scholarly journals Estimating Baseflow and Baseflow Index in Ungauged Basins Using Spatial Interpolation Techniques: A Case Study of the Southern River Basin of Thailand

Water ◽  
2021 ◽  
Vol 13 (21) ◽  
pp. 3113
Author(s):  
Pakorn Ditthakit ◽  
Sarayod Nakrod ◽  
Naunwan Viriyanantavong ◽  
Abebe Debele Tolche ◽  
Quoc Bao Pham
Keyword(s):  
Spatial Interpolation ◽  
Water Resource Management ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Filter Method ◽  
Ungauged Basins ◽  
Calibration And Validation ◽  
Distance Weighting ◽  
Interpolation Techniques

This research aims to estimate baseflow (BF) and baseflow index (BFI) in ungauged basins in the southern part of Thailand. Three spatial interpolation methods (namely, inverse distance weighting (IDW), kriging, and spline) were utilized and compared in regard to their performance. Two baseflow separation methods, i.e., the local minimum method (LM) and the Eckhardt filter method (EF), were investigated. Runoff data were collected from 65 runoff stations. These runoff stations were randomly selected and divided into two parts: 75% and 25% for the calibration and validation stages, respectively, with a total of 36 study cases. Four statistical indices including mean absolute error (MAE), root mean squared error (RMSE), correlation coefficient (r), and combined accuracy (CA), were applied for the performance evaluation. The findings revealed that monthly and annual BF and BFI calculated by EF were mostly lower than those calculated by LM. Furthermore, IDW gave the best performance among the three spatial interpolation techniques by providing the highest r-value and the lowest MAE, RMSE, and CA values for both the calibration and validation stages, followed by kriging and spline, respectively. We also provided monthly and annual BF and BFI maps to benefit water resource management.

scholarly journals Random Forest Spatial Interpolation

2020 ◽  
Vol 12 (10) ◽  
pp. 1687 ◽  
Author(s):  
Aleksandar Sekulić ◽  
Milan Kilibarda ◽  
Gerard B.M. Heuvelink ◽  
Mladen Nikolić ◽  
Branislav Bajat
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Case Studies ◽  
Spatial Interpolation ◽  
Inverse Distance Weighting ◽  
Regression Kriging ◽  
Distance Weighting ◽  
Interpolation Techniques ◽  
Inverse Distance

For many decades, kriging and deterministic interpolation techniques, such as inverse distance weighting and nearest neighbour interpolation, have been the most popular spatial interpolation techniques. Kriging with external drift and regression kriging have become basic techniques that benefit both from spatial autocorrelation and covariate information. More recently, machine learning techniques, such as random forest and gradient boosting, have become increasingly popular and are now often used for spatial interpolation. Some attempts have been made to explicitly take the spatial component into account in machine learning, but so far, none of these approaches have taken the natural route of incorporating the nearest observations and their distances to the prediction location as covariates. In this research, we explored the value of including observations at the nearest locations and their distances from the prediction location by introducing Random Forest Spatial Interpolation (RFSI). We compared RFSI with deterministic interpolation methods, ordinary kriging, regression kriging, Random Forest and Random Forest for spatial prediction (RFsp) in three case studies. The first case study made use of synthetic data, i.e., simulations from normally distributed stationary random fields with a known semivariogram, for which ordinary kriging is known to be optimal. The second and third case studies evaluated the performance of the various interpolation methods using daily precipitation data for the 2016–2018 period in Catalonia, Spain, and mean daily temperature for the year 2008 in Croatia. Results of the synthetic case study showed that RFSI outperformed most simple deterministic interpolation techniques and had similar performance as inverse distance weighting and RFsp. As expected, kriging was the most accurate technique in the synthetic case study. In the precipitation and temperature case studies, RFSI mostly outperformed regression kriging, inverse distance weighting, random forest, and RFsp. Moreover, RFSI was substantially faster than RFsp, particularly when the training dataset was large and high-resolution prediction maps were made.

scholarly journals Smooth regional estimation of low-flow indices: physiographical space based interpolation and top-kriging

