REGIONAL IMPACT OF WATER RESOURCE INVESTMENTS IN A DEVELOPING AREA1

ABSTRACT: Regional development and industrialization patterns are investigated and related via regression analysis to water resource investments for the island of Puerto Rico. Although results of this study indicate such investments have little immediate or short-term impact, significant relationships and variations in regional responses appear over longer time periods. This is shown by applying a variation of Zellner's method of performing seemingly unrelated regressions jointly. By this method, subsets of parameter coefficients of specific economic variables were restricted across regional equations while unrestricted coefficients were interpreted as explaining systematic regional variations in response to public investment. Regional differences, obtained by using this method, are frequently neglected when simply examining the overall development process. Among the more interesting results in terms of policy implications is the apparent significant relationship, over the period considered, between changes in the distribution of income and the pattern of water resource development.     

Extreme learning machines: a new approach for modeling dissolved oxygen (DO) concentration with and without water quality variables as predictors

In this paper, several extreme learning machine (ELM) models, including standard extreme learning machine with sigmoid activation function (S-ELM), extreme learning machine with radial basis activation function (R-ELM), online sequential extreme learning machine (OS-ELM), and optimally pruned extreme learning machine (OP-ELM), are newly applied for predicting dissolved oxygen concentration with and without water quality variables as predictors. Firstly, using data from eight United States Geological Survey (USGS) stations located in different rivers basins, USA, the S-ELM, R-ELM, OS-ELM, and OP-ELM were compared against the measured dissolved oxygen (DO) using four water quality variables, water temperature, specific conductance, turbidity, and pH, as predictors. For each station, we used data measured at an hourly time step for a period of 4 years. The dataset was divided into a training set (70%) and a validation set (30%). We selected several combinations of the water quality variables as inputs for each ELM model and six different scenarios were compared. Secondly, an attempt was made to predict DO concentration without water quality variables. To achieve this goal, we used the year numbers, 2008, 2009, etc., month numbers from (1) to (12), day numbers from (1) to (31) and hour numbers from (00:00) to (24:00) as predictors. Thirdly, the best ELM models were trained using validation dataset and tested with the training dataset. The performances of the four ELM models were evaluated using four statistical indices: the coefficient of correlation (R), the Nash-Sutcliffe efficiency (NSE), the root mean squared error (RMSE), and the mean absolute error (MAE). Results obtained from the eight stations indicated that: (i) the best results were obtained by the S-ELM, R-ELM, OS-ELM, and OP-ELM models having four water quality variables as predictors; (ii) out of eight stations, the OP-ELM performed better than the other three ELM models at seven stations while the R-ELM performed the best at one station. The OS-ELM models performed the worst and provided the lowest accuracy; (iii) for predicting DO without water quality variables, the R-ELM performed the best at seven stations followed by the S-ELM in the second place and the OP-ELM performed the worst with low accuracy; (iv) for the final application where training ELM models with validation dataset and testing with training dataset, the OP-ELM provided the best accuracy using water quality variables and the R-ELM performed the best at all eight stations without water quality variables. Fourthly, and finally, we compared the results obtained from different ELM models with those obtained using multiple linear regression (MLR) and multilayer perceptron neural network (MLPNN). Results obtained using MLPNN and MLR models reveal that: (i) using water quality variables as predictors, the MLR performed the worst and provided the lowest accuracy in all stations; (ii) MLPNN was ranked in the second place at two stations, in the third place at four stations, and finally, in the fourth place at two stations, (iii) for predicting DO without water quality variables, MLPNN is ranked in the second place at five stations, and ranked in the third, fourth, and fifth places in the remaining three stations, while MLR was ranked in the last place with very low accuracy at all stations. Overall, the results suggest that the ELM is more effective than the MLPNN and MLR for modelling DO concentration in river ecosystems.     

An Examination of Governance Arrangements at Kisakasaka Mangrove Reserve in Zanzibar

This study employs insights largely derived from critical reflections on the common pool resources (CPR) theory to examine the current governance arrangements in place to manage the mangrove forest at Kisakasaka, in Zanzibar, Tanzania. Kisakasaka was used as a site for a community-based management pilot project of forest resources in Zanzibar. After some initial success in setting up a local management structure and regulating access to the mangrove for mainly charcoal production, there are now clear indications that forest conditions have deteriorated dramatically with concomitant ongoing resource use problems for local villagers who have relied heavily on forest resources as a source of cash income. Extra-local factors, such as urban population increases and associated market pressures for charcoal, are also conjectured to overlay and interact with the institutional problems at Kisakasaka. As a result, over concern about the deterioration in the condition of the mangrove forest, the responsible government authority decided not to renew the community-based governance arrangements after an initial five-year pilot period. While revealing the inadequacies of existing governance arrangements and of its relationship to deteriorating forest conditions at Kisakasaka, this study concludes by suggesting an approach to more fully understand forces driving local resource management and use.     

Developing a predictive understanding of landscape importance to the Punan-Pelancau of East Kalimantan, Borneo

In order for local community views to be incorporated into new development initiatives, their perceptions need to be clearly understood and documented in a format that is readily accessible to planners and developers. The current study sought to develop a predictive understanding of how the Punan Pelancau community, living in a forested landscape in East Kalimantan, assigns importance to its surrounding landscapes and to present these perceptions in the form of maps. The approach entailed the iterative use of a combination of participatory community evaluation methods and more formal modeling and geographic information system techniques. Results suggest that landscape importance is largely dictated by potential benefits, such as inputs to production, health, and houses. Neither land types nor distance were good predictors of landscape importance. The grid-cell method, developed as part of the study, appears to offer a simple technique to capture and present the knowledge of local communities, even where their relationship to the land is highly complex, as was the case for this particular community.     

The East African decapod crab Neosarmatium meinerti (de Man) sweeps mangrove floors clean of leaf litter

Leaf litter removal by the abundant mangrove decapod crab Neosarmatium meinerti was studied in series of field and laboratory experiments in East Africa. In the high intertidal Avicennia marina zone crabs buried all leaves placed on the forest floor and consumed on average 67% of them within 2 hrs. High shore crabs in Kenya buried 4 g m(-2) leaf-litter in 1 hr, i.e. approx. twice the daily litter fall. In contrast, in the low shore Sonneratia alba zone, where typical leaf-eating crabs were absent, none of the offered leaves showed signs of herbivory. Leaf choice experiments in the laboratory showed that N. meinerti preferred some species to others. Leaf consumption per gram crab was higher in females than males. The laboratory studies also indicated that crabs could consume substantially more than the average daily litter fall. Video recordings documented frequent fights to gain or retain fallen leaves, suggesting strong competition for leaf litter. Earlier studies indicating that N. meinerti may sweep mangrove forest floors clean of leaf litter are confirmed. In high shore mangroves of East and South Africa where N. meinerti is common, energy flow appears unique: virtually all litter production is retained.     

Generalized regression neural network (GRNN)-based approach for colored dissolved organic matter (CDOM) retrieval: case study of Connecticut River at Middle Haddam Station,USA

The prediction of colored dissolved organic matter (CDOM) using artificial neural network approaches has received little attention in the past few decades. In this study, colored dissolved organic matter (CDOM) was modeled using generalized regression neural network (GRNN) and multiple linear regression (MLR) models as a function of Water temperature (TE), pH, specific conductance (SC), and turbidity (TU). Evaluation of the prediction accuracy of the models is based on the root mean square error (RMSE), mean absolute error (MAE), coefficient of correlation (CC), and Willmott’s index of agreement (d). The results indicated that GRNN can be applied successfully for prediction of colored dissolved organic matter (CDOM).