Machine Learning Methods for Better Water Quality Prediction

In any aquatic system analysis, the modelling water quality parameters are of considerable significance. The traditional modelling methodologies are dependent on datasets that involve large amount of unknown or unspecified input data and generally consist of time-consuming processes. The implementation of artificial intelligence (AI) leads to a flexible mathematical structure that has the capability to identify non-linear and complex relationships between input and output data. There has been a major degradation of the Johor River Basin because of several developmental and human activities. Therefore, setting up of a water quality prediction model for better water resource management is of critical importance and will serve as a powerful tool. The different modelling approaches that have been implemented include: Adaptive Neuro-Fuzzy Inference System (ANFIS), Radial Basis Function Neural Networks (RBF-ANN), and Multi-Layer Perceptron Neural Networks (MLP-ANN). However, data obtained from monitoring stations and experiments are possibly polluted by noise signals as a result of random and systematic errors. Due to the presence of noise in the data, it is relatively difficult to make an accurate prediction. Hence, a Neuro-Fuzzy Inference System (WDT-ANFIS) based augmented wavelet de-noising technique has been recommended that depends on historical data of the water quality parameter. In the domain of interests, the water quality parameters primarily include ammoniacal nitrogen (AN), suspended solid (SS) and pH. In order to evaluate the impacts on the model, three evaluation techniques or assessment processes have been used. The first assessment process is dependent on the partitioning of the neural network connection weights that ascertains the significance of every input parameter in the network. On the other hand, the second and third assessment processes ascertain the most effectual input that has the potential to construct the models using a single and a combination of parameters, respectively. During these processes, two scenarios were introduced: Scenario 1 and Scenario 2. Scenario 1 constructs a prediction model for water quality parameters at every station, while Scenario 2 develops a prediction model on the basis of the value of the same parameter at the previous station (upstream). Both the scenarios are based on the value of the twelve input parameters. The field data from 2009 to 2010 was used to validate WDT-ANFIS. The WDT-ANFIS model exhibited a significant improvement in predicting accuracy for all the water quality parameters and outperformed all the recommended models. Also, the performance of Scenario 2 was observed to be more adequate than Scenario 1, with substantial improvement in the range of 0.5% to 5% for all the water quality parameters at all stations. On validating the recommended model, it was found that the model satisfactorily predicted all the water quality parameters (R2 values equal or bigger than 0.9). © 201

Toward bridging future irrigation deficits utilizing the shark algorithm integrated with a climate change model

Climate change is one of the most effectual variables on the dam operations and reservoir water system. This is due to the fact that climate change has a direct effect on the rainfall–runoff process that is influencing the water inflow to the reservoir. This study examines future trends in climate change in terms of temperature and precipitation as an important predictor to minimize the gap between water supply and demand. In this study, temperature and precipitation were predicted for the period between 2046 and 2065, in the context of climate change, based on the A1B scenario and the HAD-CM3 model. Runoff volume was then predicted with the IHACRES model. A new, nature-inspired optimization algorithm, named the shark algorithm, was examined. Climate change model results were utilized by the shark algorithm to generate an optimal operation rule for dam and reservoir water systems to minimize the gap between water supply and demand for irrigation purposes. The proposed model was applied for the Aydoughmoush Dam in Iran. Results showed that, due to the decrease in water runoff to the reservoir and the increase in irrigation demand, serious irrigation deficits could occur downstream of the Aydoughmoush Dam

Potential Role of Plant Growth Regulators in Administering Crucial Processes Against Abiotic Stresses