2011 ◽  
Vol 15 (3) ◽  
pp. 715-727 ◽  
Author(s):  
S. Castiglioni ◽  
A. Castellarin ◽  
A. Montanari ◽  
J. O. Skøien ◽  
G. Laaha ◽  
...  
Keyword(s):  
Spatial Interpolation ◽  
Central Italy ◽  
Low Flow ◽  
Flow Index ◽  
Ungauged Basins ◽  
Low Flows ◽  
Interpolation Techniques ◽  
Point Data ◽  
Leave One Out ◽  
Low Flow Index

Abstract. Recent studies highlight that spatial interpolation techniques of point data can be effectively applied to the problem of regionalization of hydrometric information. This study compares two innovative interpolation techniques for the prediction of low-flows in ungauged basins. The first one, named Physiographical-Space Based Interpolation (PSBI), performs the spatial interpolation of the desired streamflow index (e.g., annual streamflow, low-flow index, flood quantile, etc.) in the space of catchment descriptors. The second technique, named Topological kriging or Top-kriging, predicts the variable of interest along river networks taking both the area and nested nature of catchments into account. PSBI and Top-kriging are applied for the regionalization of Q355 (i.e., a low-flow index that indicates the streamflow that is equalled or exceeded 355 days in a year, on average) over a broad geographical region in central Italy, which contains 51 gauged catchments. The two techniques are cross-validated through a leave-one-out procedure at all available gauges and applied to a subregion to produce a continuous estimation of Q355 along the river network extracted from a 90m elevation model. The results of the study show that Top-kriging and PSBI present complementary features. Top-kriging outperforms PSBI at larger river branches while PSBI outperforms Top-kriging for headwater catchments. Overall, they have comparable performances (Nash-Sutcliffe efficiencies in cross-validation of 0.89 and 0.83, respectively). Both techniques provide plausible and accurate predictions of Q355 in ungauged basins and represent promising opportunities for regionalization of low-flows.

Electricity Consumption Analysis Using Demographic Variables Case Study – Nakhonratchasima, Thailand

2013 ◽  
Vol 734-737 ◽  
pp. 1679-1682
Author(s):  
Sureeporn Meehom ◽  
Nopphadon Khodpun
Keyword(s):  
High Speed ◽  
Mean Squared Error ◽  
Mean Absolute Error ◽  
Electricity Consumption ◽  
Absolute Error ◽  
Demographic Variables ◽  
Resources Management ◽  
Squared Error ◽  
High Speed Diesel

Electricity energy is vital in social and economic for nation development. The electricity consumption analysis plays an important role for sustainable energy and electricity resources management in the future. In this paper, the influence of demographical variables on the annual electricity consumption in Nakhonratchasima has been investigated by multiple regression analysis. It is founded that the electricity consumption correlated with four demographic variables, which are the number of electricity consumers, the amount of high speed diesel usages, the number of industrial factory and the number of employed labor force. The historical electricity consumption and all variables for the period 20022010 have been analyzed in 8 models for electricity prediction in 2011. In conclusion, the effective model has been selected by comparison of adjusted R2, mean absolute error (MAE) and root mean squared error (RMSE) of the proposed models. Model 8 is acceptable in relation to electricity consumption analysis with adjusted-R2, RMSE and MAE equal to 0.9980, 0.7540% and 0.6095% respectively. The results indicate that the model using all four variables has strong ability to predict future annual electricity consumption with 4,195,837,877 kWh in 2011.

scholarly journals Predicting Fire Brigades Operational Breakdowns: A Real Case Study

Mathematics ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 1383 ◽  
Author(s):  
Selene Cerna ◽  
Christophe Guyeux ◽  
Guillaume Royer ◽  
Céline Chevallier ◽  
Guillaume Plumerel
Keyword(s):  
Public Service ◽  
Mean Squared Error ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Machine Learning Techniques ◽  
Fire Departments ◽  
Squared Error ◽  
Learning Techniques ◽  
Over Time

