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2021 ◽  
Vol 1197 (1) ◽  
pp. 012021
Author(s):  
Preeti S. Kulkarni ◽  
Shreenivas Londhe ◽  
Nikita Sainkar ◽  
Sayali Rote

Abstract A reservoir operation planning using Data driven Techniques is gaining its momentum in hydrological area with good prediction and Estimation capabilities. The present work aims at using the 5 years data of Water Level to estimate the discharge and water level at the Yedgaon dam which is like pick up weir having its own yield and storage. It receives water from Dimbhe (though DLBC), Wadaj (through MLBC), Manikdoh (through river) and through Pimpalgaojoge (through river), in the Kukadi project of Maharashtra State, India. 4 different models were developed to estimate the water level using the Data Driven Techniques: M5 Model Tree, Support Vector Regression, Multi Gene Genetic Programming and Random Forest. The Accuracy of the developed models is assessed by the values of coefficient of correlation, coefficient of efficiency, mean absolute error and root mean squared error and comparison is done between actual values and Predicted values. The results indicated that the MGGP model was superior as compared to other techniques with correlation coefficient as 0.86 with an advantage of a single equation to estimate the water level.


2021 ◽  
Author(s):  
Vahdettin DEMIR

Abstract This paper investigates the accuracy of three different techniques with periodicity component for estimation of monthly lake levels. The compared methods are Least Square Support Vector Regression (LSSVR) Multivariate Adaptive Regression Splines (MARS) and M5 Model Tree (M5-Tree). Data from Lake Michigan, located in the USA, is used in the analysis. In the first stage of the study, three different techniques were applied to forecast monthly lake-levels variations up to 8- mount ahead of time intervals. In the second stage, the influence of the periodicity component was applied (month number of the year, e.g., 1, 2, 3, …12) as an external sub-set in modeling monthly lake levels. The Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R2) were utilized are used for evaluating the accuracy of models. In both stages, the comparison results indicate that the MARS model generally performs superior to the LSSVR, and M5-Tree models. Furthermore, it has been discovered that including periodicity as an input to the models improves their accuracy in projecting monthly lake levels.


2021 ◽  
Author(s):  
Dilip Kumar Roy ◽  
Kowshik Kumar Saha ◽  
Mohammad Kamruzzaman ◽  
Sujit Kumar Biswas ◽  
Mohammad Anower Hossain

Abstract Reference evapotranspiration (ET0) is a crucial element for deriving a meaningful scheduling of irrigation for major crops. Thus, precise projection of future ET0 is essential for better management of scarce water resources in many parts of the globe. This study evaluates the potential of a Hierarchical Fuzzy System (HFS) optimized by Particle Swarm Optimization (PSO) algorithm (PSO-HFS) to predict daily ET0. The meteorological variables and estimated ET0 were employed as inputs and outputs, respectively, for the PSO-HFS model. The FAO 56 PM method to ET0 computation was implemented to obtain ET0 values using the climatic variables obtained from two weather stations located in Gazipur Sadar and Ishurdi, Bangladesh. Prediction accuracy of PSO-HFS was compared with that of a FIS, M5 Model Tree, and a Regression Tree (RT) model. Several statistical performance evaluation indices were used to evaluate the performances of the PSO-HFS, FIS, M5 Model Tree, and RT in estimating daily ET0. Ranking of the models was performed using the concept of Shannon’s Entropy that accounts for a set of performance evaluation indices. Results revealed that the PSO-HFS model performed better than the tree-based models. Generalization capabilities of the preposed models were evaluated using the dataset from a test station (Ishurdi station). Results revealed that the models performed equally well with the unseen test dataset, and that the PSO-HFS model provided superior performance over other tree based models. The overall results imply that PSO-HFS model could effectively be utilized to model ET0 values quite efficiently and accurately.


2021 ◽  
Vol 13 (13) ◽  
pp. 2569
Author(s):  
Pingyi Dong ◽  
Lei Liu ◽  
Shulei Li ◽  
Shuai Hu ◽  
Lingbing Bu

This article presents a new method for retrieving the Ice Water Path (IWP), the median volume equivalent sphere diameter (Dme) of thin ice clouds (IWP < 100 g/m2, Dme < 80 μm) in the Terahertz band. The upwelling brightness temperature depressions caused by the ice clouds at 325.15, 448.0, 664.0 and 874.0 GHz channels are simulated by the Atmospheric Radiative Transfer Simulator (ARTS). The simulated forward radiative transfer models are taken as historical data for the M5 model tree algorithm to construct a set of piecewise functions which represent the relation of simulated brightness temperature depressions and IWP. The inversion results are optimized by an empirical relation of the IWP and the Dme for thin ice clouds which is summarized by previous studies. We inverse IWP and Dme with the simulated brightness temperature and analyze the inversion performance of selected channels. The 874.4 ± 6.0 GHz channel provides the most accurate results, because of the strong brightness temperature response to the change of IWP in the forward radiative transfer model. In order to improve the thin ice clouds IWP and Dme retrieval accuracy at the middle-high frequency channels in Terahertz band, a dual-channel inversion method was proposed that combines the 448.0 ± 3.0 GHz and 664.0 ± 4.2 GHz channel. The error analysis shows that the results of the 874.4 ± 6.0 GHz channel and the dual-channel inversion are reliable, and the IWP inversion results meet the error requirement range proposed by previous studies.


