PMID- 32007881
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20200303
LR  - 20200303
IS  - 1879-1026 (Electronic)
IS  - 0048-9697 (Linking)
VI  - 715
DP  - 2020 May 1
TI  - Enhancing nitrate and strontium concentration prediction in groundwater by using 
      new data mining algorithm.
PG  - 136836
LID - S0048-9697(20)30346-6 [pii]
LID - 10.1016/j.scitotenv.2020.136836 [doi]
AB  - Groundwater resources constitute the main source of clean fresh water for 
      domestic use and it is essential for food production in the agricultural sector. 
      Groundwater has a vital role for water supply in the Campanian Plain in Italy and 
      hence a future sustainability of the resource is essential for the region. In the 
      current paper novel data mining algorithms including Gaussian Process (GP) were 
      used in a large groundwater quality database to predict nitrate (contaminant) and 
      strontium (potential future increasing) concentrations in groundwater. The 
      results were compared with M5P, random forest (RF) and random tree (RT) 
      algorithms as a benchmark to test the robustness of the modeling process. The 
      dataset includes 246 groundwater quality samples originating from different 
      wells, municipals and agricultural. It was divided for the modeling process into 
      two subgroups by using the 10-fold cross validation technique including 173 
      samples for model building (training dataset) and 73 samples for model validation 
      (testing dataset). Different water quality variables including T, pH, EC, 
      HCO(3)(-), F(-), Cl(-), SO(4)(2)(-), Na(+), K(+), Mg(2+), and Ca(2+) have been 
      used as an input to the models. At first stage, different input combinations have 
      been constructed based on correlation coefficient and thus the optimal 
      combination was chosen for the modeling phase. Different quantitative criteria 
      alongside with visual comparison approach have been used for evaluating the 
      modeling capability. Results revealed that to obtain reliable results also 
      variables with low correlation should be considered as an input to the models 
      together with those variables showing high correlation coefficients. According to 
      the model evaluation criteria, GP algorithm outperforms all the other models in 
      predicting both nitrate and strontium concentrations followed by RF, M5P and RT, 
      respectively. Result also revealed that model's structure together with the 
      accuracy and structure of the data can have a relevant impact on the model's 
      results.
CI  - Copyright (c) 2020 Elsevier B.V. All rights reserved.
FAU - Bui, Dieu Tien
AU  - Bui DT
AD  - Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi 
      Minh City, Viet Nam; Faculty of Environment and Labour Safety, Ton Duc Thang 
      University, Ho Chi Minh City, Viet Nam. Electronic address: 
      buitiendieu@tdtu.edu.vn.
FAU - Khosravi, Khabat
AU  - Khosravi K
AD  - School of Engineering, University of Guelph, ON, Canada.
FAU - Karimi, Mahshid
AU  - Karimi M
AD  - Department of Watershed Management, Sari Agricultural Science and Natural 
      Resources University, Sari, Iran.
FAU - Busico, Gianluigi
AU  - Busico G
AD  - University of Campania "Luigi Vanvitelli", Department of Environmental, 
      Biological and Pharmaceutical Sciences and Technologies, Via Vivaldi 43, 81100, 
      Caserta, Italy.
FAU - Khozani, Zohreh Sheikh
AU  - Khozani ZS
AD  - Department of Civil Engineering, Faculty of Engineering & Built Environment, 
      Universiti Kebangsaan, Malaysia.
FAU - Nguyen, Hoang
AU  - Nguyen H
AD  - Institute of Research and Development, Duy Tan University, Da Nang 550000, Viet 
      Nam. Electronic address: nguyenhoang23@duytan.edu.vn.
FAU - Mastrocicco, Micol
AU  - Mastrocicco M
AD  - University of Campania "Luigi Vanvitelli", Department of Environmental, 
      Biological and Pharmaceutical Sciences and Technologies, Via Vivaldi 43, 81100, 
      Caserta, Italy.
FAU - Tedesco, Dario
AU  - Tedesco D
AD  - University of Campania "Luigi Vanvitelli", Department of Environmental, 
      Biological and Pharmaceutical Sciences and Technologies, Via Vivaldi 43, 81100, 
      Caserta, Italy; Istituto Nazionale di Geofisica e Vulcanologia, sezione di Napoli 
      - Osservatorio Vesuvuviano, Via Diocleziano 328 - Napoli, Italy.
FAU - Cuoco, Emilio
AU  - Cuoco E
AD  - University of Campania "Luigi Vanvitelli", Department of Environmental, 
      Biological and Pharmaceutical Sciences and Technologies, Via Vivaldi 43, 81100, 
      Caserta, Italy.
FAU - Kazakis, Nerantzis
AU  - Kazakis N
AD  - Aristotle University of Thessaloniki, Department of Geology, Lab. of Engineering 
      Geology & Hydrogeology, 54124 Thessaloniki, Greece. Electronic address: 
      kazakis@geo.auth.gr.
LA  - eng
PT  - Journal Article
DEP - 20200124
PL  - Netherlands
TA  - Sci Total Environ
JT  - The Science of the total environment
JID - 0330500
SB  - IM
EIN - Sci Total Environ. 2020 Nov 10;742:141568. PMID: 32839005
OTO - NOTNLM
OT  - Data mining
OT  - Gaussian process
OT  - Italy
OT  - Nitrate
OT  - Prediction
OT  - Strontium
COIS- Declaration of competing interest The authors declare that they have no known 
      competing financial interests or personal relationships that could have appeared 
      to influence the work reported in this paper.
EDAT- 2020/02/03 06:00
MHDA- 2020/02/03 06:01
CRDT- 2020/02/03 06:00
PHST- 2019/11/10 00:00 [received]
PHST- 2020/01/19 00:00 [revised]
PHST- 2020/01/19 00:00 [accepted]
PHST- 2020/02/03 06:00 [pubmed]
PHST- 2020/02/03 06:01 [medline]
PHST- 2020/02/03 06:00 [entrez]
AID - S0048-9697(20)30346-6 [pii]
AID - 10.1016/j.scitotenv.2020.136836 [doi]
PST - ppublish
SO  - Sci Total Environ. 2020 May 1;715:136836. doi: 10.1016/j.scitotenv.2020.136836. 
      Epub 2020 Jan 24.