Plant growth regulators are naturally biosynthesized chemicals in plants that influence physiological processes. Their synthetic analogous trigger numerous biochemical and physiological processes involved in the growth and development of plants. Nowadays, due to changing climatic scenario, numerous biotic and abiotic stresses hamper seed germination, seedling growth, and plant development leading to a decline in biological and economic yields. However, plant growth regulators (PGRs) can potentially play a fundamental role in regulating plant responses to various abiotic stresses and hence, contribute to plant adaptation under adverse environments. The major effects of abiotic stresses are growth and yield disturbance, and both these effects are directly overseen by the PGRs. Different types of PGRs such as abscisic acid (ABA), salicylic acid (SA), ethylene (ET), and jasmonates (JAs) are connected to boosting the response of plants to multiple stresses. In contrast, PGRs including cytokinins (CKs), gibberellins (GAs), auxin, and relatively novel PGRs such as strigolactones (SLs), and brassinosteroids (BRs) are involved in plant growth and development under normal and stressful environmental conditions. Besides, polyamines and nitric oxide (NO), although not considered as phytohormones, have been included in the current review due to their involvement in the regulation of several plant processes and stress responses. These PGRs are crucial for regulating stress adaptation through the modulates physiological, biochemical, and molecular processes and activation of the defense system, upregulating of transcript levels, transcription factors, metabolism genes, and stress proteins at cellular levels. The current review presents an acumen of the recent progress made on different PGRs to improve plant tolerance to abiotic stress such as heat, drought, salinity, and flood. Moreover, it highlights the research gaps on underlying mechanisms of PGRs biosynthesis under stressed conditions and their potential roles in imparting tolerance against adverse effects of suboptimal growth conditions.Fil: Sabagh, Ayman EL. Kafrelsheikh University; EgiptoFil: Mbarki, Sonia. National Institute Of Research In Rural Engineering; TúnezFil: Hossain, Akbar. Bangladesh Agricultural Research Institute; BangladeshFil: Iqbal, Muhammad Aamir. University Of Poonch Rawalakot; PakistánFil: Islam, Mohammad Sohidul. Hajee Mohammad Danesh And Technology University; BangladeshFil: Raza, Ali. Fujian Agriculture And Forestry University; ChinaFil: Llanes, Analia Susana. Universidad Nacional de Rio Cuarto. Facultad de Cs.exactas Fisicoquimicas y Naturales. Instituto de Investigaciones Agrobiotecnologicas. - Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - Cordoba. Instituto de Investigaciones Agrobiotecnologicas.; ArgentinaFil: Reginato, Mariana Andrea. Universidad Nacional de Rio Cuarto. Facultad de Cs.exactas Fisicoquimicas y Naturales. Instituto de Investigaciones Agrobiotecnologicas. - Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - Cordoba. Instituto de Investigaciones Agrobiotecnologicas.; ArgentinaFil: Rahman, Md Atikur. Grassland And Forage Division National Institute; Corea del SurFil: Mahboob, Wajid. Nuclear Institute Of Agriculture,; PakistánFil: Singhal, Rajesh Kumar. Indian Council Of Agricultural Research; IndiaFil: Kumari, Arpna. Guru Nanak Dev University; IndiaFil: Rajendran, Arvind. Vellore Institute Of Technology; IndiaFil: Wasaya, Allah. Bahauddin Zakariya University; PakistánFil: Javed, Talha. Fujian Agriculture And Forestry University; JapónFil: Shabbir, Rubab. University Of Poonch Rawalakot; PakistánFil: Rahim, Junaid. University Of Çukurova; PakistánFil: Barutçular, Celaleddin. Institute Of Crop Science And Resource Conservation; AlemaniaFil: Habib Ur Rahman, Muhammad. Sichuan Agricultural University; ChinaFil: Raza, Muhammad Ali. Sichuan Agricultural University; ChinaFil: Ratnasekera, Disna. University Of Ruhuna; Sri LankaFil: Konuskan l, Ömer. Mustafa Kemal University; TurquíaFil: Hossain, Mohammad Anwar. Bangladesh Agricultural Research Institute; BangladeshFil: Meena, Vijay Singh. Indian Council Of Agricultural Research; IndiaFil: Ahmed, Sharif. Bangladesh Agricultural Research Institute; BangladeshFil: Ahmad, Zahoor. Bangladesh Wheat And Maize Research Institute; BangladeshFil: Mubeen, Muhammad. Sichuan Agricultural University; ChinaFil: Singh, Kulvir. Punjab Agricultural University; IndiaFil: Skalicky, Milan. Czech University Of Life Sciences Prague; República ChecaFil: Brestic, Marian. Slovak University Of Agriculture; EslovaquiaFil: Sytar, Oksana. Slovak University Of Agriculture; EsloveniaFil: Karademir, Emine. Siirt University; TurquíaFil: Karademir, Cetin. Siirt University; TurquíaFil: Erman, Murat. Siirt University; TurquíaFil: Farooq, Muhammad. College Of Agricultural And Marine Sciences Sultan; Omá

Geometry Dependent Optimal Bounds of Absorption and Scattering Cross-section for Small Nanoparticles

Light-matter interaction in particles of subwavelength size mostly depends on the size, shape, composition of the particles and relies on the properties of incident light. Henceforth, resonant behavior of light can be effectively controlled by changing the aforesaid geometrical parameters. The aim of this study is to investigate the effect of size, shape, and material property dependency on the optimal bounds of scattering and absorption cross-section of nanoparticles. Mie theory is used here to solve Maxwell’s equations of light scattering from nanoparticles of different shape to calculate both scattering and absorption cross-sections of silicon and gold nanoparticles. In this work, numerical analysis based on Finite-Difference Time-Domain (FDTD) method is performed to study the optical properties of different size and shapes of silicon and gold nanoparticles such as sphere, pyramid, ring, and cubical structures. In particular, this study reveals how the resonant behaviour (magnitude and peak position) of light varies in accordance with the change in size, shape and material of a specific particle structured in nanoscale. In conclusion, we can quantify the efficiency of a small absorbing or scattering medium and propose structures suitable for implementation in inverse design applications.