2021 ◽  
Author(s):  
Muhammad Waqas ◽  
Muhammad Saifullah ◽  
Sarfraz Hashim ◽  
Mohsin Khan ◽  
Sher Muhammad

The forecasting plays key role for the water resources planning. Most suitable technique is Artificial intelligence techniques (AITs) for different parameters of weather forecasting and generated runoff. The study compared AITs (RBF-SVM and M5 model tree) to understand the rainfall runoff process in Jhelum River Basin, Pakistan. The rainfall and runoff of Jhelum river used from 1981 to 2012. The Different rainfall and runoff dataset combinations were used to train and test AITs. The data record for the period 1981–2001 used for training and then testing. After training and testing, modeled runoff and observed data was evaluated using R2, NRMSE, COE and MSE. During the training, the dataset C2 and C3 were found to be 0.71 for both datasets using M5 model. Similar results were found for dataset of C3 using RBF-SVM. Over all, C3 and C7 were performed best among all the dataset. The M5 model tree was performed better than other applied techniques. GEP has also exhibited good results to understand rainfall runoff process. The RBF-SVM performed less accurate as compare to other applied techniques. Flow duration curve (FDCs) were used to compare the modeled and observed dataset of Jhelum River basin. For High flow and medium high flows, GEP exhibited well. M5 model tree displayed the better results for medium low and low percentile flows. RBF-SVM exhibited better for low percentile flows. GEP were found the accurate and highly efficient DDM among the AITs applied techniques. This study will help understand the complex rainfall runoff process, which is stochastic process. Weather forecasting play key role in water resources management and planning.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yun Yang ◽  
Qinfang Cui ◽  
Peng Jia ◽  
Jinbao Liu ◽  
Han Bai

AbstractA precise estimation of the heavy metal concentrations in soils using multispectral remote sensing technology is challenging. Herein, Landsat8 imagery, a digital elevation model, and geochemical data derived from soil samples are integrated to improve the accuracy of estimating the Cu, Pb, and As concentrations in topsoil, using the Daxigou mining area in Shaanxi Province, China, as a case study. The relationships between the three heavy metals and soil environmental factors were investigated. The optimal combination of factors associated with the elevated concentrations of each heavy metal was determined combining correlation analysis with collinearity tests. A back propagation network optimised using a genetic algorithm was trained with 80% of the data for samples and subsequently employed to estimate the heavy metal concentrations in the area. The validation results show that the RMSE of the proposed model is lower than those of the existing linear model and rule-based M5 model tree. From the spatial distribution map of the three metals concentrations using the proposed method, there are findings that high concentrations of the heavy metals studied occur in the mining area, across the slag storage area, on the sides of the road used for transporting ore materials, and along the base of slopes in the area. These findings are consistent with the survey results in the field. The validation and findings validate the effectiveness of the proposed method.


Author(s):  
Ozgur Kisi ◽  
Behrooz Keshtegar ◽  
Mohammad Zounemat-Kermani ◽  
Salim Heddam ◽  
Nguyen-Thoi Trung

2021 ◽  
Author(s):  
Ozgur Kisi ◽  
Behrooz Keshtegar ◽  
Mohammad Zounemat-Kermani ◽  
Salim Heddam ◽  
Nguyen-Thoi Trung

Abstract In the current study, an ability of a novel regression-based method is evaluated in modelling daily reference evapotranspiration (ET0), which is an important issue in water resources management plans and helps farmers in irrigation planning. The method was developed by hybridizing radial basis function and M5 model tree and called as radial basis M5 model tree (RM5Tree). The radial-based kernel function was used to control the input variables in modelling process of M5 model tree. The new model results were compared with traditional M5 model tree (M5Tree), response surface method (RSM) and two neural networks (multi-layer perceptron neural networks, MLPNN & radial basis function neural network, RBFNN) with respect to several statistical indices. Daily climatic data (relative humidity, RH, solar radiation, SR, wind speed, air temperature, T) recorded at three stations in Turkey, Mediterranean Region, were used. The effect of each weather data on ET0 was also investigated by utilizing three different input scenarios with various combinations of input variables. On the whole, the RM5Tree provided the best results (NSE > 0.997) followed by the MLPNN (NSE > 0.990), and M5Tree (NSE > 0.945) in modelling daily ET0. The SR was observed as the most effective input parameter on ET0 which was followed by the T and RH. However, the findings of the third modelling scenario revealed that taking into account of all variables would considerably increase models’ accuracies for the three stations.


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