Over the years, fire departments have been searching for methods to identify their operational disruptions and establish strategies that allow them to efficiently organize their resources. The present work develops a methodology for breakage calculation and another for predicting disruptions based on machine learning techniques. The main objective is to establish indicators to identify the failures due to the temporal state of the organization in the human and vehicular material. Likewise, by forecasting disruptions, to determine strategies for the deployment or acquisition of the necessary armament. This would allow improving operational resilience and increasing the efficiency of the firemen over time. The methodology was applied to the Departmental Fire and Rescue Doubs (SDIS25) in France. However, it is generic enough to be extended and adapted to other fire departments. Considering a historic of breakdowns of 2017 and 2018, the best predictions of public service breakdowns for the year 2019, presented a root mean squared error of 2.5602 and a mean absolute error of 2.0240 on average with the XGBoost technique.

scholarly journals Assessing the Immediate Effect of Covid-19 Lockdown on Air Quality: A Case Study of Delhi, India

2020 ◽  
Vol 13 (3-4) ◽  
pp. 27-33
Author(s):  
Ankit Sikarwar ◽  
Ritu Rani
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Spatial Interpolation ◽  
World Health ◽  
Guideline Values ◽  
Distance Weighting ◽  
Health Organization ◽  
The City ◽  
Inverse Distance

Abstract In India, a nationwide lockdown due to COVID-19 has been implemented on 25 March 2020. The lockdown restrictions on more than 1.3 billion people have brought exceptional changes in the air quality all over the country. This study aims to analyze the levels of three major pollutants: particulate matter sized 2.5 μm (PM2.5) and 10 μm (PM10), and nitrogen dioxide (NO2) before and during the lockdown in Delhi, one of the world’s most polluted cities. The data for PM2.5, PM10, and NO2 concentrations are derived from 38 ground stations dispersed within the city. The spatial interpolation maps of pollutants for two times are generated using Inverse Distance Weighting (IDW) model. The results indicate decreasing levels of PM2.5, PM10, and NO2 concentrations in the city by 93%, 83%, and 70% from 25 February 2020 to 21 April 2020 respectively. It is found that one month before the lockdown the levels of air pollution in Delhi were critical and much higher than the guideline values set by the World Health Organization. The levels of air pollution became historically low after the lockdown. Considering the critically degraded air quality for decades and higher morbidity and mortality rate due to unhealthy air in Delhi, the improvement in air quality due to lockdown may result as a boon for the better health of the city’s population.

scholarly journals Assessing the immediate effect of COVID-19 lockdown on air quality: A case study of Delhi, India

2020 ◽  
Author(s):  
Ankit Sikarwar ◽  
Ritu Rani
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Spatial Interpolation ◽  
World Health ◽  
Guideline Values ◽  
Distance Weighting ◽  
Health Organization ◽  
The City ◽  
Inverse Distance

Abstract In India, the nationwide lockdown due to COVID-19 has been implemented on 25 March 2020. The lockdown restrictions on more than 1.3 billion people have brought exceptional changes in the air quality all over the country. This study aims to analyze the levels of three major pollutants (PM2.5, PM10, and NO2) before and during the lockdown in Delhi, one of the world’s most polluted cities. The data for PM2.5, PM10, and NO2 concentrations are derived from 38 ground stations dispersed within the city. The spatial interpolation maps of pollutants for two times are generated using Inverse Distance Weighting (IDW) model. The results indicate the lowering of PM2.5, PM10, and NO2 concentrations in the city by 93%, 83%, and 70% from 25 February 2020 to 21 April 2020 respectively. It is found that before one month of the lockdown the levels of air pollution in Delhi were critically high and far beyond the guideline values set by the World Health Organization. The levels of air pollution are historically low after the lockdown. Considering the critically degraded air quality for decades and higher morbidity and mortality rate due to unhealthy air in Delhi, the improvement in air quality due to lockdown may result as a boon for the better health of the city’s population.

scholarly journals Spatial Interpolation of Daily Rainfall Data for Local Climate Impact Assessment over Greater Sydney Region

