1. Comparison of Levenberg Marquardt and Conjugate Gradient Descent Optimization Methods for Simulation of Streamflow Using Artificial Neural Network

   Thabo Michael Bafitlhile, Zhijia Li, Qiaoling Li

   College of Hydrology and Water Resources, Hohai University, Nanjing 210098, P. R. China.
   

   Abstract: Prediction of absolute extreme flood peak discharge is a crucial research topic for hydrologists because it is essential in developing the best management practices, addressing water-related issues like flood warning, mitigation schemes planning, management and operation of water resources development projects, etc. The primary purpose of this study was to develop Artificial Neural Network Model (ANN) that can accurately predict Changhua streamflow using hourly data for flood events that occurred between 07/04/1998 to 16/04/2010. Zhejiang province is one of the areas in eastern of China that is prone to severe weather, including heavy rain, thunderstorms, and hail. Since 2011 Zhejiang province has continuously been hit by torrential rain which has left many deaths, loss of property and direct economic loss. Therefore, since Qingshandian reservoir function as a power generator and as flood control system, prediction of the downstream flow of Changhua River is vital for improving the management of the reservoir. Rainfall data from seven stations were used as inputs to the ANN model, and streamflow data were used as the desired outputs of the ANN model. ANN is one of the artificial intelligence method attempting to copy the human brain functioning. It acquires knowledge through a learning process that involves the shifting of connection weight and changing bias parameters to determine the optimal network. Levenberg Marquardt Algorithm (LMA) and Conjugate Gradient Descent (CGD) optimization methods were used to train ANN. The performance of the two algorithms was measured using Residual Standard Error (RSE), R squared, Nash–Sutcliffe Efficiency (NSE) and Pearson’s Product Method (PPM). The overall results show that CGD method is the best method for simulation of Changhua streamflow as compared to LMA.

   Keywords: Artificial neural network, Conjugate Gradient Descent, Levenberg Marquardt, Streamflow simulation.

   Pages: 217 – 237 | Full PDF Paper

2. Primary School Curriculum Contributing to Plant Blindness: Assessment Through the Biodiversity Perspective

   Alexandros Amprazis, Penelope Papadopoulou

   Department of Early Childhood Education, University of Western Macedonia, 3rd klm Florinas – Nikis, 53100, Florina, Greece.

   Abstract: Biodiversity loss is already an important issue as planet Earth loose species at an alarming rate. This loss has a direct effect on humanity for the capacity of ecosystems to provide welfare, depends on biodiversity. So, no organism can be ignored or be underestimated, as this is happening with plants according to the “Plant Blindness” hypothesis. The limited plant references in subject matter taught in schools are listed among the causes of this phenomenon. Aim of this research is to analyze Greek primary school curriculum according to the grounded theory principles in order to clarify whether they shape attitudes and add to knowledge regarding plant life. According to the results, plant morphology and plant physiology seem to be adequately analyzed throughout the science curriculum. On the contrary, references about the importance of plants for human welfare and life’s evolution on earth are almost lacking. Plant life seems to be examined rather incompletely in the Greek primary school. These findings bring to light issues relating to curricular effectiveness and enhance the general concern about the reduced emphasis on plant life in educational systems worldwide. Hence, a need emerges for revising curriculum and textbooks in order to eliminate deficiencies in plant knowledge.

   Keywords: Plant Blindness, Primary school, Attitudes, Biodiversity, Curriculum.

   Pages: 238 – 256 | Full PDF Paper

3. Identification of Unfavorable Climate and Sanitary Periods in Oueme Department in Benin (West Africa)

   MEHINTO DOVONOU FLORE¹, BOKO Nouvêwa Patrice Maximilien², HOUSSOU CHRISTOPHE SÈGBȳ

   1. Laboratory Pierre PAGNEY: Climate, Water, Ecosystems and Development (LACEEDE), University of Abomey-Calavi (Bénin).
   2. Department of Geography and Regional planning (DGAT), University of Abomey-Calavi (Bénin), boko2za@gmail.com
   3. Institute of Geography, of Regional planning and the Environment (IGATE), University of Abomey-Calavi (Bénin).

   Abstract: Since the advent of climate change, the effect of climate on the human body is increasingly felt, this leads to the recrudescence of several pathologies. This study aims to determine the adverse climatic and health periods for people who are sensitive to malaria and acute respiratory infections in the department of Ouémé in Benin. To do this, this study was conducted using descriptive statistics methods, and calculation of bioclimatic indices (K and THI). The data used are the climatological data (rainfall, temperature, relative humidity, insolation and wind) on a monthly scale over the period 1971-2015 and available epidemiological data of all the health centres of the department. The results obtained make it possible to define the months of June, July, August, September and October as unfavourable weather-health periods for malaria and the months of January, February, March, April, May, December and November, as an unfavourable climatic-sanitary period for people sensitive to IRA. In conclusion, the health and sanitary periods unfavourable for the IRA are the harmattan months (January, February, December) and the unfavourable weather-health periods for the malaria-sensitive are the beginning months (March and September) and the end (July and September). November) of rainy seasons. It can be noted that, from the point of view of health and climate, adaptation measures are necessary for the sustainable development of the country.

   Keywords: Benin, Department of Ouémé, bioclimatic environments, climato-sanitary, malaria, acute respiratory infections (ARI).

   Pages: 257 – 274 | Full PDF Paper