Integrated Moving Average (ARIMA). The performance of each approaches is evaluated using root mean square error (RMSE) and correlation coefficient (R 2 ). The results indicate that ANN model is reliable in anticipating above the risky level of heavy precipitation events