応用水文 = Applied hydrology

Developing a Long Short-Term Memory (LSTM) based model for predicting water table depth in agricultural areas.

The beneficial effects of silicon and its role for plants are well established; however, the advantages of silicon nanoparticles over its bulk material are an area that is less explored. Silicon nanoparticles have distinctive physiological characteristics that allow them to enter plants and influence plant metabolic activities. The mesoporous nature of silicon nanoparticles also makes them good candidates as suitable nanocarriers for different molecules that may help in agriculture. Several studies have shown the importance of silicon nanoparticles in agriculture, but an overview of the related aspects was missing. Therefore, this review brings together the literature on silicon nanoparticles and discusses the impact of silicon nanoparticles on several aspects of agricultural sciences. The review also discusses the future application of silicon nanoparticles in plant growth, plant development, and improvement of plant productivity.

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Application of silicon nanoparticles in agriculture

Delineation of groundwater potential zone for sustainable development: A case study from Ganga Alluvial Plain covering Hooghly district of India using remote sensing, geographic information system and analytic hierarchy process

Indoor air quality (IAQ), as determined by the concentrations of indoor air pollutants, can be predicted using either physically based mechanistic models or statistical models that are driven by measured data. In comparison with mechanistic models mostly used in unoccupied or scenario-based environments, statistical models have great potential to explore IAQ captured in large measurement campaigns or in real occupied environments. The present study carried out the first literature review of the use of statistical models to predict IAQ. The most commonly used statistical modeling methods were reviewed and their strengths and weaknesses discussed. Thirty-seven publications, in which statistical models were applied to predict IAQ, were identified. These studies were all published in the past decade, indicating the emergence of the awareness and application of machine learning and statistical modeling in the field of IAQ. The concentrations of indoor particulate matter (PM2.5 and PM10 ) were the most frequently studied parameters, followed by carbon dioxide and radon. The most popular statistical models applied to IAQ were artificial neural networks, multiple linear regression, partial least squares, and decision trees.

Machine learning and statistical models for predicting indoor air quality.

Groundwater is vulnerable to contamination by anthropological activities. Vulnerability mapping is considered as a fundamental aspect of groundwater management. The aim of this study was to estimate aquifer vulnerability by applying the DRASTIC and GOD models in Abarkooh plain, Yazd province, center of Iran. The DRASTIC model uses seven environmental parameters (depth to water, net recharge, aquifer media, soil media, topography, impact of vadose zone, and hydraulic conductivity) to characterize the hydrogeological setting and evaluate aquifer vulnerability. GOD is an overlay and index method designed to map groundwater vulnerability over large regions based on three parameters (groundwater confinement, overlying strata, and depth to groundwater). The information layers for models were provided via geographic information system. The overlap techniques were used to provide and produce the vulnerability map of the study areas considering weight coefficients of each layer. Accuracy of the models was evaluated using linear regression between observations values of nitrate and estimated vulnerability to pollution in the measured wells. A significant correlation was observed between measured nitrate and pollution potential evaluated by DRASTIC model (P < 0.01), but no significant correlation was observed for GOD model (P < 0.05). The results showed that the DRASTIC model is better than GOD model to estimate groundwater vulnerability to pollution in the measured wells. For DRASTIC model, the correlation coefficient between vulnerability index and nitrate concentration was 68 % that was substantially higher than 28 % obtained for the GOD model. We can conclude that nitrate concentration should be a suitable parameter to investigate the accuracy of the DRASTIC and GOD models.

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Assessing groundwater vulnerability to contamination in an arid environment using DRASTIC and GOD models

In arid and semi-arid environments, groundwater plays a significant role in the ecosystem. In the last decades, groundwater levels have decreased due to the increasing demand for water, weak irrigation management and soil damage. For the effective management of groundwater, it is important to model and predict fluctuations in groundwater levels. In this study, several time series models were applied to predict groundwater level forecasting in Kashan plain, Isfahan province, Iran. At first, to reduce the calculation volume, the water table depths in 36 piezometric wells were clustered based on the Vard algorithm. Consequently, we categorized the 36 wells into five clusters. For each cluster, five time series models of auto-regressive (AR), moving-average (MA), auto-regressive moving-average (ARMA), auto-regressive integrated moving-average (ARIMA) and seasonal auto-regressive integrated moving-average (SARIMA) were applied. The results showed that the AR model with a two-times lag (AR(2)), shows the best forecasting of groundwater level for 60 months ahead of the five clusters, with a high accuracy of R
 2 (0.89, 0.89, 0.95, 0.95 and 0.75 in clusters 1 to 5, respectively). According to the results, the average groundwater level fluctuation in 2010 and 2016 was 74.58 and 80.71 m, respectively. With these conditions, the groundwater depletion rate would be 1.02 m per year in 2016. We combined several time series models for a better performance of prediction of groundwater level. We can conclude that combining time series models have an advantage in terms of groundwater level forecasting.

