Accuracy Enhancement for Forecasting Water Levels of Reservoirs and River Streams Using a Multiple-Input-Pattern Fuzzification Approach
Water level forecasting is an essential topic in water management affecting reservoir operations and decision making. Recently, modern methods utilizing artificial intelligence, fuzzy logic, and combinations of these techniques have been used in hydrological applications because of their considerabl...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/432976 |
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| _version_ | 1832567139516547072 |
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| author | Nariman Valizadeh Ahmed El-Shafie Majid Mirzaei Hadi Galavi Muhammad Mukhlisin Othman Jaafar |
| author_facet | Nariman Valizadeh Ahmed El-Shafie Majid Mirzaei Hadi Galavi Muhammad Mukhlisin Othman Jaafar |
| author_sort | Nariman Valizadeh |
| collection | DOAJ |
| description | Water level forecasting is an essential topic in water management affecting reservoir operations and decision making. Recently, modern methods utilizing artificial intelligence, fuzzy logic, and combinations of these techniques have been used in hydrological applications because of their considerable ability to map an input-output pattern without requiring prior knowledge of the criteria influencing the forecasting procedure. The artificial neurofuzzy interface system (ANFIS) is one of the most accurate models used in water resource management. Because the membership functions (MFs) possess the characteristics of smoothness and mathematical components, each set of input data is able to yield the best result using a certain type of MF in the ANFIS models. The objective of this study is to define the different ANFIS model by applying different types of MFs for each type of input to forecast the water level in two case studies, the Klang Gates Dam and Rantau Panjang station on the Johor river in Malaysia, to compare the traditional ANFIS model with the new introduced one in two different situations, reservoir and stream, showing the new approach outweigh rather than the traditional one in both case studies. This objective is accomplished by evaluating the model fitness and performance in daily forecasting. |
| format | Article |
| id | doaj-art-eefde64033604edebb12ce10f23ad818 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-eefde64033604edebb12ce10f23ad8182025-02-03T01:02:25ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/432976432976Accuracy Enhancement for Forecasting Water Levels of Reservoirs and River Streams Using a Multiple-Input-Pattern Fuzzification ApproachNariman Valizadeh0Ahmed El-Shafie1Majid Mirzaei2Hadi Galavi3Muhammad Mukhlisin4Othman Jaafar5Department of Civil and Structural Engineering, Universiti Kebangsaan Malaysia (UKM), 43000 Bangi, Selangor, MalaysiaDepartment of Civil and Structural Engineering, Universiti Kebangsaan Malaysia (UKM), 43000 Bangi, Selangor, MalaysiaDepartment of Civil and Structural Engineering, Universiti Kebangsaan Malaysia (UKM), 43000 Bangi, Selangor, MalaysiaCivil Engineering Department, Faculty of Engineering, Universiti Putra Malaysia (UPM), 43300 Serdang, Selangor, MalaysiaDepartment of Civil and Structural Engineering, Universiti Kebangsaan Malaysia (UKM), 43000 Bangi, Selangor, MalaysiaDepartment of Civil and Structural Engineering, Universiti Kebangsaan Malaysia (UKM), 43000 Bangi, Selangor, MalaysiaWater level forecasting is an essential topic in water management affecting reservoir operations and decision making. Recently, modern methods utilizing artificial intelligence, fuzzy logic, and combinations of these techniques have been used in hydrological applications because of their considerable ability to map an input-output pattern without requiring prior knowledge of the criteria influencing the forecasting procedure. The artificial neurofuzzy interface system (ANFIS) is one of the most accurate models used in water resource management. Because the membership functions (MFs) possess the characteristics of smoothness and mathematical components, each set of input data is able to yield the best result using a certain type of MF in the ANFIS models. The objective of this study is to define the different ANFIS model by applying different types of MFs for each type of input to forecast the water level in two case studies, the Klang Gates Dam and Rantau Panjang station on the Johor river in Malaysia, to compare the traditional ANFIS model with the new introduced one in two different situations, reservoir and stream, showing the new approach outweigh rather than the traditional one in both case studies. This objective is accomplished by evaluating the model fitness and performance in daily forecasting.http://dx.doi.org/10.1155/2014/432976 |
| spellingShingle | Nariman Valizadeh Ahmed El-Shafie Majid Mirzaei Hadi Galavi Muhammad Mukhlisin Othman Jaafar Accuracy Enhancement for Forecasting Water Levels of Reservoirs and River Streams Using a Multiple-Input-Pattern Fuzzification Approach The Scientific World Journal |
| title | Accuracy Enhancement for Forecasting Water Levels of Reservoirs and River Streams Using a Multiple-Input-Pattern Fuzzification Approach |
| title_full | Accuracy Enhancement for Forecasting Water Levels of Reservoirs and River Streams Using a Multiple-Input-Pattern Fuzzification Approach |
| title_fullStr | Accuracy Enhancement for Forecasting Water Levels of Reservoirs and River Streams Using a Multiple-Input-Pattern Fuzzification Approach |
| title_full_unstemmed | Accuracy Enhancement for Forecasting Water Levels of Reservoirs and River Streams Using a Multiple-Input-Pattern Fuzzification Approach |
| title_short | Accuracy Enhancement for Forecasting Water Levels of Reservoirs and River Streams Using a Multiple-Input-Pattern Fuzzification Approach |
| title_sort | accuracy enhancement for forecasting water levels of reservoirs and river streams using a multiple input pattern fuzzification approach |
| url | http://dx.doi.org/10.1155/2014/432976 |
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