Geometry Dependent Optimal Bounds of Absorption and Scattering Cross-section for Small Nanoparticles

Light-matter interaction in particles of subwavelength size mostly depends on the size, shape, composition of the particles and relies on the properties of incident light. Henceforth, resonant behavior of light can be effectively controlled by changing the aforesaid geometrical parameters. The aim of this study is to investigate the effect of size, shape, and material property dependency on the optimal bounds of scattering and absorption cross-section of nanoparticles. Mie theory is used here to solve Maxwell’s equations of light scattering from nanoparticles of different shape to calculate both scattering and absorption cross-sections of silicon and gold nanoparticles. In this work, numerical analysis based on Finite-Difference Time-Domain (FDTD) method is performed to study the optical properties of different size and shapes of silicon and gold nanoparticles such as sphere, pyramid, ring, and cubical structures. In particular, this study reveals how the resonant behaviour (magnitude and peak position) of light varies in accordance with the change in size, shape and material of a specific particle structured in nanoscale. In conclusion, we can quantify the efficiency of a small absorbing or scattering medium and propose structures suitable for implementation in inverse design applications.

Operating a reservoir system based on the shark machine learning algorithm

The operating process of a multi-purpose reservoir needs to develop models that have the ability to overcome the challenges facing the decision makers. Therefore, the development of a mathematical optimization model is crucial for selecting the optimal policies for the reservoir operation. In the current study, the shark machine learning algorithm (SMLA) is proposed to develop an optimal rule for operating the reservoir. The SMLA began with a group of randomly produced potential solutions and later interactively executed the search for the optimal solution. The procedure for the SMLA is suitable to be applied to a reservoir system due to its ability to tackle the stochastic features of dam and reservoir systems. The major purpose of the proposed models is to generate an operation rule that could minimize the absolute value of the differences between water release and water demand. The proposed model has been examined using the data of the Aswan High Dam, Egypt as the case study. The performance of the SMLA was compared with the performance of the most widespread evolutionary algorithms, namely, the genetic algorithm (GA). Comprehensive analysis of the results was performed using three performance indicators, namely, resilience, reliability, and vulnerability. This work concluded that the performance of the SMLA model was better than the GA model in generating the optimal policy for reservoir operation. The result showed that the SMLA succeeded in providing high reliability (99.72%), significant resilience (1) and minimum vulnerability (20.7% of demand)

Determination of biochemical oxygen demand and dissolved oxygen for semi-arid river environment: application of soft computing models

Surface and ground water resources are highly sensitive aquatic systems to contaminants due to their accessibility to multiple-point and non-point sources of pollutions. Determination of water quality variables using mathematical models instead of laboratory experiments can have venerable significance in term of the environmental prospective. In this research, application of a new developed hybrid response surface method (HRSM) which is a modified model of the existing response surface model (RSM) is proposed for the first time to predict biochemical oxygen demand (BOD) and dissolved oxygen (DO) in Euphrates River, Iraq. The model was constructed using various physical and chemical variables including water temperature (T), turbidity, power of hydrogen (pH), electrical conductivity (EC), alkalinity, calcium (Ca), chemical oxygen demand (COD), sulfate (SO 4 ), total dissolved solids (TDS), and total suspended solids (TSS) as input attributes. The monthly water quality sampling data for the period 2004–2013 was considered for structuring the input-output pattern required for the development of the models. An advance analysis was conducted to comprehend the correlation between the predictors and predictand. The prediction performances of HRSM were compared with that of support vector regression (SVR) model which is one of the most predominate applied machine learning approaches of the state-of-the-art for water quality prediction. The results indicated a very optimistic modeling accuracy of the proposed HRSM model to predict BOD and DO. Furthermore, the results showed a robust alternative mathematical model for determining water quality particularly in a data scarce region like Iraq

Additional file 1: of Antioxidant, antidiarrheal, hypoglycemic and thrombolytic activities of organic and aqueous extracts of Hopea odorata leaves and in silico PASS prediction of its isolated compounds

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