A Stochastic Modelling Technique for Groundwater Level Forecasting in an Arid Environment Using Time Series Methods

Urban runoff increased due to augment of impervious surfaces. In order to flood mitigation during rainy season, determination of critical urban sub-catchments is very important for urban planners. Due to lack of information, adopting a simulation approach is one of the practical ways to identify the surcharged junctions and critical sub-catchments. Occurrence of destructive floods in the rainy seasons indicates the inappropriateness of the urban drainage system in Urmia. The main aims of this study were to estimate the surface runoff of urban sub-catchments using SWMM, to evaluate the accuracy of the drainage system of the study urban area and to prioritize sub-catchments using PROMETHEE II approach and SWMM. In the present study, the occurrence of rainfall event of the Urmia city (West Azerbaijan province, Iran) used for estimation of runoff depth. The study area was divided into 22 sub-catchments. For calibration and validation of model parameters, 3 rainfall events and their related runoff were measured. According to sensitivity analysis CN was the most sensitive parameter for model calibration. Amount of surcharged conduits and junctions indicates that the drainage system of the study area has not enough capacity for converting of the runoff and. For 10 year return period, depth of channels should increase by 20% for prevention of flooding in these sub-catchments. Sub-catchments were prioritized using PROMETHEE II approach and its results were compared with SWMM simulation outcomes. Based on SWMM simulation, S11, S7, S18, S16 and S1 sub-catchments are more critical sub-catchments respectively, while according to PROMETHEE method, S1, S11, S16, S14 and S18 are determined as the critical areas.

Urban flood simulation and prioritization of critical urban sub-catchments using SWMM model and PROMETHEE II approach

In arid and semiarid areas, bimodal and high rainfall leads to infrequent flood that can be extremely damaging. To reduce the impacts of persistent intra-seasonal drought and also to reduce flood damaging in arid and semiarid areas, rainwater storage is a prerequisite that keeps water far from evapotranspiration, increases groundwater level and decreases flood hazards modification to exchange between surface water and groundwater through flood spreading, dams, etc. The purpose of this paper is to delineate and explain variations in groundwater recharge and groundwater quality along an ephemeral stream that has been modified by flood spreading. Groundwater samples were collected from 14 deep wells located at different distances from flood spreading projection area (FSPA) in 1 month interval during September 2005 to September 2008. Groundwater quality was followed via Na+, K+, Ca2+, Mg2+, Cl-, Hco
 3
 -
 SO
 4
 2-
 , Electrical Conductivity (EC) and pH measurements for two time periods between 2005 and 2008. The results show significant impact of flood spreading in groundwater table and groundwater salinity variation. Groundwater table decreased in all study wells, but groundwater drawdown increased by increasing the distance to FSPA (during 4 years study, 11.02 m in the well located at 20 m of FSPA versus 38.88 in the well located at 1,825 m). Also ion concentration increased in all of the wells during the study period, but the increasing ion concentration was significantly less important in FSPA closeness.

Impact of Flood Spreading on Groundwater Level Variation and Groundwater Quality in an Arid Environment

Flood spreading (FS) is one of the suitable methods for flood management and water harvesting that increases the groundwater recharge, makes soil more fertile and increases nutrients in soil. It is also a method for reusing sediment, which is usually wasted. The purpose of this paper is to investigate the impact of flood spreading on physical and chemical soil properties (soil texture, infiltration rate, pH, EC, Na, P, K, Ca, Mg, Cl, HCO3, and SO4). It is examined that the soil properties change in the flood spreading projection area (FSP). The physico-chemical properties of soil and infiltration rate were measured in different soil depths at both flood spreading and control area. For the 20 cm of top soil, the amount of clay increased after the flood spreading implementation especially in the first and second dikes. Increasing clay was accompanied by decreasing soil infiltration and sand percentage. The mean differences of the clay, sand and infiltration rate between FSP and the control area were statistically significant (P < 0.01). A significant difference was not observed in 20–30 cm of the depth. Soil pH, Mg, HCO3, Cl and SO4 in different soil layers did not show any significant difference between the control and FSP. Soil EC in 0–20 cm depth of FSP and control area was showed a significant difference (P < 0.05) but no significant differences were found in deeper layers (P < 0.05). K, Na and Ca were remarkably different between 0 and 10 cm depths (P < 0.05) whereas no significant differences were found in deeper layers (P < 0.05). Comparison of the physico-chemical properties and infiltration rates between the dikes in the FSP shows that there are the significant differences between the medians of dike 1 with dikes 2, 3, 4 and 5, but the differences were not observed between dikes 3, 4 and 5. Our results show that the flood spreading operation can be influenced by the area that is under this operation. This study allowed us to investigate the mechanisms that regulate the infiltration rate and chemical soil properties throughout a seasonally flooded area.

Reza Ghazavi

Papers

Assessing groundwater vulnerability to contamination in an arid environment using DRASTIC and GOD models

A Stochastic Modelling Technique for Groundwater Level Forecasting in an Arid Environment Using Time Series Methods

Urban flood simulation and prioritization of critical urban sub-catchments using SWMM model and PROMETHEE II approach

Impact of Flood Spreading on Groundwater Level Variation and Groundwater Quality in an Arid Environment

Impact of Flood Spreading on Infiltration Rate and Soil Properties in an Arid Environment