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Xihua Yang ◽  
Xiaojin Xie ◽  
De Li Liu ◽  
Fei Ji ◽  
Lin Wang
Keyword(s):  
Spatial Interpolation ◽  
Mean Squared Error ◽  
Daily Rainfall ◽  
Climate Impact ◽  
Rainfall Data ◽  
Rainfall Erosivity ◽  
Local Climate ◽  
Erosion Risk ◽  
Daily Rainfall Data ◽  
Interpolation Techniques

This paper presents spatial interpolation techniques to produce finer-scale daily rainfall data from regional climate modeling. Four common interpolation techniques (ANUDEM, Spline, IDW, and Kriging) were compared and assessed against station rainfall data and modeled rainfall. The performance was assessed by the mean absolute error (MAE), mean relative error (MRE), root mean squared error (RMSE), and the spatial and temporal distributions. The results indicate that Inverse Distance Weighting (IDW) method is slightly better than the other three methods and it is also easy to implement in a geographic information system (GIS). The IDW method was then used to produce forty-year (1990–2009 and 2040–2059) time series rainfall data at daily, monthly, and annual time scales at a ground resolution of 100 m for the Greater Sydney Region (GSR). The downscaled daily rainfall data have been further utilized to predict rainfall erosivity and soil erosion risk and their future changes in GSR to support assessments and planning of climate change impact and adaptation in local scale.

Comparison of Quantitative Precipitation Estimation Using Spatial Interpolation methods in Bukhan River Basin

2020 ◽  
Author(s):  
Sanghoo Yoon ◽  
Junseok Kim ◽  
Taeyong Kwon
Keyword(s):  
Spatial Interpolation ◽  
Mean Squared Error ◽  
Additive Model ◽  
Absolute Error ◽  
Interpolation Methods ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation ◽  
Weight Method ◽  
Inverse Distance ◽  
Spatial Interpolation Methods

<p>Quantitative precipitation estimation is needed to reduce damages from weather disasters such as torrential rain. This study is dealt with estimates of the quantitative precipitation using multiple spatial interpolation methods and compares the results. Inverse distance weight method and k-nearest neighborhood algorithm were considered as a deterministic approach and the general additive model and kriging methods were used as a stochastic approach. To evaluate the prediction performance, leave-one-out cross-validation was performed with the root mean squared error (RMSE), mean absolute error (MAE), bias, and correlation coefficient. The research data were rain gauged and radar data in the Bukhan river, which were collected from May 2018 to August 2019. The results showed that the inverse distance weight method reflected the spatial rainfall characteristics well. However, caution is needed because the best models vary depending on the pattern of rainfall in the sense of RMSE.</p><p>*This work was supported by KOREA HYDRO & NUCLEAR POWER CO., LTD(No. 2018-Tech-20)</p>

scholarly journals Improvement of e-commerce recommendation systems with deep hybrid collaborative filtering with content: A case study

Econometrics ◽  
2020 ◽  
Vol 24 (3) ◽  
pp. 37-50
Author(s):  
Filip Wójcik ◽  
Michał Górnik
Keyword(s):  
Collaborative Filtering ◽  
Network Architecture ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Percentage Error ◽  
P Value ◽  
Hybrid Collaborative Filtering ◽  
Post Hoc ◽  
Better Than

This paper presents a proposition to utilize flexible neural network architecture called Deep Hybrid Collaborative Filtering with Content (DHCF) as a product recommendation engine. Its main goal is to provide better shopping suggestions for customers on the e-commerce platform. The system was tested on 2018 Amazon Reviews Dataset, using repeated cross validation and compared with other approaches: collaborative filtering (CF) and deep collaborative filtering (DCF) in terms of mean squared error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). DCF and DHCF were proved to be significantly better than the CF. DHCF proved to be better than DCF in terms of MAE and MAPE, it also scored the best on separate test data. The significance of the differences was checked by means of a Friedman test, followed by post-hoc comparisons to control p-value. The experiment shows that DHCF can outperform other approaches considered in the study, with more robust scores

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